research on human behavior

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NCBI Bookshelf. A service of the National Library of Medicine, National Institutes of Health.

National Research Council; Division of Behavioral and Social Sciences and Education; Commission on Behavioral and Social Sciences and Education; Committee on Basic Research in the Behavioral and Social Sciences; Gerstein DR, Luce RD, Smelser NJ, et al., editors. The Behavioral and Social Sciences: Achievements and Opportunities. Washington (DC): National Academies Press (US); 1988.

The Behavioral and Social Sciences: Achievements and Opportunities.

• Hardcopy Version at National Academies Press

1 Behavior, Mind, and Brain

From the beginnings of scientific inquiry, researchers have tried to understand the workings of the mind and its relationship to behavior. In modern terms, scientists seek to answer such questions as:

How does an individual manage to see coherent objects in changing patches of multicolored light or to hear speech and music in bursts of sound varying in loudness and pitch?

How do people remember, even imperfectly, the vast storehouse of factual and functional information that each of us carries about?

How do infants—and other nonverbal creatures—think, and what are their thoughts like?

How does an individual learn new ideas, create concepts, organize his or her knowledge, and act upon what he or she knows?

Until about 100 years ago, these and similar questions led mainly to speculation, to sophisticated but untestable guesses about the nature of the mind and its function in behavior. Many scientists thought that this confinement to speculation would always be the case, on the presumption that mental life, unlike the world of objects, is not directly observable and so is not amenable to systematic, scientific study. But during the second half of the nineteenth century, experimental and close observational approaches to such questions began to appear. Some of the first work involved the study of sensory processes that underlie organic sensitivity to different physical stimuli, rigorous observations about how behavior is shaped by experience, and the relation of observable behaviors to certain brain regions and neural events.

Both theory and method have since advanced at an accelerating pace, with extensive revisions of earlier questions to make them more tractable and to take advantage of the growing linkages between knowledge about behavior, the mind, and the brain. Instead of asking only how best to measure the intelligence of people, researchers now ask about the nature of intelligent action, whether exhibited by humans, animals, computers, or robots. In addition to seeking laws that directly relate external stimuli to behavioral responses, researchers now attempt to unravel the basic processing that the brain must carry out in order to generate behavior and to simulate that processing on computers to formalize and test theories. These are the new questions—with their extensions into the details of visual and auditory processing, memory formation and retrieval, language, cognition, and action—that drive the research investigations highlighted in this chapter.

• Seeing and Hearing

As you look around, you see a variety of objects at various distances, some still and others moving, some transparent and some densely colored, some partially obscured and some not. All is perfectly ordinary and seen without effort if the light is adequate. Your cat or dog sees these things, too, although somewhat differently from the way you do. And so does the fly that evades your swat. And when you listen to a musical recording, you have little trouble hearing the separate instruments. If someone speaks while you are listening to the music, you have no difficulty in understanding the words, unless the music is very loud or you suffer from a certain form of hearing loss that is common in older males.

It is all so commonplace—but no one yet knows exactly how the brain does any of these things. No one yet knows enough to program a computer to pick out a wide range of objects from a scene, to isolate a violin in an orchestral passage, or to separate speech from noise and to partition it into words. Enabling machines to do these things would surely affect the way people live as much as the telephone, the thermostat, or the radar have done. And it is clear these tasks can be done because the human brain does them, continuously and apparently effortlessly. One approach to studying how these tasks are carried out is to work with computers and sensing devices, without much regard for how the tasks are done in the brain. Another approach is to focus directly on unraveling nature’s way of doing them.

These tasks may be very complex. Or they may be simple but involve principles that scientists do not yet understand. For example, between one-quarter and two-fifths of the total cortex of the human brain is devoted to vision, suggesting that it is one of the most complex brain functions. However, abilities somewhat similar to human vision are exhibited by animal brains far more modest. For example, pigeons trained to identify the occurrence of trees (in contrast to bushes or other plants) in color slides—whether alone or in a forest, in whole or in part, leafy or bare—are then able to identify trees as accurately as people can, in slides they have not previously seen. Pigeons can also be trained to recognize a particular person in slides of individuals or crowds. Yet, with similar training, pigeons cannot identify simple line drawings, such as cartoon characters. The difficult is easy and the easy difficult for that tiny brain, or so it seems.

Hearing similarly involves complexity and simplicity. The only physical event on which hearing is based is the variation of air pressure at the ears. A plot of the sound pressure generated by a person saying “science” in a quiet room is completely different from the plot of that same word said by the same person against a background of white noise, such as cocktail party chatter. A person has no difficulty hearing the message in the noise, although the physical sound patterns are quite different. The ability of the human ear and brain to recognize the word in the noise is vastly superior to that of any machine.

Understanding such abilities is the current focus of research on perception. This research includes innovative theoretical work and sophisticated experiments with animals, and it is increasingly able to use computers to develop theoretical models and simulate experimental tests. The research involves psychologists, biologists, physiologists, physicians, graphic artists, physicists, engineers, and computer scientists. The work is highly interactive: for example, behavioral analyses of sensory and perceptual processes provide evidence for functional distinctions that can then be sought in the workings of the brain. Another example comes from theoretical advances in the development of recursive computer models that capture the simultaneous use of multiple sources of information. These are often called activation models because each piece of information that plays a part in a particular mental process is viewed as an active influence on the direction of the process. These activation models are being applied to previously intractable problems in research on human and animal vision.

HEARING How do the senses, such as hearing, work? How does a person’s auditory system identify a single voice in a buzz of conversation and pick words out of a vocal stream?

The figures on the facing page show the sound pressure generated by a human male voice saying the word “science.” They are digitized plots of the amplitude of a sound wave over time; the total time is 0.85 seconds; the amplitude is sampled at a rate of 10,000 observations per second.

In the top figure, the person spoke in the stillness of a soundproof chamber. In the bottom figure, he spoke while his voice was masked by noise that simulated a roomful of people talking, such as would occur at a cocktail party.

Neither the human eye nor any known form of sound filter or computer analysis can detect the wave form of the top figure in the bottom figure. That is, they cannot extract the vocal wave form from the noise. But a normal listener has no trouble hearing and understanding the word through the noise. How the human ear and brain perform the analysis needed to recognize the word in the noise is a fascinating and complicated scientific problem.

Visual Analyzers

The retina of the eye—really a bit of exposed brain—is a regular mosaic of five different layers of nerve cells whose spatial arrangement affects the nature of vision. Aided by recent technical advances in electronics, researchers have shown that in one early stage of visual processing, millions of small groups of neurons in the eye and the rest of the brain work as parallel microprocessors or analyzers. Each analyzer responds preferentially to stimulus components within a narrow range of size and orientation; they decompose the scene into a vast number of overlapping components. Precise quantitative models based on such analyzers can be explored by complex computer simulations and can now explain how the human eye and brain detect and identify low-contrast visual patterns, such as nighttime shadows. Some current work suggests that the brain may be able to rapidly select and regroup the analyzers, depending on what is being perceived.

One application of such models is to evaluate, at the design stage, the relative clarity and efficiency of a variety of visual displays: flight simulators, video terminals, warning lights, and information signs. Another application is image augmentation. The inherent inadequacies of electrical and optical imaging systems—such as blurring due to the atmosphere—can be compensated for by computers. Information that is particularly useful to human perception can be improved at the expense of less useful information. These applications are aimed at speeding up such tasks as reading x-ray plates and satellite reconnaissance displays, tracing circuit diagrams, and following blueprints and maps, which are done slowly and are error-prone without augmentation. Another application in the future will be the possibility of more efficient transmission of visual images over telephone lines.

Researchers have discovered that some visual tasks are so complex that humans, some primates, and possibly many other animal species use special-purpose brain areas to solve them efficiently. For example, the human ability to recognize and discriminate readily among a seemingly endless variety of faces is now known to make use of certain neural circuits in the right-posterior cerebral hemisphere. Facial recognition may be different from the recognition of most other kinds of objects in the world, but it may be that other forms of expertise in pattern recognition hinge at least in part on engaging those neural circuits. Researchers are beginning to explore in detail the extent and nature of such special-purpose systems in the human and animal brain and their relation to perception.

Color Constancy

An outstanding example of a very important special feature of visual perception is color constancy: color relations between objects in a scene seem relatively invariant to the eye, independent of the light playing on the scene. A blue shirt looks blue whether seen in sunlight or in a dimly lit restaurant. Yet this invariance cannot be replicated by color photographic media.

Recently, some investigators have proposed a theory about how the eye and brain may separate the inherent color relations among objects from the light impinging on them. Certain newly defined mathematical algorithms, if carried out simultaneously over the entire visual field, presumably by millions of visual analyzers, have been shown in theory to be able to separate the inherent colors of objects from incident lighting. This theoretical model assumes certain mathematical conditions that correspond to qualities of colors, of light, and of the high-level “programming” of the brain. Large-scale computations are now being carried out to test and refine the model and to find ways to simulate it in real time with new computer architectures.

If, as the theory suggests, this decomposition works efficiently and if, as is anticipated, the electro-optical technology for recording a visual scene digitally can be made sufficiently compact, a new form of photography could result. Instead of the various “speeds” of film suited to different lighting conditions, one would need only one type of photosensitive medium that could be used under any circumstances. The “developing” process would then decompose what is inherent in the scene from the lighting that happened to exist at the time of the photograph. With such a process, one could display or print the picture with whatever lighting conditions one wanted. The developing and printing processes, carried out on a computer, would be analogous to what the brain does all the time. The ability to adjust lighting as if from the mind’s eye would mean unprecedented expansion of the capabilities for the scientific study of vision and for applications in graphic arts, industrial design, and computer vision.

Depth and Motion

That the world is spatially three-dimensional while the retina is spatially two-dimensional creates inherent visual ambiguities. The eye cannot directly capture motion perpendicular to it; the eye and the rest of the brain must infer depth and motion from ambiguous clues. Resolving these ambiguities, which is usually essential for accurate vision, is possible because of certain constraints that occur in natural scenes concerning shading, motion, texture, and so on. Recent results of behavioral experiments with humans and animals, which have inspired corroborating neurophysiological experiments, reveal that higher-level neural analyzers of motion in visual images are sensitive to the global direction of a pattern’s motion, even when parts of the pattern are moving in other directions. These analyzers give rise to moving optical illusions, such as rigid objects that are perceived as flexible and vice versa. Another example is the illusion that very large objects are moving more slowly than they actually are. Landing or taking off, a jumbo jetliner appears to be flying more slowly, by a sizable factor, than a small, executive jet, when in fact their speeds are very similar. This kind of size-speed illusion is believed to be the underlying cause of many railroad-crossing accidents when motorists drastically underestimate the speed of an approaching train. These illusions, which are difficult and in some cases virtually impossible to study experimentally except with modern computer-graphic technology, imply that immense distortion in visual displays may be tolerated or even go unnoticed by both humans and animals.

Perceiving Objects

The process by which visual analysis fills in a big picture from a collection of details means that mere fragments of objects suffice in normally cluttered scenes to permit observers to produce coherent, conceptually appropriate perceptions. Adults make use of several experimentally confirmed theoretical principles to do this, such as assuming that objects in a scene do not share boundaries. People also tend to see correlated movement of disconnected parts in natural scenes as a single moving object partially masked by another object. This latter principle is apparently a deep-seated tendency and has even been demonstrated in very young infants. Increasing knowledge about visual perception can be expected to improve the design of factory robots that must make complex identifications of objects in order to carry out their function without selecting the wrong object or inadvertently hurting someone.

Temporal Auditory Patterns

Hearing problems having to do with space (for example, distance and localization) are very important in the design of sound equipment and auditoriums and have long been studied. Much of the current focus of research on auditory perception, however, concerns complex patterns of sound stimuli varying in time, such as speech and music. Auditory signals can now be designed and stimulated quite precisely by a computer driving a digital-to-analog signal coverter, and these artificial sound patterns are used to study specific aspects of the hearing process.

One type of study measures a listener’s ability to discriminate changes in the intensity of one of several tones that are played simultaneously. Initially, the ability to sense a change in intensity within a complex of other tones is far less than when a single tone is played by itself. But with practice in listening to complex tones repeated with little variation, the ability to recognize the variations when they do occur becomes very good. Studies are now under way to see whether practice in listening to complex sound patterns is characteristic of how an infant learns to identify the particular phonemic differences that characterize its prospective language.

An important area of application of research on the perception of auditory patterns (including speech recognition, described more fully below) is in the design of hearing aids. They have been greatly improved in the past decade by coupling what was known about the nature of hearing loss with modern electronics. Whenever the auditory deterioration is peripheral rather than central, certain aspects of the loss, such as reduced loudness at certain frequencies, can be mitigated by appropriately altering the sound waves at the eardrum. Whether it will also prove possible to compensate for the inability to separate speech signals from background noise may depend similarly on the nature of the loss, which further research is likely to reveal.

Music is the focus of much scientific research. Musical keys and scales are highly structured, and musicians, mathematicians, and philosophers have long speculated about the underlying nature of that structure. More recently, the rules of counterpoint and orchestration are being explained, not as arbitrary requirements of particular musical styles, but as selective adaptations to basic tendencies of the human auditory system to group sounds in certain ways as a step in auditory pattern recognition. Researchers some time ago postulated that a set of ratios corresponding to a helical geometric structure underlies tonal perception. Recently, far more systematic studies, using trained musicians as respondents and multidimensional scaling methods (discussed in Chapter 5 ) to analyze the results, have led to a modified understanding of the geometric relations implicit in tonal perception; they appear to be conical rather than helical configurations. Stimulated by the success of linguists in analyzing language as a rule-governed system, behavioral researchers working with musicians have also successfully begun to define the deeply embedded rules that become internalized in the course of growing up in a musical culture, and they have demonstrated how such deep rule structures strongly affect listeners’ perception and memory of what is heard.

Most people can readily find their way around a once frequented neighborhood, improve a sporting skill with practice, or recall an old story. These deeds are possible because the capacity for memory storage is vast; indeed, virtually everything discussed in this chapter depends critically on humans having very large, readily accessible banks of organized information in the brain. Not only is the capacity large, but also much of its use is automatic: learning and memory often occur incidentally, with little special effort. Yet for some people memory failure can become an acute problem. In certain diseases, such as Alzheimer’s, the ability to learn and remember is drastically impaired, and life becomes a series of unconnected moments.

Some questions about memory are of long standing: How are memories organized? How does the brain code, store, and retrieve them? What are the computations and processes involved? What happens when the capacity for memory becomes impaired? How can memories be improved? For both these questions and newer ones, a whole new approach to the study of memory derives from the development of computers. However, although much has been learned from the way information is stored in computational systems, the analogy between computer memory and human memory is imperfect in many significant ways.

In the past 20 years, an interdisciplinary revolution has occurred in understanding the organization of learning and memory and their biological foundations. Behavioral studies in animals and humans are characterizing the categories and properties of learning and memory; research on human memory and the brain is identifying the neuronal systems that serve different categories of memory; memory trace circuits in the mammalian brain are being defined and localized in animal models;, researchers are beginning to understand the neuronal, neurochemical, molecular, and biophysical substrates of memory in both invertebrates and vertebrates; and theoretical mathematical analysis of basic associative learning and of neuronal networks is proceeding rapidly. Better mathematical and computational modeling of elaborate memory processes, carried out to simulate aspects of specific brain architecture, has become feasible because of the “computer revolution.” The newest phase of that revolution—the availability of massive, parallel computer architectures, in which many specialized unit devices process pieces of information simultaneously, seemingly analogous to the way in which neural memories operate—will almost certainly facilitate future work on memory.

Types of Memory

Of the many questions about memory, one of the most fundamental concerns the types or varieties of memory. In recent years, both psychological and neurobiological work have suggested that memory is dissociated into processes or systems that are fundamentally different. For example, amnesic patients with brain injury or disease exhibit severe inabilities to recall and recognize recent events and have difficulty learning new facts or other kinds of information. But, these patients possess some relatively intact learning and memory abilities: for example, on tasks such as manual-dexterity learning trials, they perform as well as healthy and uninjured people, even though they may have no conscious memory of having performed the task before. This evidence—that some kinds of learning can proceed normally even when the brain structures that mediate conscious remembering are damaged—supports the general proposition that there are distinct, dissociated types of memory.

Analysis of recall performance shows that memory is an active process of seeking and reconstructing information, not a passive recording and reproducing of events. Thus, expectations of what things should look like or the way events should happen influence what people notice and remember. For example, after listening to a story presented in jumbled order, people still tend to remember it as being told in proper sequence, following certain widely accepted schemas for what constitutes a story. People also tend to pay little attention to the details of routine situations; consequently, people often remember that the most probable things happened even when they did not. This phenomenon has been demonstrated clearly in the context of eyewitness court testimony. And because people tend to remember events and places in terms of expectations and general knowledge of the world, experiments have shown that memory of an event can be modified or distorted by the manner in which questions about the event are posed. Such experimental findings have important theoretical implications for understanding the formal structure of memory, and they have practical implications for legal proceedings (see “Small Groups and Behavior” in Chapter 2 ).

Behavioral studies in animals have also revealed certain persistent constraints on learning and memory. Many species, ranging from invertebrates to primates, can learn very quickly to associate or connect distinctive tastes with subsequent nausea, but they are much slower to associate those tastes with prompt threats of pain. In contrast, certain visual and auditory signals are readily associated with pain episodes, but are not so quickly associated with subsequent nausea. These findings also support the general proposition of different types of memory.

Animal studies have illuminated other important aspects of natural learning. In foraging, for example, animals search efficiently among many possible food sources, using past experience to reckon the best trade-offs between relative distances (and associated caloric costs), probability of finding food, and relative nutritional quality (especially caloric content) available at alternative sites (see “Patterns of Food Consumption” in Chapter 2 ).

Imprinting provides an example of biological adaptive mechanisms for natural learning. Song learning in birds that have a species-typical song is perhaps the most striking example of biological preparedness to learn. Young birds apparently have a “song template” encoded in the brain so that they best learn the species-typical song during a critical period early in life. Some birds, like the canary, learn a new song each year. This capacity for annual song learning has recently been found to depend on an extraordinary biological mechanism. Neurons in the “song circuits” of the brain grow during a period of song learning, extending their connections to other cells and increasing the opportunity for interaction among neurons, and then shrink at the end of the period. These findings may be relevant to examples of human learning that appear to operate under strong biological constraints and occur best during early years—like language learning.

Brain Structure and Neurotransmitters

Memory is under intense investigation at the neurobiological level as well as at the behavioral level. Recently developed techniques for inducing amnesia in nonhuman primates offer great promise for understanding the neural circuits underlying particular aspects of memory. Surgical brain lesions in monkeys, involving the hippocampus and related structures, produce the same selective impairment in memory as that which occurs in human patients who have experienced comparable cerebral damage due to head injury or stroke. Performance is poor in memory tests that require stimuli to be recognized as familiar or after a long delay, but skill learning is intact. This research will lead to identification of the precise set of brain structures and connections that, when damaged, causes human amnesia. At the same time, parallel work on other mammals, especially rats, will be useful in identifying the neural systems involved in acquiring and storing different kinds of memory. This work should make it possible to specify clearly what these systems’ functions are and to investigate how these systems work at the cellular/neurophysiological level.

Neural accounts of learning depend heavily on and benefit importantly from the highly developed study of learning at a behavioral level. Once memory circuits are defined in invertebrate and vertebrate nervous systems, their performance capabilities have to be measured. How neural circuits are organized is a mathematical problem of enormous complexity that can only be solved by mathematical and computational modeling. One such complex network model, an approximation to visual cortex, was shown a few years ago to be computationally in agreement with a large number of experimental results.

Recent, unexpectedly rapid progress has been made in identifying essential memory trace circuits that code, store, and retrieve associative memory in the brains of birds and mammals. The kinds of memory under study include the learning of discrete, adaptive behavioral responses and the conditioning of involuntary responses. For one well-studied type of associative learning, classical conditioning of the eye-blink response, the essential neural circuitry required for the reflex has been partly identified. Moreover, locations have been found within that circuitry where memory traces are likely to be stored when conditioning of the reflex occurs. In this case, there is growing evidence that at least one site where memory traces are stored is the cerebellum, and neurophysiological research is being carried out on the interpositus nucleus where changes related to learning can be studied within a volume of one cubic millimeter. Most of this work involves pigeons, rabbits, and baboons.

Other active research is clarifying the role that the brain’s neurotransmitters and neuropeptides play in modulating memory. Studies with drugs and experimental animals show that memory can be amplified or diminished by specific pharmacological treatments. Full identification of the core-memory circuits for basic associative learning in mammals is very likely in the next few years. As these circuits are identified and the storage sites located, scientists can make substantial progress in analyzing the detailed storage mechanisms, particularly those underlying long-term and permanent memory. These basic cellular and molecular mechanisms are now the focus of great interest and excitement. For example, in a well-studied invertebrate, the sea hare, simple, biologically universal forms of learning like habituation and sensitization are being related to changes in the readiness of particular neurons to release a transmitter. From work in both invertebrate and mammalian preparations, the general picture emerging is that learning involves the reduction of one or more kinds of potassium conductance through the membranes of nerve cells. Such a reduction in ion conductance may constitute an essential step in how nerve cells change in response to input, thereby profoundly altering the way those cells receive and transmit information. This process is thought to be brought about by phosphorylation of specific substrate proteins, mediated by “second messengers,” which can give rise to long-lasting neuronal changes. Second messengers are attractive molecules because they themselves can serve as the agent for short-term memory, while their intracellular effects can include the genomic regulation involved in the cellular changes that occur in the foundation of long-term memory.

The cellular and molecular mechanisms emerging as important in animal-model systems appear to be recurrent in the evolution of species, offering the promise that these models may be of general significance to the question of how human memory is formed and retrieved, contributing ultimately to greater understanding about the functioning of the human mind and brain. Work is proceeding at all levels from abstract mathematics to exploration of molecular and biophysical substrates. Continuing advances in such work may lead to significant applications in the treatment of memory disorders, such as those associated with amnesia, seizures, stroke, and Alzheimer’s disease.

• Cognition and Action

New questions about the mind have emerged in this century: How do people acquire knowledge, use it in reasoning, and turn it into practical action? How do human beings and other animals construct mental categories in a world of particular objects and episodes? How do people manipulate images in their minds, train their fingers to perform delicate handiwork, or judge probabilities and decide which risks to take?

Recent discoveries regarding these mysteries have resulted from new scientific methods for observing, describing, replicating, and analyzing cognitive structures and processes. To study human infant abilities, for example, researchers exploit the finding that infants look preferentially at novel objects and events, work at sucking or turn their heads to hear a sound or bring a picture into focus, and give characteristic responses to changes in the environment. For studies of adult attention, researchers take advantage of small but consistent differences in the time-course of mental events. To map the rules that guide behavior, researchers rely on the human ability to detect errors in rule application.

Neuroscience data suggest that complex behavior and mental activity emerge from simultaneous and parallel contributions of many specialized component parts. One possibility is that the basic processing units are neurological “columns” or “modules,” each containing 1,000 or fewer nerve cells; if this is true, then there are more than 10 million such functional units in the human brain. A major challenge for future study is to identify more fully the functional units of organization in the cortex, the most highly evolved and complex part of the primate brain. This effort, which is already under way, involves neuroanatomy and physiology together with the analysis of cognition and perception.

Studies of complex intellectual task management take advantage of a wide variety of methods, such as computer simulations. With the increasing availability of powerful computers oriented toward symbolic processing, theories can be tested in the form of programs that simulate the hypothetical mental activities involved in understanding and reasoning tasks. Similarly, mathematical, logical, and other formal models serve to make hypotheses about mental structures explicit and precise. Empirical methods for testing such hypotheses use detailed observations, including protocols that have individuals think aloud while they work, complex tracking systems that record and analyze eye fixations while a person is examining visual information, and repeated runs of computer models to see how well they perform in processing a variety of materials, such as texts to be analyzed or symptoms to be diagnosed.

Early Cognitive Development and Learning

Studies of early human development give some of the strongest examples of how new observational methods have led to new knowledge. The once-common belief that the experiential world of an infant is mostly sensory chaos has been displaced as evidence has accumulated showing that infants are especially sensitive to those subtle distinctions among oral sounds that are important in learning to speak, to cues of visual depth and distance, and to other precursors of complex perception.

The techniques used to show these facts make use of an infant’s innate curiosity and tendency to prefer novel sights and sounds over very familiar ones. Experiments built around these techniques are now able to investigate infants’ ability to form abstract concepts. For example, infants as young as 6 months are able to match the number of items they see in a display with the number of drumbeats they hear. In a series of studies, infants were sat either on their mother’s lap or in an infant seat and shown pairs of slides, one displaying three objects, the other two objects. The pictures of familiar objects used included a comb, apple, scissors, crayon, and book. The objects in the slides varied from trial to trial, and on each trial the infants heard either two or three drumbeats emanating from a hidden, centrally placed speaker. In one set of experiments, infants consistently tended to look at the visual display in which the number of items matched the number of drumbeats played. In experiments similar in design but with significant changes in conditions such as exposure time, infants consistently preferred to look at the display that differed in number from the number of sounds they heard on a trial. Thus, infants can respond to numerical information, in terms of whether pairs of stimuli are equal or different in number. How they do this and whether they use adult like numerical processes is the subject of much current research.

This line of study at first evolved an elegant theory that young children progress through relatively well-defined stages of understanding in which fundamental structures not present at one stage come into being virtually discontinuously at a later age. Over time, the theory has changed as new evidence has led to new hypotheses and more sensitive methods to testing them have been developed. Evidence now exists that even 3-year-old children can use general principles to understand the structure of stories, organize learning about cause and effect, represent numbers and space, analyze and reason about event sequences, and rapidly acquire the meanings of words. These findings have provided a basis for fundamental reconceptualizations of early childhood learning, which now appears to be enabled and constrained by available mental structures and fostered by a pronounced tendency of the young to seek out particular environments that challenge and nourish cognitive development. Instead of leaps across cognitive gaps, development involves the gradual accumulation of insights and adjustments that cumulatively constitute profound increases in cognitive capability. Current efforts to spell out the mechanisms of such learning benefit from detailed descriptions of development within particular domains of knowledge as well as formal innovations (for example, theories describing the conditions that make a language learnable), computer simulations of change, and the investigation of social-environmental conditions that foster or interfere with self-motivated learning, knowledge transfer, and problem solving. (The case of language learning is discussed later in this chapter.)

Categorical Knowledge and Representation

One aspect of development is the creation of categorical knowledge—concepts and relations among them—which is the preeminent form of mental framework for interpreting and understanding experience. How are categorical ideas represented in human knowledge? A traditional view holds that categories are defined by a list of necessary and sufficient properties that an object must have in order to be a member. Scientific categories (for example, mammal) often have this characteristic. However, categories in natural language generally do not. This is true of relatively concrete ideas such as dogs, trees, and chairs; more so for somewhat more abstract categories such as animals, fruit, and furniture; and completely for thoroughly abstract concepts such as freedom, happiness, and justice.

CATEGORIZATION How are categories defined in the mind? How do people recognize instances of categories? These questions are fundamental to understanding human thought, since reasoning about things is based on knowledge about them, and knowledge about things is largely knowledge about categories of things.

Sharp boundaries between categories, based on strictly defined sets of properties that an object must have to belong to the category, occur in many scientific concepts but rarely in the world of experience that is expressed in natural language. For example, what makes something a “cup”? The evidence of numerous experiments in several languages is that it helps for the object to have a handle, to have sides that bow inward, to contain a beverage, to be made of ceramic, and to look slightly wider than deep. A tall glass cylinder with no handles and a flower in it is clearly not a cup, but something can still be a cup if it has straight sides, or if it lacks a handle, or if it has a flower in it—as long as it has most of the other properties associated with cups. As particular properties change, an object tends to be recategorized into distinctly different categories such as glass, bowl, or vase, sometimes passing through transitional or subordinate concepts such as mug. This figure shows such categories and characteristics.

From a wealth of such findings arose the idea that, in natural language, category membership is a graded function of the typicality of the properties of an object relative to other members of the category; a category is thus represented in the mind by an abstract prototype and specifically remembered examples, and a new object is compared to both.

Not all human knowledge consists of propositions, concepts, and principles that can be expressed verbally. Crucial components of knowledge are based on visual and auditory imagery and on patterns of motor activity. Research during the past 15 years has made major advances in understanding the properties of spatial cognition and action and ways in which brain structures and processes are involved in cognitive functioning in different domains.

Major advances in the study of visual imagery have included identification of specific mental operations for manipulating spatial information. A landmark demonstration based on a typical performance IQ test item established that the decision as to whether two simple figures are the same or different is based on an imaginary mental rotation of the image, much as one would physically rotate an object with one’s hands. The time required for such judgments has been shown to depend approximately linearly on the angle through which a figure has to be rotated in the mind’s eye. This finding is consistent with the hypothesis that such mental operations are analogous to spatial ones. Other tests about the nature of spatial reasoning draw on quite different spatial operations, such as expanding or compressing the size of an image or inferring the appearance of a scene from different perspectives. These tests are also consistent with the hypothesis of some sort of analog representation, although in each case one can also devise explanations in terms of descriptive statements about the object.

Not all physical properties of objects need be encompassed by the cognitive mechanisms of imagery. Researchers are now working on such questions as: Which properties of physical transformations are preserved in mental images? What are the differences in imagery skills among individuals? How do these differences arise? It is now established that imagery is not a unitary, undifferentiated phenomenon but consists of distinct spatial abilities. Individuals can be good or bad at a given component of imagery ability independent of the other components. Research on imagery is leading toward an increased understanding of the cognitive deficits that can result from brain damage, as well as useful characterizations of intellectual differences that may be relevant to observed male-female differences in intellectual style, science and mathematics education, and other scientific and practical questions.

As the imagery example illustrates, one legacy of research on intelligence testing is the idea that there are different kinds of intelligence. Some theorists are exploring the related hypothesis that there are specialized modules of thoughts, each with separate sets of principles, not only in language but in other forms of symbolic processing, such as in the way all living creatures learn to find their way in space. It is striking but taken for granted that people can recognize places while apparently lacking detailed information about them. For example, many people can retrace a route along city streets, knowing when to turn at a particular corner, without knowing street names or being able to recall in detail what is at that or any nearby corners. What does this mean and how does it relate to imagery? To what extent could things be scrambled or replaced and a location still be recognized as the same place? How can robots be programmed to recognize readily where they are? The problem of recognition is clearly important and most likely complicated, even though many animals seem to possess the skill. Work on this topic is pursued fruitfully by researchers at many levels of analysis, from neurophysiologists mapping the locations of these functions in the brain to psychologists, linguists, anthropologists, city planners, and ethologists describing and modeling the behaviors involved in moving efficiently and communicating efficiently about location.

Individual Decision Making

Decision making—choosing among options—is a central topic of research on modern life. Studies of the decision making of individuals have followed two broad, intertwining pathways—normative and descriptive. Research in a normative vein seeks to define the conditions, practices, and procedures under which decision makers can achieve some prescribed goal, such as maximizing expected utility or satisfaction, achieving economic efficiency, or securing democratic representativeness. A central line in normative research is to illuminate rational selection among alternative actions that have complex patterns of possible outcomes depending in part on chance and uncertainty in the environment. Research in a descriptive vein seeks to understand the mechanisms and procedures actually followed by individuals in reaching decisions, particularly when normative approach is difficult to specify or calculate. Each mode of research stimulates the other, and many assumptions, hypotheses, and findings are common to both.

Normative research has traditionally been guided by a theory based, in part, on the principles of mathematical probability and, in part, on calculation of trade-offs between the values of outcomes and the probabilities of their actual occurrences. The modern development of normative decision theory began in the 1940s with the introduction of an elegant mathematical theory of games and economic behavior that laid out a rational basis for making choices among actions whose outcomes are partially determined by chance events with known probabilities of occurring. This theory reduces in practice to a rather simple numerical model for computing which alternative move in a situation has the largest average utility and should therefore be the one selected. Despite its logically compelling character, however, the theory does not seem to be followed by most people when they make individual decisions. As a result, a noteworthy line of experimental study is trying to formulate and systematically understand in what ways people depart from the normative theory.

A considerable body of economic and statistical work has been built on normative decision theory, and it has proved powerful when applied to behavior in various financial and insurance markets. But objections have been raised against applying the theory in other areas, both by decision theorists on formal grounds and by experimentalists carrying out carefully designed studies of the choices people make under well-controlled conditions. In the past 10 years, the empirical results consistently showed departures from rationality postulates, and these findings have created a new challenge for theory development. Because the empirical findings are complicated, it is as yet unclear exactly which one (or more) of the basic postulates of rationality is the principal culprit and needs to be modified. Further experimental studies and development of more satisfactory theoretical fundamentals are proceeding.

A study by physicians and behavioral scientists of how people use statistical information in decision making highlighted the importance of how the possible outcomes are perceived. More than 1,000 people—graduate students in business school, physicians, and medical patients—were asked to imagine that they were suffering from lung cancer (none was known to have this disease) and could elect one of two treatments: surgery or radiation. The subjects were presented with the statistical prognosis for each treatment: for one group, the outcome was stated in terms of the probability of surviving for various lengths of time (for example, a two-thirds chance of living at least 1 year following treatment); for the other group, the outcome was stated in terms of the complementary probabilities of dying within those lengths of time (for example, a one-third chance of dying during the first post-treatment year).

When presented with the two therapeutic options framed in terms of survival chances, the students chose radiation over surgery only 17 percent of the time, while the students presented with the identical prognoses framed in terms of the chances of dying chose radiation 43 percent of the time. When radiologists were the subjects, the contrast was just as pronounced: 16 percent chose radiation when presented the survival prognosis and 50 percent chose it when presented the mortality prognosis. Among patients with chronic medical conditions, a considerably older group, the results were 20 percent and 40 percent in preference for radiation. Since the situations described to the subjects were simple and logically identical, these data clearly contradict a postulate of traditional decision theory that identical situations be treated the same. The framing of the problem clearly matters. Various studies are now in progress to expand and test several alternative theories of these framing effects, and research in the next several years is likely to bring major developments in this fundamental area of inquiry.

A major line of work is focused on the fact that individuals often appear to rely on conventional biases, simplified concepts, and common rules of thumb in making decisions, rather than developing close probabilistic calculations or estimates. Although such heuristics typically reduce informational and cognitive demands for reaching decisions, they lead to demonstrably and systematically incorrect results in certain very common situations. For example, the heuristic of representativeness leads people to think that a more “typical” event is also a more probable one, even when this is logically impossible. In one experiment, respondents were asked to report on the relative likelihood of various possible events, an example being the performance of championship tennis player Björn Borg in a hypothetical Wimbledon match. A substantial fraction of subjects thought it more likely that ( a ) Borg would lose the opening set but still win the match than that ( b ) he would lose the opening set, whatever the final outcome. Yet possibility b has a likelihood equal to or greater than a , since b includes every instance of a as well as the additional possibilities of losing both the first set and the match. This kind of “cognitive illusion,” at tributed to people using the heuristic of representativeness, has been confirmed in many experiments.

Two other common heuristics are availability and anchoring. Availability refers to the fact that estimates of the likelihood of an outcome are unduly influenced by the ease with which examples of particular outcomes are brought to mind. For example, most people will conclude that there are more words with r as the first letter than the third because it is easier to build a list of the former, but the latter are in fact more numerous. In anchoring, an individual’s final estimate or judgment of a situation is overly influenced by the first of multiple examples or reference points that are observed; for example, extra weight is attached to the first of a series of numbers whose average (mean) is to be estimated. Knowledge about these and other heuristics and framing effects is laying the foundation for a more general theory of how information is used by individuals, and consequently, for a more thorough and precise understanding of individual decision-making behavior in general.

Framing effects are recognized and manipulated on an intuitive level by publicists, advertising specialists, and politicians, among others. However, new formal models incorporating framing effects and decision-making heuristics are likely to have important new policy applications. For example, studies of framing effects may affect how ingredient or warning labels are written, how truth-in-lending laws are formulated, how unit prices are displayed in supermarkets, and how election ballots are designed.

Progress in understanding decision making has relied heavily on theoretical analysis and questionnaire-based experiments; there have been only limited observations of behavior in real decision situations. Now, however, researchers are beginning to use more complicated and realistic methods of dynamic study, including interactive computer-run experiments, improved field observation methods, and refined techniques of statistical inference to study actual behavior. A unified understanding of framing, biases, and heuristics, including the conditions that generate them, their robustness and their consequences, will become more possible as researchers are able to characterize choice behavior as a multistage process that involves information, evaluation, expression, and feedback. A promising parallel development is the fuller inclusion of these characteristics of choice behavior in systems models, working out their implications for market efficiency and other aspects of organizational performance.

Reasoning, Expertise, and Scientific Education

How can general science education provide more students with durable scientific concepts and principles? It is widely recognized that such instruction often fails to communicate the fundamental meaning of scientific concepts. Indeed, recent research has dramatically shown that many people interpret physical phenomena in ways that are contrary to principles that they have apparently learned well, at least to the extent of being able correctly to solve typical textbook problems. For example, many students who know how to calculate the Newtonian formulas fail to invoke the Newtonian principle of inertia when asked to sketch roughly the path followed by an object dropped from an airplane in flight. Researchers ask: Do people base their incorrect judgments on fragments of knowledge that come from experience, such as the way in which objects ordinarily fall when they are dropped? Or, do people have relatively coherent, but incorrect, naive theories about motion, gravitational force, and the like? Since people cannot report how their judgments in these matters are formed, current researchers are attempting to find out through indirect approaches. The answers have important implications for science education because one uses quite a different set of instructional methods to teach students when to apply conceptual schemes than one uses to bring about major reconstructions of the students’ naive theories.

The handling of concepts, reasoning, and skills by “experts” is of great interest to information technologists and educators. Initially, two complementary ideas about experts were prevalent: that they have exceptional mental capacities, such as an innate or trained ability to retrieve more facts or consider more possibilities at a time than do nonexperts; and that they have accumulated more knowledge about their subject than nonexperts, often because they have many years of experience. These ideas were incorporated into the design of early programs in artificial intelligence, in particular those for chess, for which the main goal was believed to be an ability to selectively consider many moves in advance. These ideas also underlie the design of contemporary computational “expert systems,” most of which incorporate large collections of knowledge. These same ideas are also reflected in many features of the educational system, where achievement in teaching and learning is often assessed exclusively with tests of factual knowledge or computational accuracy.

However, researchers have shown that the underlying structure of knowledge is at least as important as the amount of information. Although experts do remember a great deal of specific information, their capacity to do this mainly depends on their having acquired elaborate, highly organized structures of knowledge. For example, chess experts are hardly better than nonplayers at remembering a randomly jumbled set of pieces on a chess board—but they are far better at remembering a coherent board. Furthermore, experts, like novices, are typically not fully aware of the principles by which their knowledge is organized and used, so these tacit forms of knowledge are not reported by them. Thus, most expert systems now in use neglect some of the most important aspects of expert knowledge.

One such well-known system was designed to assist in the diagnosis of infectious diseases and prescription of antibiotics. Initially, the knowledge simulated in the system was a set of relatively simple rules of inference and a program that evaluated hypotheses simply by accumulating the positive and negative evidence it received. It matched reasonably well the judgments of successful physicians about the specific knowledge they used in their diagnostic work. But when the system was extended to aid in the training of physicians, it did not work well; it became evident that the organization of its knowledge and its methods of reasoning were seriously deficient. The program was reorganized based on more thorough analyses of the knowledge and real-time reasoning sequences of physicians in their diagnostic and training activities. These analyses showed that diagnostic strategies, including the use of hypotheses in selecting questions in interviews and the comparison of symptom patterns with the physician’s mental representation of disease conditions, play a crucial role in diagnostic performance. These usually tacit components of knowledge have to be explicitly considered in physicians’ training; the latest version of the expert system attempts to bring such tacit knowledge into play.

The importance of general concepts and principles in expert knowledge is also demonstrated in recent research on problem solving in physics, in which expert and novice performance has been contrasted. An expert’s understanding of problems includes the general qualitative concepts and principles that the problem illustrates, such as conservation of energy and laws of force, and these principles are used to organize the expert’s reasoning in the problem-solving process, leading to calculations as a final step. In contrast, a novice’s understanding of problems mainly involves more superficial features, such as the kinds of objects in the problem. The novice typically seeks solutions by translating the available information directly into formulas that allow the quick calculation of an answer—correct or not. Indeed, as noted above, students’ knowledge of formulas is often quite disconnected from their understanding of general principles.

People tend, as they become expert, to reorganize their knowledge of a given domain, and this tendency is not restricted to adults. For example, young children have much implicit knowledge of the difference between animate and inanimate objects. They know that animals move by themselves, that trees cannot have feelings, that dolls lack brains. But they do not assume that all animals breathe, reproduce, and so forth. Neither do they classify plants and animals together. They come to do these mental tasks, around ages 8 to 10, when they reorganize their knowledge about objects in the world, independent of explicit formal instruction, in accord with an intuitive theory of biology. The growing realization that children regularly reorganize their knowledge is of considerable import. These naturally occurring mental processes help uncover the laws of theory construction and concept reorganization that underlie the acquisition of expertise.

Research has begun to clarify some of the ways that understanding of general principles contributes to expert problem solving and reasoning, and it has also begun to show how children develop intuitive understanding of important general concepts and principles. Researchers are just beginning to be able to characterize that understanding in explicit, testable ways. The crucial issue now is constructing a rigorous theory of the cognitive processes that are usually categorized as “intuition,” involving qualitative reasoning about quantitative and other abstract concepts. A promising start has been made in formulating such theories, which will enable experimenters to test hypotheses about the properties of this form of expert reasoning that previously eluded systematic study and theoretical analysis.

Complex Action

The miracle of action is the ability of the mind to produce organized acts related to plans of action, perceptions of the world, and motives. People are not born with this ability. Newborn behavior appears to be comprised primarily of random movements and rhythmic stereotypes; over the first year of life they are gradually transformed into voluntarily guided, intentional, purposive actions. The study of such motor skills from infancy to advanced performance is exploding after a long, relatively dormant period. The basic research interest in this area is complemented and invigorated by advances in robotics, by searches for a better fit between people and machines, by problems that occur in the manufacture and use of motor prosthetics, and by medical concern with motor disorders ranging from stuttering to Parkinson’s disease. Exciting and promising results have been facilitated by new optical, magnetic, ultrasonic, and x-ray technologies for transducing motion, for storing the massive amounts of data collected, and for analyzing these large data bases, sometimes using artificial intelligence methods.

Researchers are addressing a wide variety of questions, such as: What aspects of movement does the nervous system control? In accomplishing an action, how does the system constrain the many motions that are biomechanically available? What determines the accuracy of reaching? How are limbs coordinated? How is serial order represented and realized in planned movement sequences? How do errors in speech and typing come about?

Consider a person reaching his or her hand toward an object, a motion controlled by an interplay of elbow and shoulder rotations. Biomechanically, the hand can approach the object along any of a vast number of paths; one might expect the actual path and the velocity profile along that path to reflect this complexity of control and to vary with the conditions of movement. Yet in the horizontal plane, the path is essentially a straight line, exhibiting a single-peaked, bell-shaped velocity curve. More generally, it is the overall movement itself, and not the pattern of activity in individual muscles, that is invariant during compound arm movements. Such simplicity suggests planning at the hand level rather than the joint level, with the system generating complex, coordinated joint-angle changes to achieve simple hand trajectories.

Also in the domain of single actions, there is a new understanding of the remarkably general logarithmic trade-off between the speed and spatial accuracy of limb movements. The prevailing theory had been that precision slows a movement because of an increase in the number of visually guided corrective submovements, with each submovement independent of precision. However, current experiments, growing out of a new mathematical theory, show that the trade-off occurs without visual feedback and that submovement speed varies with precision.

How are single actions combined into ordered sequences? Studies of speech and typing during the past few years have revealed advance planning of entire sequences and the hierarchial organization of actions in multiaction units. For example, in rapid utterances in languages such as English, the unit is not the syllable, or the word, but the stress group, a sequence of syllables containing a primary stress, which usually corresponds to a grammatical phrase or clause structure. This supports the traditional hierarchical model of speech production, but it has also led some researchers to posit a network model, in which activation spreads both “up” from the sensors and “down” from the control nodes of the network. Only a hierarchical model can thus far explain facts of slips of the tongue, and new methods of inducing speech errors under laboratory conditions have facilitated research of this topic. In a similar vein, a model of typewriting, based on parallel distributed processing that converts a sequence of discrete symbols into continuous and temporally overlapping movements of fingers and hands, explains many features of timing and errors in the performance of skilled typists, including how a stroke by one finger can be accompanied by movements of other, then irrelevant, fingers to position them more favorably for action two or three strokes later.

This is an active and exciting period in the study of complex human action. New findings are expected to improve person-machine interfaces (for example, instrument panels and keyboards), skill training, implementation of artificial movement systems (prosthetics, manipulators, robots), the diagnosis and treatment of movement disorders, and understanding of other complex skills like talking and walking.

To be fluent in a language is to be able to produce and understand an indefinite number of sentences never spoken or heard before. As one component of this ability, every spoken word can be identified by hearers in less than one-third of a second, drawing on the more than 100,000 forms stored in the mental dictionary of a typical monolingual adult (bilingual or multilingual people store hundreds of thousands of word forms). In little more time than it takes to process the sounds themselves, the words are then assembled into meaningful sentences that more or less correctly represent the message intended by the speaker.

This casual miracle of communication is possible in part because the human brain is uniquely suited to acquire and use language. Chimpanzees and gorillas are now widely viewed as having greater nonlinguistic cognitive abilities than previously thought, but they are unable, even with the most intensive human training (in sign language), to learn 1 percent of the vocabulary that is acquired by virtually any 3-year-old human child. Nor can these primates learn even the simplest of the complex grammatical rules known to nearly any 2- or 3-year-old human child.

Since nearly all humans are fluent in at least one language and, except when learning a new one, seldom consider what makes this possible, the complexity of the knowledge underlying the ability to speak and understand, and to read and write (abilities derived from spoken or signed language), is often not fully appreciated. Though the question of language acquisition and use has puzzled philosophers, educators, and scientists throughout modern history, answers to the puzzle have proven elusive until recent advances that rest, in part, on a sophisticated modular conception of language and its relationship to other cognitive faculties.

While it was once commonplace to view the grammatical properties of language as essentially derivative—a by-product of the general cognitive, physiological, and other nonspecific systems underlying human intelligence—new evidence has convinced a growing number of scientists that linguistic capacity (and possibly, the mastery of grammar itself) is best viewed as an autonomous cognitive system, serving other systems but governed by its own set of distinct principles. For example, discourse patterns have been discovered that suggest that speakers can accommodate only one item of new information in a grammatical clause. Moreover, it appears that speakers restrict the appearance of this new item to certain specific grammatical roles within the clause. This and related results have led to proposals of specific models of cognitive resources that both enable communicative processes and limit their scope.

Principles of language performance and processing by no means exhaust the realm of possible knowledge about language, which is perhaps our richest cultural heritage, produced collectively over thousands of years and used for a great variety of social purposes. But this approach has led to entirely new methods of investigation that underlie some of the most important recent discoveries.

Acquisition

The remarkable human facility to acquire language depends on a rich genetic endowment. People are well equipped for fluency in language (speech and gestures), just as birds are especially equipped to acquire and perform the songs of their species. Newborn infants, for example, respond to acoustic distinctions that are systematically used in some human languages—even though not necessarily in the child’s own linguistic environment—in a way that is different from their responses to distinctions that are not linguistically significant in any known language. In a study of speech perception in 4-month-old infants, the child sucks rapidly to hear “pa” or “ba”. As infants become habituated, the sucking rate drops. When a new stimulus is substituted (“ba” for “pa” or “pa” for “ba”), the infant dishabituates and sucks quickly once again. Similar results have been obtained for 1-month-old infants.

These American infants thus show a sharp boundary in discriminating between the sounds/ba/and/pa/, a phonemic boundary in the English language. But these same American infants have an analogous sharp discrimination boundary in a prevoicing region that is not a phonemic contrast in English but is in certain other languages, such as Thai. Adults have considerable difficulty making such discriminations when they are not phonemically contrastive (functionally important) in their language. This indicates that certain aspects of phonological sensibility may be “prewired.”

Linguistic abilities can also be dissociated developmentally from other cognitive abilities. There are numerous cases of children who have few cognitive skills and virtually no ability to use language in sustained, meaningful communication and yet have extensive mastery of linguistic structure. For example, one severely retarded young woman with a nonverbal IQ of 41 to 44—who lacked almost all number concepts including basic counting principles, drew at a preschool level, and possessed an auditory memory span of three units (for example, syllables such as “two, three, one”)—could nonetheless produce syntactically complex sentences like “Last year at school when I first went there three tickets were gave out by a police.” In a sentence imitation task she both detected and corrected surface syntactic and morphological errors. But she did not know how many “three tickets” were and was not sure whether “last year” occurred before or after “last week” or “an hour ago.”

Conversely, there are cases of children with little grammar but with other verbal abilities. One girl who was physically and socially isolated from the world from approximately age 1 to 14—with no language input during that period—rapidly acquired a large vocabulary following her liberation, but her utterances remained nongrammatical, devoid of morphological endings (for example, past tense or plural markers) or syntactic operations (for example, converting statements into questions). This contrast between word lists and grammatical rules is indicative of different and distinct abilities.

Clinical studies of aphasia have given dramatic confirmation to these new fundamental theories. For example, local damage to certain regions of the left brain do not lead to across-the-board reduction in language ability, but to selective, deep deficits, consistent with the idea of independent grammatical components or modules. Some patients with left-brain damage make many semantic substitutions in reading words: saying “pixie” when asked to read “gnome,” “sick” for “ill,” “prison” for “jail”. Some can read a word like “tortoise” perfectly but cannot say what it means; some speak fluently but with nonsensical content; others speak in telegraphic style, leaving out all the short function words. Clinicians are able to use linguistic phenomena such as these, together with analysis of the known brain damage and previously observed patterns of correction between such symptoms and brain damage discoveries in autopsies, to improve the diagnosis and treatment of aphasia.

The dramatic new technologies of neuroimaging, such as computerized tomography (CT), magnetic resonance (NMR or MRI), and emission tomography using positrons (PET) or single photons (SPET), now make possible the exact delineation of brain structures involved in various language functions. A major stimulus to further progress in this field may take place when imaging technology becomes more widely available for research with a variety of populations. For example, since aphasias occur among speakers of all languages, research on aphasia can help isolate the basic, universal capacities and neural substrates underlying human language. Because languages like English rely heavily on word order to convey information that languages like Russian signal through inflections (suffixes, prefixes), an important question is whether patients with neurologically similar brain lesions (discoverable through imaging) but who speak very different languages will exhibit manifestations of the lesion that seem very different yet correspond to the same underlying abstract functions.

The organization of language mechanisms is also being studied using electrophysiological techniques, such as scalp recordings of event-related brain potentials (ERPs), which measure the electrical fields that arise from coordinated groups of neurons engaged in processing sensory, cognitive, and linguistic information. By studying how ERPs vary in time, it has been possible to differentiate among certain linguistic operations. The various components of ERPs exhibit asymmetries over the left and right sides of the brain and are sensitive to such factors as handedness and mode of language acquisition (spoken versus signed).

Another important new line of research links language acquisition studies with theoretical work in cognitive science and artificial intelligence. One emerging area is the theory of machine inductive inference, which investigates how intelligent systems develop logical models or schemas based on evidence from their environment: for example, the inferring of the grammatical structure of a language based on utterances heard and overheard. This theory provides a framework for systematic comparison of various learning algorithms in terms of their relative strengths, resource requirements, and behavior in various environments. When combined with empirical studies of language acquisition, the theory of machine inductive inference provides constraints on the character of learning strategies implemented by children and reflects on the character of the class of languages that can be acquired. Such studies are important to system builders in artificial intelligence.

Sign Language

Linguistic research on the sign languages of the deaf, particularly American Sign Language (ASL), is only 25-years-old, and it has opened a very important avenue toward a deeper understanding of all language. In spite of its name, ASL is not a signed version of American English, but rather a complete language in itself. It is more closely related to French sign language than to spoken English or British sign language, which is logical because it was first brought to the United States by teachers of the deaf from France. ASL has all the crucial properties common to spoken languages, including highly abstract underlying grammatical and “phonological” principles. The relationship between the form of a sign and its meaning is as arbitrary as that between the sound of a spoken word and its meaning. Sentence formation in ASL is just as rule governed as it is in spoken languages. ASL uses facial and other simultaneous body gestures (for example, lifting of eyebrows) to convey linguistic information similar to the morphological inflections that occur in spoken language. Variations in tone and emphasis convey additional layers of meaning in both spoken and sign languages—in the latter, through the pacing and shaping of the gestures.

Like hearing children with speaking parents, deaf children with signing parents acquire their native language without formal instruction and in similar stages. Brain studies of normal signers and deaf aphasics (patients suffering language loss following left-side brain damage) show that the left cerebral hemisphere is just as dominant for sign language as for spoken language. This finding has been a definitive result in proving that the left-hemisphere specialization in the brain in language acquisition is not due to its capacity for fine auditory analysis, but for language analysis as such.

Grammatical Universals

Work on formal theories of grammar in the past 25 years has considerably sharpened understanding of linguistic universals—principles that are common to all languages. For example, despite the fact that the rules to form passives (“The ball was thrown by John”), questions (“Who threw the ball?” or “The ball was thrown by whom?”), and imperatives (“Throw the ball!”) differ markedly from language to language, modern theory and data argue that such constructions are manifestations of simple but highly abstract underlying principles of grammar that differ only slightly across tongues. Work on languages related to English—such as Dutch, French, Spanish, and Italian—and nonrelated languages—such as Japanese, Chinese, Arabic, Hausa (of West Africa), and Warlpiri (of Australia)—support this view. Data on a wide variety of languages, as well as observations about how languages are acquired, are expected to contribute in a major way to the development of a viable formal language learning theory. In particular, these data will permit the testing of various proposed theories of the learning process, including computer simulations of language acquisition, models evaluating the chances of learning a grammar from observing a modest sample of the language, efficient forms of artificial intelligence, and explicit behavioral models of language performance and proficiency. Grammatical and word acquisition studies in children should help to resolve controversies regarding the species-specificity of language, language-specific innate constraints, and the relative contribution of the child and the environment to the learning process. The improvement of communication between people and computers by developing programming modes closer to “natural language” will also benefit from this work.

A major research task in the next decade is to explore more completely the hypothesis of grammatical universality, through intensive investigation of languages that are just now being fully described and are historically unrelated to the commonly studied ones. This work will involve considerable coordination and a stronger international basis between theoretical linguists and those descriptive linguists whose research is directed toward these frontier languages. It is also vitally important to this work to add to the relatively sparse instances of longitudinal studies that cover the same people over a period of many years and across a variety of linguistic learning environments.

Machines That Talk and Listen

Shortly after World War II, attempts were initiated to devise computer schemes to translate from one language to another, to recognize spoken and handwritten language, and to convert text into natural-sounding speech. Abortive and highly expensive early approaches to automatic machine translation and speech recognition were made in electrical engineering projects, but they did not address the complexities of human language; participants in these efforts came ruefully to appraise their results as “language in—garbage out.” After engaging linguistic and phonological expertise to diversify the lines of research undertaken, team efforts ultimately did make good progress, leading to the scientific and practical successes seen to date.

At present, interdisciplinary research aims to explain the intricate relations that hold between language, the world, and intelligent systems (whether natural or artificial). In one line of research, formal linguistic models are being developed that make explicit provision for the varying computational requirements of language understanding. Relating linguists’ grammars to existing computer systems enables computer scientists to provide a new generation of interpreter programs that run much more efficiently than their predecessors. A new theory of the semantics of programming languages promises to unite two formerly separate analyses: the denotational meanings of terms in natural language and the functional meanings of instructions in a computer program. Other developments in this work include knowledge about the recursive (looping-back) nature of computational processes, symbolic systems, and the importance of shared knowledge and beliefs in conversational face-to-face interactions.

In recent decades, collaboration between linguists, communication engineers, and computer scientists has led to dramatic increases in knowledge and new methods for analyzing and synthesizing acoustic speech signals. The computer revolution has made it possible to acquire and analyze in hours or days, rather than months or years, the large phonetic data bases needed to study the sound structures of language. As a result of the investigation of intonational and other phonetic properties of speech, undertaken with the goal of constructing machines that talk and listen, there is a better understanding of how the units of a linguistic system relate to acoustic signals, leading to more natural-sounding, machine “voices” and more capable machine “ears.” However, the present ability to synthesize speech far exceeds the ability to automate speech recognition and understanding, because the normal acoustic flow of speech cannot be readily divided into neat, uniform segments corresponding to discrete sounds, syllables, words, or even phrases, as is readily apparent by listening to unfamiliar languages. At any instant of speech production, several articulators (larynx, tongue, velum, lips, jaw) are executing a complex, interwoven, rhythmical pattern of movements, blurring the boundaries between words as well as between the phonemic segments that compose words. The problem is further complicated because each perceived unit—sound, syllable, word, or phrase—varies widely with phonetic context, stress or emphasis, and the rate of speech, style, dialect, and gender of individual speakers. (The additional problem of discriminating a speech signal from background noise, including other speech, was noted above.)

What is invariant that enables people to recognize and understand speech? The discoveries of the last several decades have contributed to speech-recognition schemes, but they are still far from satisfactory; since they can deal at most with 5,000 words, compared with the 14,000 known by a 6-year-old or the 100,000 to 150,000 used by monolingual adults. The current solutions are still not very subtle. It is expected that dramatic improvements will accompany increased understanding of how the brain carries out the segmentation and analysis provided by the linguistic system.

Reading incorporates a complex hierarchy of skills, each raising a long list of questions in its own right. What are the fundamental perceptual processes involved in registration of visual print? How does word recognition occur? In the absence of aural and gestural cues, how do readers accomplish phrase interpretation? How are processes of inferential reasoning, critical thinking, and behavioral response catalyzed by the text? How predictable are these responses?

There have been fairly dramatic advances recently in understanding each of these interactive processes linking reader and text, especially as a result of developments in the cognitive sciences. These advances have yielded new insights into what the eyes and brain do that enable a reader to comprehend written language. These insights have progressed to the point that computer simulations can pinpoint exactly where and why a given reader (with known skills) encounters difficulty in a text and disclose what might be done to avoid or ameliorate those difficulties.

Knowledge from this research has led to educational strategies that result in improved reading skills in children. For example, in some projects, children who are poor readers have been taught particular cognitive strategies discovered in the skilled adult reader, such as how to identify and retain the most central information in a text. Children given this training show large and general improvements in comprehension. Other studies are implementing new programs to teach word recognition, taking advantage in some instances of computer-based technology for practice, drill, and individual tailoring of the curriculum. The research has practical importance for scientific and technical documentation, display technologies, and instructional methods, and it also provides a major opportunity to expand the frontiers of knowledge about interactive multilevel learning.

Decoding and Dyslexias

The process of decoding letters and words is critical in learning to read and has received a great deal of attention over the years, constituting the bulk of the research relating to reading. One central issue in research and pedagogical practice is whether a reader must use the printed words to retrieve a sound and then use the sound to retrieve the meaning of the word. Most of the evidence shows that a mature reader needs an intervening phonological code only if a word is unfamiliar or if the material is particularly difficult. But phonological decoding is critical when first learning to read. For example, awareness of the phonological structure of language is one predictor of the rapidity of early reading progress; 5-year-old prereaders who can segment a spoken word into its constituent phonemes (who can, for example, follow an instruction to say “table” without the t ) tend to be better at word recognition at the end of second grade. Such results reinforce the importance of a phonics approach at the earliest stages of reading instruction, when fluency of word recognition is the key factor to rapid progress.

By third or fourth grade, children have generally mastered recognition skills sufficiently so that individual differences in reading skills no longer closely reflect differential word-decoding abilities. At these stages, knowledge of particular kinds of text structure, such as narrative, becomes increasingly important to comprehension, and these matters are now receiving greatly increased attention. Even very young children are aware of narrative conventions and use this knowledge in understanding stories that are read or told to them. Recent analyses of grade-school materials suggest that many selections violate conventional story structures, making it difficult for a learning reader to establish the coherence of the story. Research on reading comprehension will lead to better selection of useful texts for teaching.

Some grammatical structures are known to be used much less in speaking than in writing. For example, cleft constructions like “It was John who won the trophy” are rarely spoken in English; rather, vocal stress on the name—“ John won the trophy”—is sufficient to convey that the identity of the winner is the key information in the sentence. These differences between reading and speaking seem small, but for some dialects they may result in serious interference between the spoken language and comprehension of the written language. Such difficulties have been noted in some speakers of black English and some deaf children, for whom written English primers are virtually samples of a foreign dialect. While learning to read a second language without first knowing how to speak it is not uncommon for adults who are already literate in their native speech, it is a very unusual and difficult hurdle for children who are first learning how to read.

Explaining the wide variation among early and middle readers in the rates at which basic skills become automated and more advanced ones develop and finding the causes behind reading disabilities (dyslexias) are major challenges. Research on reading disability has proven to be especially difficult. There is still no general consensus about the nature or definition of dyslexia, and indeed there are probably several distinct kinds of dyslexia, some of which are corollaries or causes and some of which are effects of reading dysfunctions. The field has discarded numerous theories that have not stood up to close testing, such as the hypothesis that the disorder is visual, involving reversals between letters such as b and d or words such as “saw” and “was”; that the disorder involves particular difficulty in associating visual and verbal elements; or that poor readers have difficulty in maintaining information about sequences. Other theories that have some support, but not full assent, include the idea that there is a deficiency in the specifically auditory-linguistic background and that the problem results from contracted vocabulary, low verbal fluency, inappropriate grammar or syntax, or difficulty or slowness in word retrieval. The most promising approaches to these tangles of cause and effect appear to be continued fundamental research on normal acquisition of reading skills, more detailed examination of disabled individuals by interdisciplinary terms—psychologists, cognitive scientists, medical researchers, and educators—and more longitudinal research, starting with prereading children.

Interpretation and Comprehension

READING How do a reader’s eyes move across the written page? What does the mind do as the eye moves? How are marks on paper interpreted as words that convey ideas?

Researchers have developed methods to mechanically track a person’s eye movements while reading a text and to meticulously analyze the patterns of gaze and subsequent accuracy of comprehension of the material. Such experiments are used to test and develop theoretical models of how people process what they see on paper. The different gaze patterns in this figure, represented by the pause durations (in milliseconds) and retracking lines, are typical of different kinds of readers. (There is no significance to the different texts shown; they are standard passages used in experiments.) Though texts differ in complexity, a given reader processes them the same way and at relatively small variation in overall speed.

Most people sample a written text densely, fixing their gaze (for more than 50 milliseconds) on about two-thirds of the words and rarely skimming past more than one word at a time; in most cases, the skimmed words are articles, prepositions, and conjunctions, “function words.” Nearly all readers interpret each word immediately, inferring its meaning even when important clues to that meaning come later in the text. The duration of gaze on each word depends on the word’s length, familiarity, and grammatical or contextual clarity. There is also a tendency to pause longer on the word at the end of a printed line—rather than at the end of a sentence—to “wrap up” any loose ends in interpretation. Normally, readers retrace their gaze only when a grammatical or semantic ambiguity leads them to rethink how to interpret a phrase.

Readers trained to “speed read” fixate on fewer words and spend less time on each fixation. Like normal readers, they retain very little information from words they do not fixate. The major skill that speed readers learn is to infer the meaning of a line of text based on a smaller sample of the words in it than before and not to tarry or retrack in order to resolve local ambiguities in meaning. The cost of these rapid inference procedures is less accurate comprehension, especially for nonrecurrent details.

Dyslexic readers seem to have trouble mainly in decoding written words, that is, segmenting the letters into syllables and retrieving their sounds correctly. These readers spend extra time on each word and frequently re-track in order to reinterpret inaccurately perceived words. And even at their slower reading rates, dyslexic readers generally develop a much less accurate understanding of a written text than other readers—a problem they do not necessarily have in interpreting spoken words.

Since a reader is constructing a mental representation of a text while processing only a single phrase or sentence at a time, short-term memory limitations may play an important role in reading comprehension. This is not a new hypothesis. But the original theoretical claims that short-term memory may be an important bottleneck for comprehension processes were contradicted by the fact that standard short-term memory tests (for example, digit span) did not correlate with reading comprehension skills. The more recent experimental work does not evaluate short-term memory capacity in static contexts or at nonreading tasks; rather, it evaluates how much excess capacity different readers have available during the actual reading process: the resulting measures are highly predictive of comprehension levels.

Detailed temporal analyses of this sort have also made it possible to write a computer program that reads newly encountered though very carefully edited and prepared texts about as well and as rapidly as human readers. The program “pauses”—taking longer to process a passage—when there is difficulty in comprehension and speeds up when comprehension is easy. It can produce a summary or answer certain kinds of questions about the content of texts about as well as humans can.

Efficient readers adapt their expenditure of effort and ingenuity to their goal in reading, such as “light” reading for relaxation, reading to learn, or reading to react to an action document, such as a request for funds. Mature readers possess a complex repertoire of reading and study strategies for enhancing understanding and for detecting and overcoming comprehension difficulties. Considerable advances have been made in understanding of how these learning activities develop and how they can be enhanced by instruction. All of these advances can now be described and measured in terms of scientific theory rather than just intuitively, as was once the case.

No artificial intelligence reading program comes close to the seemingly effortless way in which humans seem able to call up just the right information needed to interpret a text and ignore the mass of irrelevant data. But recent work has begun to create substantially better insight into how people do this, and one interesting fact is that readers do not simply ignore irrelevant information. Consider the sentence: “Thieves broke into the vault of a bank and stole a million dollars.” Do you even consider interpreting “bank” in the sense of “river bank?” No? Sophisticated methods show otherwise. If a person is flashed the word “bank” and then, immediately, “money,” the response to “money” is about 40 milliseconds faster than when an unrelated word (say, “bark”) comes first. This is called a priming effect. In a context like the sentence above, if “bank” is followed quickly by “river,” instead of “money,” the priming effect on recognition is present for this word as well, showing that both possible meanings had been activated. After a 300 millisecond interval, however, priming would occur only for the contextually appropriate meaning (“money”). The results of such laboratory experiments suggest that a rich knowledge of relationships among words may facilitate decoding and perhaps other levels of processing, even though there is no conscious awareness by a reader of how such knowledge is being processed in the course of reading.

A recent goal of language research is to develop a scientific basis for writing comprehensible texts. Previously, the classical view was that texts are less readable when they contain long sentences and use uncommon words. But research shows that revising a difficult text by shortening its sentences and simplifying the vocabulary does not make it substantially (or sometimes even at all) more comprehensible. One idea being studied is that some texts may be hard to read because the reader must repeatedly search in long-term memory for specific information needed to interpret phrases or sentences. Other research focuses on how well the syntactic devices and cues in a text focus the reader’s attention on its major themes or most important information. Related research has shown that successful didactic writing highlights information that the reader needs in order to act or that sets forth specific examples that the reader will most likely have encountered or expect to encounter.

Continued work along these lines should lead to improved and simplified models of how to make written documents more comprehensible, but this is only a proximate goal. Making a text comprehensible for a given reader will certainly contribute to understanding, but it will not necessarily lead to learning. People can comprehend, remember, and summarize a text and still be unable to use the information acquired; learning requires the integration of textual information with previous knowledge. There is much work to be done to discover how people learn, how they use the information acquired from a text in new situations.

• Opportunities and Needs

The results of research on basic processes linking the mind, the brain, and behavior have grown impressively in the recent past and show even more promise in the immediate future. Some of those results have led or soon will lead to valuable applications, such as new types of photography; economical telephone-line transmission of video pictures; better hearing aids; ways to improve normal and impaired memory; enhanced computer abilities to synthesize, decode, and translate natural speech; vastly improved expert systems to aid in such matters as medical diagnosis and car repair; and innovations in teaching students across all areas and levels of learning. But the major contribution of this research is that of sheer knowledge about human beings and other intelligent beings—a wealth of insights into the nature, possibilities, and limits of intelligent individual action. In this section we propose increased expenditures of approximately $61 million annually for this research: for equipment, investigator-initiated grants, new data collection, research centers, pre-doctoral and postdoctoral fellowships, postdoctoral training institutes, and multidisciplinary collaborative activities.

Research in these areas depends heavily on instrumentation for simulating, modeling, and controlling experiments, generating stimuli, recording and analyzing data, and developing new theoretical models, which require substantial amounts of computational power. Some of this equipment is currently only available, if at all, in such special settings as clinics, national laboratories, or expensive commercial centers. Increasingly, there is a call for greater access to powerful workstations and supercomputers. There is also a lively expectation that massively parallel computer architectures will be especially well suited to many behavioral and cognitive research problems. To some extent the computational need is filled by machines available at major research universities, but during the recent period of serious funding cutbacks and stringencies imposed on the behavioral and social sciences and the increased regulatory demands on research involving animals and humans, the overall level of instrumentation in laboratories has fallen seriously short of the research needs. Many formerly up-to-date university laboratories are no longer adequately equipped to do the research that is now possible.

In light of the increasingly advanced and specialized technology required to carry out experiments on behavior, mind, and brain, an estimated $25 million in new funds annually are needed to acquire or provide access to new equipment. We estimate that about one-half of this amount ($12 million) should be allocated to new and upgraded laboratory equipment and service facilities, about one-fourth ($7 million) to new computer hardware and software development, about $4 million to the improvement of animal care for research animals, and about $2 million for access to major neuroimaging devices.

The principal mode of studies on behavior, mind, and brain is carried out by small groups of investigators: one or two principal investigators working on a separately funded project (though each investigator may have more than one project) with one or a few assistants, who may range in level of training, skills, and independence from technicians to postdoctoral scientists. This approach has worked well, and we do not recommend major changes.

Such research by small groups is largely supported by investigator-initiated grants. The aggregated funding level of these grants has been somewhat buffered from roller-coaster trends in behavioral and social sciences spending at the federal level, due partly to the linkages and overlaps with life science and computer science research, which have been on more monotonic funding paths. Cognitive and behavioral sciences support nevertheless has not kept up with the growth in scientific opportunities, such as the rapid shifts in technological capabilities that have resulted from the microprocessor revolution. A rapid increase in funds for investigator-initiated grants—by about $20 million annually—is a high priority. These funds should be used to increase research productivity in three ways. First, they should be used to reverse the lack of change or actual reduction in the size of grants that has occurred at a time when real costs are increasing. Those real cost increases have resulted from improvements in animal care, the need for auxiliary staff in experiments involving infants and children, and the routine costs of supplies and services, among other matters. Second, they should be used to increase the duration of a typical grant to an experienced investigator from 3 to 5 years. Such an increase will sharply reduce the burden of time imposed both on investigators preparing renewal grant proposals and on the individuals asked to read and evaluate each proposal (generally 6, but sometimes as many as 15), which is a nonnegligible cost in research time. Third, they should be used to increase somewhat the total number of grants available because many promising proposals are now being turned down due to lack of funds.

A great deal of the pioneering research lies at the interface of disciplines and often requires a great deal of highly specialized technical expertise in fields ranging from biochemistry and physics to neuroscience, physiology, psychology, linguistics, economics, statistics, mathematics, and computer science. This inherent feature of much current work on behavior, the mind, and the brain calls for a number of developments to foster and facilitate more collaboration among people from very different backgrounds. This facilitation may take different forms, such as short-term visits, new centers of research, interdisciplinary training in the predoctoral curricula of universities, or the growth of postdoctoral programs that broaden rather than intensify the focus of new scientists’ thesis work. We especially note the need for advanced training institutes for younger postdoctoral-level researchers working on newly emerging methods in highly technical specialities and recommend an additional $1 million be spent annually for such institutes. Given the geographical dispersion of investigators, collaboration can often be sustained only if there are opportunities to come together periodically in joint working sessions. Short-term workshops, seminars, and conferences focused on exchanges of current ideas and methods are the ideal medium to encourage this, and we recommend that $1 million be added to current levels of funding for such activities.

The picture of recruitment into graduate schools in the behavioral, cognitive, and brain science disciplines continues to reflect a growth trend. But much of this growth leads toward clinical and other nonresearch employment, and there is growing concern about the number of highly motivated and talented young people who are entering research careers. In particular, there is a relative lack of opportunity for individuals to gain intense experience at the predoctoral or postdoctoral levels in a variety of research methods and theoretical disciplines. This is only in small part a problem of curriculum: it is in large part a problem of funding, which is available more for teaching and clinical or applied work than for research apprenticeships. We therefore recommend that an additional $5 million annually be invested in research fellowships at the predoctoral and postdoctoral levels, with the majority share ($3 million) directed to the postdoctoral level.

There is in certain areas a critical need to inaugurate new data bases that can be accessed by large numbers of investigators; in particular, longitudinal data bases concerning cognitive and educational development. The cost of these new data collection effort would be about $5 million annually.

Finally, there is at present momentum in certain fields toward major new interdisciplinary research enterprises. In the recent climate of restrained budget growth, it has not been possible to organize the sort of stimuli—either in the form of planning grants or competitive announcements for new center programs—that would lead to competitive, full-fledged center proposals. We recommend that there be an initial commitment of $4 million to create two to three new interdisciplinary centers, with a view toward increasing this by as much as threefold if the experience of productivity warrants. We should note that some increment in support staffing in research agencies is a necessary element to generate, evaluate, and monitor good center grants.

• Cite this Page National Research Council; Division of Behavioral and Social Sciences and Education; Commission on Behavioral and Social Sciences and Education; Committee on Basic Research in the Behavioral and Social Sciences; Gerstein DR, Luce RD, Smelser NJ, et al., editors. The Behavioral and Social Sciences: Achievements and Opportunities. Washington (DC): National Academies Press (US); 1988. 1, Behavior, Mind, and Brain.
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Article Contents

Introduction, what is hbe, a systematic overview of current research, hbe: strengths, weaknesses, opportunities, and open questions, supplementary material, human behavioral ecology: current research and future prospects.

Forum editor: Sue Healy

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Daniel Nettle, Mhairi A. Gibson, David W. Lawson, Rebecca Sear, Human behavioral ecology: current research and future prospects, Behavioral Ecology , Volume 24, Issue 5, September-October 2013, Pages 1031–1040, https://doi.org/10.1093/beheco/ars222

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Human behavioral ecology (HBE) is the study of human behavior from an adaptive perspective. It focuses in particular on how human behavior varies with ecological context. Although HBE is a thriving research area, there has not been a major review published in a journal for over a decade, and much has changed in that time. Here, we describe the main features of HBE as a paradigm and review HBE research published since the millennium. We find that the volume of HBE research is growing rapidly, and its composition is changing in terms of topics, study populations, methodology, and disciplinary affiliations of authors. We identify the major strengths of HBE research as its vitality, clear predictions, empirical fruitfulness, broad scope, conceptual coherence, ecological validity, increasing methodological rigor, and topical innovation. Its weaknesses include a relative isolation from the rest of behavioral ecology and evolutionary biology and a somewhat limited current topic base. As HBE continues to grow, there is a major opportunity for it to serve as a bridge between the natural and social sciences and help unify disparate disciplinary approaches to human behavior. HBE also faces a number of open questions, such as how understanding of proximate mechanisms is to be integrated with behavioral ecology’s traditional focus on optimal behavioral strategies, and the causes and extent of maladaptive behavior in humans.

Very soon after behavioral ecology (henceforth BE) emerged as a paradigm in the late 1960s and early 1970s, a tradition of applying behavioral ecological models to human behavior developed. This tradition, henceforth human behavioral ecology (HBE), quickly became an important voice in the human-related sciences, just as BE itself was becoming an established and recognized approach in biology more generally. HBE continues to be an active and innovative area of research. However, it tends not to receive the attention it might, perhaps in part because its adherents are dispersed across a number of different academic disciplines, spanning the life and social sciences. Although there were a number of influential earlier reviews, particularly by Cronk (1991) and Winterhalder and Smith (2000) , there has not been a major review of the HBE literature published in a journal for more than a decade. In this paper, we undertake such a review, with the aim of briefly but systematically characterizing current research activity in HBE, and drawing attention to prospects and issues for the future. The structure of our paper is as follows. In the section “What is HBE?”, we provide a brief overview of the HBE approach to human behavior. The section “A systematic overview of current research” presents our review methodology and briefly describes what we found. We argue that the HBE research published in the period since 2000 represents a distinct phase in the paradigm’s development, with a number of novel trends that require comment. Finally, the section “HBE: strengths, weaknesses, opportunities, and open questions” presents our reflections on the current state and future prospects of HBE, which we structure in terms of strengths, weaknesses, opportunities, and open questions.

BE is the investigation of how behavior evolves in relation to ecological conditions ( Davies et al. 2012 ). Empirically, there are 2 arms to this endeavor. One arm is the study of how measurable variation in ecological conditions predicts variation in the behavioral strategies that individuals display, be it at the between-species, between-population, between-individual, or even within-individual level. (Throughout this paper, “ecological conditions” is to be interpreted in its broadest sense, to include the physical and social aspects of the environment, as well as the state of the individual within that environment.). The other arm concerns the fitness consequences of the behavioral strategies that individuals adopt. Because fitness—the number of descendants left by individuals following a strategy at a point many generations in the future—cannot usually be measured within a study, this generally means measuring the consequences of behavioral strategies in some more immediate proxy currency related to fitness, such as survival, mating success, or energetic return. The 2 arms of BE are tightly linked to one another; the fitness consequences of some behavioral strategy will differ according to the prevailing ecological conditions. Moreover, central to BE is the adaptationist stance. That is, we expect to see, in the natural world, organisms whose behavior is close to optimal in terms of maximizing their fitness given the ecological conditions that they face. This expectation is used as a hypothesis-generating engine about which behaviors we will see under which ecological conditions. The justification for the adaptationist stance is the power of natural selection. Selection, other things being equal, favors genes that contribute to the development of individuals who are prone to behaving optimally across the kinds of environments in which they have to live ( Grafen 2006 ). Note that this does not imply that behavioral strategies are under direct genetic control. On the contrary, selection favors various mechanisms for plasticity, such as individual and social learning, exactly because they allow individuals to acquire locally adaptive behavioral strategies over a range of environments ( Scheiner 1993 ; Pigliucci 2005 ), and it is these plastic mechanisms that are often in immediate control of behavioral decisions. However, the capacity for plasticity is ultimately dependent on genotype, and plasticity is deployed in the service of genetic fitness maximization.

BE is also characterized by a typical approach, to which actual exemplars of research projects conform to varying degrees. This approach is to formulate simple a priori models of what the individual would gain, in fitness terms, by doing A rather than B, and using these models to make predictions either about how variation in ecological conditions will affect the prevalence of behaviors A and B, or about what the payoffs to individuals doing A and B will be, in some currency related to fitness. These models are usually characterized by the assumption that there are no important phylogenetic or developmental constraints on the range of strategies that individuals are able to adopt and also by a relative agnosticism about exactly how individuals arrive at particular behavioral strategies (i.e., about questions of proximate mechanism as opposed to ultimate function; Mayr 1961 ; Tinbergen 1963 ). The assumptions of no mechanistic constraints coming from the genetic architecture or the neural mechanisms are known, respectively, as the phenotypic gambit ( Grafen 1984 ) and the behavioral gambit ( Fawcett et al. 2012 ). To paraphrase Krebs and Davies (1981 ), “think of the strategies and let the mechanisms look after themselves.” We return to the issue of the validity of the behavioral gambit in particular in section “Open questions.” However, one of the remarkable features of early research in BE (what Owens 2006 calls “the romantic period of BE”) was just how well the observed behavior of animals of many different species was explained by very simple optimality models based on the gambits.

HBE is the study of human behavior from an adaptive perspective. Humans are remarkable for their ability to adapt to new niches much faster than the time required for genetic change ( Laland and Brown 2006 ; Wells and Stock 2007 ; Nettle 2009b ). HBE has been particularly concerned with explaining this rapid adaptation and diversity, and thus, the concept of adaptive phenotypic plasticity has been even more central to HBE than it is to BE in general. HBE represents a rejection of the notion that fundamentally different explanatory approaches are necessary for the study of human behavior as opposed to that of any other animal. Note that this does not imply that humans have no unique cognitive and behavioral mechanisms. On the contrary, they clearly do. Rather, it implies that the general scientific strategy for explaining behavior instantiated in BE remains similar for the human case: understand the fitness costs and benefits given the ecological context, make predictions based on the hypothesis of fitness maximization, and test them. There is a pleasing cyclicity to the development of HBE. BE showed that microeconomic models based on maximization, which had come from the human discipline of economics, could be used at least as a first approximation to predict the behavior of nonhuman animals. HBE imported these principles, enriched from their sojourn in biology by a focus on fitness as the relevant currency, back to humans again.

The first recognizably HBE papers appeared in the 1970s (e.g., Wilmsen 1973 ; Dyson-Hudson and Smith 1978 ). The pioneers were anthropologists, and to a lesser extent archaeologists. A major focus was on explaining foraging patterns in hunting and gathering populations ( Smith 1983 ), though other topics were also represented from the outset ( Cronk 1991 ). The focus on foragers was due to the evolutionary antiquity of this mode of subsistence, as well as these being the populations in which optimal foraging theory was most straightforwardly applicable. However, there is no reason in principle for HBE research to be restricted to such populations. The emphasis in HBE is on human adaptability; humans have mechanisms of adaptive learning and plasticity by virtue of which they can rapidly find adaptive solutions to living in many kinds of environments. Thus, we might expect their behavior to be adaptively patterned in societies of all kinds, not just the types of human society, which have existed for many millennia.

The first phase of HBE lasted through the 1980s ( Borgerhoff Mulder 1988 ). In the second phase, the 1990s, HBE grew rapidly, with Winterhalder and Smith (2000) estimating that there were nearly 300 studies published during the decade. Its focus broadened to encompass more studies from nonforaging subsistence populations, such as horticulturalists and pastoralists (e.g., Borgerhoff Mulder 1990 ), and the use of historical demographic data (e.g., Voland 2000 ; Clarke and Low 2001 ). There were also some pioneering forays into the BE of industrialized populations ( Kaplan 1996 ; Wilson and Daly 1997 ). The 1990s were characterized by an increasing emphasis on topics which fall under the general headings of distribution (cooperation and social structure) and particularly reproduction (mate choice, mating systems, reproductive decisions, parental investment), rather than production (foraging). Anthropologists continued to dominate HBE, and the methodologies of the studies reflect this: many of the studies represented the field observations of a single field researcher from a single population, usually a single site. Having briefly outlined what HBE is and where it came from, we now turn to reviewing the HBE research that has appeared in the years since the publication of Winterhalder and Smith (2000) .

Our objective was to ascertain what empirical research has been done within the HBE paradigm since 2000, and characterize its key features, quantitatively where possible. We thus conducted a systematic search of 17 key journals for papers published between the beginning of 2000 and late 2011, which clearly belong in the HBE tradition (see Supplementary material for full methodology). This involved some contentious decisions about how to draw the boundaries of HBE and in the end, we drew it narrowly, including only papers containing quantitative data on naturally occurring behavior in human populations and employing a clearly adaptive perspective. This excludes a large number of studies that take an adaptive perspective but measure hypothetical preferences or decisions in experimental scenarios. It also excludes many studies that focus on nonbehavioral traits such as stature or physical maturation. The sample is not exhaustive even of our chosen subset of HBE, given that some HBE research is published in edited volumes, books, or journals other than those we searched. However, we feel that our strategy provides a good transect through current research, which is prototypically HBE, and the sampling method is at least repeatable and self-consistent over time.

We used the full text of the papers identified to code a number of key variables relevant to our review, including year of publication, journal, first author country of affiliation, and first author academic discipline. We also adopted Winterhalder and Smith’s (2000) ternary classification of topics into production (foraging and other productive activity), distribution (resource sharing, cooperation, social structure), and reproduction (mate choice decisions, sexual selection, life-history decisions, parental and alloparental investment). Finally, we coded the presence of some key features we wished to examine: the presence of any data from foraging populations, the presence of any data from industrialized populations, the use of secondary data, and the use of comparative data from more than one population.

The search resulted in a database of 369 papers (see Supplementary material for reference list and formal statistical analysis; an endnote library of the references of the papers in the database is also available from the corresponding author). The distribution of papers across journals is shown in Table 1 , which also shows the median year of publication of a paper in that journal. The overall median year of publication for the full sample was 2007; thus, the table can be used to identify those journals that carried HBE papers disproportionately earlier in the study interval (e.g., American Anthropologist , median 2004), and those which carried them disproportionately more recently (e.g., American Journal of Human Biology , median 2009). The total number of papers found per year increased significantly over the 12 years sampled, from around 20 at the beginning to nearly 50 in 2011 ( Figure 1a ; regression analysis suggests an average increase of 2.4 papers per year). In the Supplementary material , we show that HBE papers also increased as a proportion of all papers published in our target journals. First authors were affiliated with institutions in 28 different countries, with 57.5% based in the United States and 20.1% in the United Kingdom. In terms of discipline, anthropology (including archaeology) was strongly represented (49.9% of papers), followed by psychology (19.5%) and biology (12.7%). The remaining papers came from demography (3.3%), medicine and public health (3.0%), sociology and social policy (2.4%), economics and political science (2.2%), or were for various reasons unclassifiable (7.0%). However, the growth in number of papers over time was due to increasing HBE activity outside anthropology ( Figure 1a ). In 2000–2003, 64.0% of papers were from anthropology departments, whereas by 2009–2011, this figure was 47.4%. Our search strategy may, if anything, have underestimated the growth in HBE research from outside anthropology, because our search strategy was based on the journals that had carried important BE or HBE research prior to 2000 and did not include any specialist journals from disciplines such as demography or public health.

Numbers and percentages of papers in the database by journal. Also shown is the median year of publication of an HBE paper in the sample in that journal

a Formerly Journal of Cultural and Evolutionary Psychology .

b Targeted search only; for all other journals, all abstracts read.

Number of published papers identified by year over the study period (a) by disciplinary affiliation of first author; (b) by type of study population (other = agriculturalist, pastoralist, horticulturalist, or multiple types); (c) by tripartite classification of topic.

In terms of type of population studied, 80 papers (21.7%) contained some data from foragers, broadly defined to include any subsistence population for whom foraging forms a substantial part of the diet. One hundred and forty-five papers (39.3%) contained data from industrialized populations. The remainder of papers studied either contemporary or historical agricultural, horticultural, and pastoral populations. As Figure 1b shows, the amount of work on industrialized populations has tended to increase over time, with 22 such papers in 2000–2002 (29.3% of total) and 58 in 2009–2011 (43.0%). By contrast, the amount of work on forager populations is much more stable (20 papers [26.7%] in 2000–2002, 27 papers [20.0%] in 2009–2011). As for topic, we classified 64.8% of our papers as concerning reproduction, with 9.5% concerning production and 13.3% distribution. The remaining 12.5% either spanned several topics or fit none of the 3 categories. Table 2 gives some examples of popular research questions addressed in each of the 3 topic areas. The preponderance of reproduction has increased over time ( Figure 1c ); in 2000–2002, 53.3% of the papers fell into this category, whereas by 2009–2011, it was 68.9%. In fact, the growth of HBE papers during the study period has been completely driven by an increase in papers on reproductive topics (see Supplementary material ). We classified papers according to whether they involved analysis of secondary data sets gathered for other purposes. The number of papers involving such secondary analysis increased sharply through the study period, whereas those involving primary data did not (see Supplementary material ). Comparative analyses also increased significantly over time, but not faster than the overall growth in paper numbers.

Some examples of popular research questions in our database of recent HBE papers

To summarize, the data suggest that HBE has changed measurably in the period since 2000. Some of the changes in this period represent continuations of trends already incipient before, such as the expansion away from foraging and foragers toward reproduction and other types of population ( Winterhalder and Smith 2000 ). Our analysis suggests that it is primarily research into the BE of industrialized societies, which has expanded in the subsequent years, such that over 40% of HBE research published in the most recent 3-year period was conducted on such populations. More “traditional” HBE studies of foraging and small-scale food producing societies have continued, but only at a modestly increased rate compared with the 1990s. An unexpected feature of HBE post-2000 is the expansion of HBE in disciplines outside anthropology. Much of the growth has come from the adoption of HBE ideas by researchers based in departments of psychology, and, to a modest extent, other social sciences such as demography, public health, economics, and sociology. This is concomitant with the increasing focus on large-scale industrialized societies, as well as changes in methodology. Anthropologists often work alone or in small teams to gather special-purpose, opportunistic data sets from a particular field site, and many of the pioneering HBE studies were done in this way. In demography, public health, and sociology, by contrast, research tends to be based on very large, systematically collected, representative data sets, such as censuses, cohort, and panel studies, which are designed with multiple purposes in mind. Particular researchers can then interrogate them secondarily to address their particular questions. As HBE has welcomed more researchers from these other social sciences, it has also adopted these secondary methods more strongly (see section “Strengths” for further discussion). We also note the increase in the number of comparative studies. Comparative methods (albeit usually comparing related species rather than populations of the same species) have been a strong feature of BE since the outset (or before, Cullen 1957 ), and thus this is a natural development for HBE. HBE comparative studies use existing cross-cultural databases ( Quinlan 2007 ), integrate multiple ethnographic or historical sources ( Brown et al. 2009 ), or, increasingly, coordinate researchers to collect or derive standardized measures across multiple populations ( Walker et al. 2006 ; Borgerhoff Mulder et al. 2009 ). Comparative studies have become more powerful in their analytical strategies (see section “Strengths”).

The literature review in section “A systematic overview of current research” allowed us to characterize current HBE research and show some of the ways it has changed in the last decade. In this section, we discuss what we see as the strengths, weaknesses, opportunities, and open questions for HBE as a paradigm. This is inevitably more of a personal assessment than the preceding sections, and we appreciate that not everyone in the field will share our views.

The first obvious strength of HBE is vitality . As Darwinians, it comes naturally to us to assume that something that is increasing in frequency has some beneficial features. Thus, the fact that the number of recognizably HBE papers per year found by our search strategy has doubled in a decade, and that there are more and more adopters outside of anthropology, indicates that a range of people find an HBE approach useful. Where does this utility spring from? In part, it is that HBE models tend to make very clear, a priori predictions motivated by theory. The same cannot be said of all other approaches in the human sciences, and, arguably, the more we complicate behavioral ecological models by including details about how proximate mechanisms work, the more this clarity tends to disappear. We return in section “Open questions” to the issue of whether agnosticism about mechanism can be justified, but we note here that a great strength of (and defense for) simple HBE models is that they so often turn out to be empirically fruitful, despite their simplicity. Whether we are considering when to have a first baby ( Nettle 2011 ), what the effects of having an extra child will be in different ecologies ( Lawson and Mace 2011 ), whether to marry polygynously, polyandrously, or monogamously ( Fortunato and Archetti 2010 ; Starkweather and Hames 2012 ), or which relatives to invest time and resources in ( Fox et al. 2010 ), predictions using simple behavioral ecological principles turn out to be useful in making sense of empirically observed diversity in behavior. HBE has also demonstrated the generality of certain principles, such as the fact that male culturally defined social success is positively associated with reproductive success in many different types of society, albeit that the slope of the relationship differs according to features of the social system ( Irons 1979 ; Kaplan and Hill 1985 ; Borgerhoff Mulder 1987 ; Hopcroft 2006 ; Fieder and Huber 2007 ; Nettle and Pollet 2008 ).

A related strength of HBE is its broad scope . HBE models can apply to many kinds of behavioral decision (in principle, all kinds) and in all kinds of society. It is relatively rare in the human sciences for the same set of predictive principles to apply to variation both within and between societies and to societies ranging from small-scale subsistence populations to large-scale industrial states, but HBE thinking about, for example, reproductive decisions has exactly this scope ( Nettle 2011 ; Sear and Coall 2011 ). This would be a strength indeed, even without the crucial additional feature that the explanatory principles invoked are closely related to those that can be applied to species other than our own. Thus, HBE brings a relative conceptual coherence to the study of human behavior, a study that has traditionally been spread across a number of different disciplines each with different conceptual starting points.

Another strength of HBE as we have defined it here is its relatively high ecological validity . Much psychological research into human behavior relies on hypothetical self-reports and self-descriptions, or contrived experimental situations ( Baumeister et al. 2007 ), and much of behavioral economics consists of artificial games whose relevance to actual allocation decisions outwith the laboratory has been questioned ( Levitt and List 2007 ; Bardsley 2008 ; Gurven and Winking 2008 ). Although human behavioral ecologists use such techniques as their purposes require, at the heart of HBE is still a commitment to looking at what people really do, in the environments in which they really live, as a central component of the endeavor. Furthermore, HBE’s focus on behavioral diversity means that it has studied a much wider range of populations than other approaches in the human sciences (see Henrich et al. 2010 ), and this has led to a healthy skepticism of simple generalizations about human universal preferences or motivations ( Brown et al. 2009 ). Measuring relationships between behavior and fitness-relevant outcomes across a broad range of environments, HBE has now amassed considerable evidence in favor of its core assumptions that context matters when studying the adaptive consequences of human behavior and that behavioral diversity arises because the payoffs to alternative behavioral strategies are ecologically contingent.

HBE is also characterized by increasing methodological rigor. The early phases of HBE were defined by exciting theoretical developments, as evolutionary hypotheses for human behavioral variation were first formulated and presented in the literature. However, conducting empirical studies capable of rigorously testing hypotheses derived from HBE theory presents a number of methodological challenges, not least because the human species is relatively long lived and rarely amenable to experimental manipulation. These challenges are now being increasingly overcome, as HBE expands its tool kit to include new sources of data, statistical methods, and study designs. As noted in the section “A systematic overview of current research,” recent years have witnessed an increased use of secondary demographic and social survey data sets, which often provide larger, more representative samples and a broader range of variables than afforded by field research. Some sources of secondary data have also enabled lineages to be tracked beyond the life span of any individual researcher, providing valuable new data on the correlates of long-term fitness (e.g., Lahdenpera et al. 2004 ; Goodman and Koupil 2009 ).

Statistical methods have also become more advanced. Multilevel analyses are now routinely used in HBE research to deal with hierarchically structured data and accurately partition sources of behavioral variance at different levels (e.g., within and between villages; Lamba and Mace 2011 ). Phylogenetic comparative methods, which utilize information on historical relationships between populations, have become popular for testing coevolutionary hypotheses since they were first applied to human populations in the early 1990s ( Mace and Pagel 1994 ; Mace and Holden 2005 ), though debate remains about their suitability for modeling behavioral transmission in humans ( Borgerhoff Mulder et al. 2006 ). Issues of causal inference are also being addressed with more sophisticated analytical techniques. For example, structural equation modeling and longitudinal methods such as event history analysis have enabled researchers to achieve greater confidence when controlling for potential cofounding relationships (e.g., Sear et al. 2002 ; Lawson and Mace 2009 ; Nettle et al. 2011 ). HBE researchers are also following wider trends in the social and natural sciences by exploring alternatives to classic significance testing, such as information-theoretic and Bayesian approaches for considering competing hypotheses ( Towner and Luttbeg 2007 ). Some researchers have also been able to harness “natural experiments” in situations where comparable populations or individuals are selectively exposed to socioecological change. For example, Gibson and Gurmu (2011) examined the effect of changes in land tenure (from family inheritance to government redistribution) on a population in rural Ethiopia, demonstrating that competition between siblings for marital and reproductive success only occurs when land is inherited across generations. These advancements represent an exciting and necessary step forward, as empirical methods “catch up” with the powerful theoretical framework set out in the early days of HBE.

Finally, HBE has shown itself capable of topical innovation. A pertinent recent example is cooperative breeding (typically loosely defined in HBE as the system whereby women receive help from other individuals in raising their offspring). The idea that human females might breed cooperatively had been around for several decades ( Williams 1957 ), and began to be tested empirically in the late 1980s and 1990s (e.g., Hill and Hurtado 1991 ), but it was the 21st century that saw a real upsurge in interest in this topic, leading to a revitalization of the study of kinship in humans ( Shenk and Mattison 2011 ). HBE has now mined many of the rich demographic databases available for our species to test empirically the hypothesis that the presence of other kin members is associated with reproductive outcomes such as child survival rates and fertility rates. These analyses typically find support for the hypothesis that women adopt a flexible cooperative breeding strategy where they corral help variously from the fathers of their children, other men, and pre- and postreproductive women ( Hrdy 2009 ).

Though we see HBE as a strong paradigm, there are some important weaknesses of its current research to be noted. The first is HBE’s relative isolation from the rest of BE. The core journals of BE are Behavioral Ecology and Behavioral Ecology and Sociobiology . Our search revealed only 8 HBE papers in these journals (2.2% of the sample). The vast majority of papers in our sample appeared in journals which never carry studies of species other than humans, and we know of rather few human behavioral ecologists who also work on other systems. West et al. (2011) have recently argued that evolutionary concepts are widely misapplied (or outdated understandings are applied, a phenomenon colloquially dubbed “the disco problem”) in human research, due to insufficient active integration between HBE and the rest of evolutionary biology.

HBE is clearly not completely decoupled from the rest of BE (see Machery and Cohen 2012 for quantitative evidence on this point). For example, within BE, there has been a decline in interest in foraging theory and a rise in interest in sexual selection ( Owens 2006 ), which are mirrored in the changes in HBE described in section “A systematic overview of current research.” Behavioral ecologists have also become less concerned with simply showing that animals make adaptive decisions, and more concerned with the nature of the neurobiological and genetic mechanisms underlying this ( Owens 2006 ). Parallel developments have occurred in the human literature, with the rise of adaptive studies of psychological mechanisms (see e.g., Buss 1995 ). Our search strategy did not include these studies, because their methodologies are different from those of “classical” HBE, but there is no doubt that they have increased in number. Finally, we note that there has been a recent increase in interest in measuring natural selection directly in contemporary human populations ( Nettle and Pollet 2008 ; Byars et al. 2010 ; Stearns et al. 2010 ; Milot et al. 2011 ; Courtiol et al. 2012 ). This anchors HBE much more strongly to evolutionary biology in general. Despite these developments, we see the isolation of HBE from the rest of biology as a potential risk. We hope to see more behavioral ecologists start to work on humans, and more projects across taxonomic boundaries, in the future.

Finally, we note the rather restricted topic base. HBE has had a great deal to say recently about mating strategies, reproductive decisions, fertility, and reproductive success, but much less about diet, resource extraction, resource storage, navigation, spatial patterns of habitat use, hygiene, social coordination, or the many other elements involved in staying alive. In part, this is because, as HBE expands to focus more on large-scale populations, it discovers that there are already disciplines (economics, sociology, human geography, public health) that deal extensively with these topics. It is in the general area of reproduction that it is easiest to come up with predictions that are obviously Darwinian and differentiate HBE from existing social science approaches. Nonetheless, the explanatory strategy of HBE is of potential use for any topic where behavioral effort has to be allocated in one way rather than another, and thus we would hope to see a broadening of the range of questions addressed as HBE continues to grow.

Opportunities

As HBE continues to expand, we see a major opportunity for HBE to build bridges to the social sciences. At the moment, most HBE papers are published in journals that only carry papers that take an adaptive evolutionary perspective, not general social science journals. Thus, HBE is possibly as separated from other approaches to human behavior as it is from parallel approaches to the behavior of other species. This may be because early proponents of HBE saw it as radically different from existing social science approaches to the same problems, by virtue of its generalizing hypothetico-deductive framework and commitment to quantitative hypothesis testing ( Winterhalder and Smith 2000 ). However, the social science those authors came into closest contact with was sociocultural anthropology, which is perhaps not a very typical social science (see Irons 2000 for an account of the hostile reception of HBE within sociocultural anthropology). As HBE’s expansion brings it into closer proximity with disciplines like economics, sociology, demography, public health, development studies, and political science, there may be more common ground than was previously thought. Social scientists are united in the notion that human behavior is very variable and that context is extremely important in giving rise to this variation. These are commitments that HBE obviously shares. Indeed, although it is still common in the human sciences for authors to rhetorically oppose “evolutionary” to “nonevolutionary” (or “social” and “biological”) explanations of the same problem as if these were mutually exclusive endeavors ( Nettle 2009a ), HBE defies such dichotomies adeptly.

Much of social science is highly quantitative and, generally lacking the ability to perform true experiments, relies on multivariate statistical approaches applied to observational data sets to test between competing explanations for behavior patterns. HBE is just the same, and indeed, since the millennium, has become much more closely allied to other social sciences, adopting the large-scale data resources they provide, as well as methodological tools like multilevel modeling, which they have developed to deal with these. HBE employs a priori models based on the individual as maximizer, a position not shared explicitly by all social sciences. However, this approach is widespread in economics and political science. Indeed, it was economics that gave it to BE. The big difference between HBE and much of social science is the explicit invocation of inclusive fitness (or its proxies) as the end to which behavior is deployed. This does not necessarily make it a competing endeavor, especially because what is measured in HBE is not usually fitness itself, but more immediate proxies. Rather, HBE models can often be seen as adding an explicitly ultimate layer of explanation, giving rise to new predictions and unifying diverse empirical observations, without being incompatible with existing, more proximate theories.

Indeed, our perception is that a number of social science theories make assumptions about the ends of behavior, which are quite similar to those of HBE, just not explicitly expressed in Darwinian terms; basically, people’s sets of choices are constrained by the environment in which they have to live, and they make the best choices they can given these constraints, often with knock-on effects that behavioral ecologists would describe as trade-offs. Examples include the work of Geronimus on how African American women adjust their patterns of childbearing to the prevailing rates of mortality and morbidity in their neighborhoods ( Geronimus et al. 1999 ), the work of Drewnowski and colleagues on how people adjust the type of foodstuffs they consume to the budgets they have to spend ( Drewnowski and Specter 2004 ; Drewnowski et al. 2007 ), or Downey’s work on the effects of increasing family size on socioeconomic outcomes of the children ( Downey 2001 ). If the introductory sections of any of these papers were written from a more explicitly Darwinian perspective, they would look perfectly at home in a BE journal. The breaking down of the social science–natural science divide has long been held as desirable, but is not easy to achieve in practice. HBE’s boundary with the social sciences may be one frontier where some progress can occur. Social scientists have long lamented the fragmentation of their field into multiple disciplinary areas with little common ground (e.g., Davis 1994 ). Given HBE’s broad scope and general principles, it has the potential to serve as something of a lingua franca across social scientists working on different kinds of problems.

A related opportunity for HBE is the potential for applied impact . HBE models have the potential to provide new and practical insights into contemporary world issues, from natural resource management ( Tucker 2007 ) to the consequences of inequality within developed populations ( Nettle 2010 ). The causes and consequences of recent human behavioral and environmental changes (including urbanization, economic development, and population growth) are recurring themes in recent studies in HBE. The utility of an ecological approach is clearly demonstrated in studies exploring the effectiveness of public policies or intervention schemes seeking to change human behavior or environments. HBE models clarify that human behavior tends to be deployed in the service of reproductive success, not financial prudence, health, personal or societal wellbeing ( Hill 1993 ), an important insight that differs from some economic or psychological theories. By providing insights into ultimate motivations and proximate pathways to human behavioral change, HBE studies can sometimes offer direct recommendations for the design and implementation of future initiatives ( Gibson and Mace 2006 ; Shenk 2007 ; Gibson and Gurmu 2011 ). Addressing contemporary world issues does, however, present methodological and theoretical challenges for HBE, requiring more explicit consideration of how research insights may be translated into interventions and communicated to policymakers and users ( Tucker and Taylor 2007 ).

Open questions

An open question for HBE is how the study of mechanism can be integrated into functional enquiry. This is an issue for BE generally, not just the human case. As mentioned in the section “What is HBE?”, BE has tended to proceed by the behavioral gambit—the assumption that the nature of the proximate mechanisms underlying behavioral decisions is not important in theorizing about the functions of behavior. It is important to understand the status of the behavioral gambit because it has sometimes been unfairly criticized (see Parker and Maynard Smith 1990 ). In the natural world, individuals do not always behave optimally with respect to any particular decision because there are phylogenetic or mechanistic constraints on their ability to reach adaptive solutions. However, in general terms, the only way to discover the existence of such departures from optimality is to have a theoretical model that shows what the optimal behavior would be and to test empirically whether individual behavior shows the predicted pattern. Where it does not, this may point to unappreciated constraints or trade-offs and thus shed light on the biology of the organism under study. Thus, the use of the term gambit is entirely apt; the behavioral gambit is a way of opening the enquiry designed to gain some advantage in the quest to understand. It is not the end game.

Where there is no sizable departure from predicted optimality, the ultimate adaptive explanation does not depend critically on understanding the mechanisms. This does not mean the question of mechanism is unimportant, of course; mechanistic explanations must still be sought and integrated with functional ones. This is beginning to occur in some cases. In the field of human reproductive ecology, the physiological mechanisms involved in adaptive strategies are beginning to be understood ( Kuzawa et al. 2009 ; Flinn et al. 2011 ), and there is also increasing interchange between HBE researchers and experimentalists studying psychological mechanisms ( Sear et al. 2007 ), which is clearly a development to be welcomed.

Where there is a patterned departure from optimality, understanding the mechanism becomes more critical. Aspects of mechanism can then be modeled as additional constraints, which may explain the strategies individuals pursue. For example, Kacelnik and Bateson (1996) showed that the pattern of risk aversion for variability in food amount and risk proneness for variability in food delay is not predicted by optimal foraging theory, except when Weber’s law (the principle that perceptions of stimulus magnitude are logarithmically, not linearly, related to actual stimulus magnitude) is incorporated into models as a mechanistic constraint. At a deeper level, though, this just raises further questions. Why should Weber’s law have evolved, and once it has evolved, can selection relax it for any particular task? These are what McNamara and Houston call “evo-mecho” questions ( McNamara and Houston 2009 ). Departures from optimality in one particular context raise such questions pervasively. Issues such as the robustness, neural instantiability, efficiency, and developmental cost of different kinds of mechanisms become salient here, and many apparently irrational quirks of behavior become interpretable as side effects of evolved mechanisms whose overall benefits have exceeded their costs over evolutionary time ( Fawcett et al. 2012 ). However, we would still argue that the best first approximation in understanding a question is to employ the behavioral gambit to generate and test simple optimality predictions, even though an understanding of mechanism will be essential for explaining why these may fail.

Although the issue of how incorporation of mechanism changes the predictions of BE models is a general one, in the human case, it has been discussed in particular with reference to transmitted culture because this is a class of mechanism on which humans are reliant to a unique extent ( Richerson and Boyd 2005 ). Transmitted culture refers to the behavioral traditions that arise from repeated social learning. Social learning can be an evolutionarily adaptive strategy, and the equilibrium solutions reached by it will often be the fitness-maximizing ones under reasonable assumptions ( Henrich and McElreath 2003 ). After all, if reliance on culture on average led to maladaptive outcomes, there would be strong selection on humans to rely on it less. Indeed, there is evidence that humans tend to forage efficiently for socially acquired information, using it when it is adaptive to do so ( Morgan et al. 2012 ). Thus, we would argue that culture can be treated, to a first approximation, just like any other proximate mechanism: that is, it can be set aside in the initial formulation of functional explanations ( Scott-Phillips et al. 2011 , though see Laland et al. 2011 for a different view). As an example, we could take Henrich and Henrich’s (2010) data on food taboos for pregnant and lactating women in Fiji. These authors show that the taboos reduce women’s chances of fish poisoning by 30% during pregnancy and 60% during breastfeeding and thus are plausibly adaptive. The fact that in this case it is culture by which women acquire them, rather than genes or individual learning, does not affect this conclusion or the data needed to test it. However, the quirks of how human social learning works may well explain some nonadaptive taboos that are found alongside the adaptive ones, which are in effect carried along by the generally adaptive reliance on social learning. Thus, although the behavioral gambit can be used to explain the major adaptive features of these taboos, an understanding of the cultural mechanisms is required to explain the details of how the observed behavior departs in subtle ways from the optimal pattern. Culture may often lead to maladaptive side effects in this way ( Richerson and Boyd 2005 ). Although its general effect is to allow humans to rapidly reach adaptive equilibria, nonadaptive traits can be carried along by it, and, compared with other proximate mechanisms, it produces very different dynamics of adaptive change.

A final open question is the extent of human maladaptation. Humans have increased their absolute numbers by orders of magnitude and colonized all major habitats of the planet, so they are clearly adept at finding adaptive solutions to the problem of living. However, there are also some clear cases of quite systematic departures from adaptive behavior. Perhaps most pertinently, the low fertility rate typical of industrial populations still defies a convincing adaptive explanation, despite being a longstanding topic for HBE research (see Borgerhoff Mulder 1998 ; Kaplan et al. 2002 ; Shenk 2009 ). There are patterns in the fertility of modernizing populations, which can be readily understood from an HBE perspective: parents in industrialized populations who have large families suffer a cost to the quality of their offspring, particularly with regard to educational achievement and adult socioeconomic success, so there is a quality–quantity trade-off ( Lawson and Mace 2011 ). Moreover, the reduction in fertility rate is closely associated with improvement in the survival of offspring to breed themselves, so that, as the transition to small families proceeds, the probability of having at least one grandchild may remain roughly constant ( Liu and Lummaa 2011 ). However, despite all this, it remains the case that people in affluent societies could still have many more grandchildren and great-grandchildren by having more children, and yet they do not ( Goodman et al. 2012 ). Any explanation of the demographic transition must, therefore, invoke some kind of maladaptation or mismatch between the conditions under which decision-making mechanisms evolved and those under which they are now operating.

Our review has shown that HBE is a growing and rapidly developing research area. The weaknesses of HBE mostly amount to a need for more research activity, and the unresolved questions, though important, do not in our view undermine HBE’s core strengths of theoretical coherence and empirical utility. HBE is being applied to more questions in more human populations with better methods than ever before. Our hope is that HBE will inspire more behavioral biologists to work on humans, for whom a wealth of data is available, and more social scientists to adopt an adaptive, ecological perspective on their behavioral questions, thus adding a layer of deeper explanations, as well as generating new insights.

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OPINION article

Challenges and opportunities for human behavior research in the coronavirus disease (covid-19) pandemic.

• 1 Department of General Psychology, University of Padova, Padua, Italy
• 2 Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy

The COVID-19 pandemic is a serious public health crisis that is causing major worldwide disruption. So far, the most widely deployed interventions have been non-pharmacological (NPI), such as various forms of social distancing, pervasive use of personal protective equipment (PPE), such as facemasks, shields, or gloves, and hand washing and disinfection of fomites. These measures will very likely continue to be mandated in the medium or even long term until an effective treatment or vaccine is found ( Leung et al., 2020 ). Even beyond that time frame, many of these public health recommendations will have become part of individual lifestyles and hence continue to be observed. Moreover, it is implausible that the disruption caused by COVID-19 will dissipate soon. Analysis of transmission dynamics suggests that the disease could persist into 2025, with prolonged or intermittent social distancing in place until 2022 ( Kissler et al., 2020 ).

Human behavior research will be profoundly impacted beyond the stagnation resulting from the closure of laboratories during government-mandated lockdowns. In this viewpoint article, we argue that disruption provides an important opportunity for accelerating structural reforms already underway to reduce waste in planning, conducting, and reporting research ( Cristea and Naudet, 2019 ). We discuss three aspects relevant to human behavior research: (1) unavoidable, extensive changes in data collection and ensuing untoward consequences; (2) the possibility of shifting research priorities to aspects relevant to the pandemic; (3) recommendations to enhance adaptation to the disruption caused by the pandemic.

Data collection is very unlikely to return to the “old” normal for the foreseeable future. For example, neuroimaging studies usually involve placing participants in the confined space of a magnetic resonance imaging scanner. Studies measuring stress hormones, electroencephalography, or psychophysiology also involve close contact to collect saliva and blood samples or to place electrodes. Behavioral studies often involve interaction with persons who administer tasks or require that various surfaces and materials be touched. One immediate solution would be conducting “socially distant” experiments, for instance, by keeping a safe distance and making participants and research personnel wear PPE. Though data collection in this way would resemble pre-COVID times, it would come with a range of unintended consequences ( Table 1 ). First, it would significantly augment costs in terms of resources, training of personnel, and time spent preparing experiments. For laboratories or researchers with scarce resources, these costs could amount to a drastic reduction in the experiments performed, with an ensuing decrease in publication output, which might further affect the capacity to attract new funding and retain researchers. Secondly, even with the use of PPE, some participants might be reluctant or anxious to expose themselves to close and unnecessary physical interaction. Participants with particular vulnerabilities, like neuroticism, social anxiety, or obsessive-compulsive traits, might find the trade-off between risks, and gains unacceptable. Thirdly, some research topics (e.g., face processing, imitation, emotional expression, dyadic interaction) or study populations (e.g., autistic spectrum, social anxiety, obsessive-compulsive) would become difficult to study with the current experimental paradigms ( Table 1 ). New paradigms can be developed, but they will need to first be assessed for reliability and validated, which will undoubtedly take time. Finally, generalized use of PPE by participants and personnel could alter the “usual” experimental setting, introducing additional biases, similarly to the experimenter effect ( Rosenthal, 1976 ).

Table 1 . Possible consequences of non-pharmacological interventions for COVID-19 on human behavior research.

Data collection could also adapt by leveraging technology, such as running experiments remotely via available platforms, like for instance Amazon's Mechanical Turk (MTurk), where any task that programmable with standard browser technology can be used ( Crump et al., 2013 ). Templates of already-programmed and easily customizable experimental tasks, such as the Stroop or Balloon Analog Risk Task, are also available on platforms like Pavlovia. Ecological momentary assessment is another feasible option, since it was conceived from the beginning for remote use, with participants logging in to fill in scales or activity journals in a naturalistic environment ( Shiffman et al., 2008 ). Increasingly affordable wearables can be used for collecting physiological data ( Javelot et al., 2014 ). Web-based research was already expanding before the pandemic, and the quality of the data collected in this way is comparable with that of laboratory studies ( Germine et al., 2012 ). Still, there are lingering issues. For instance, for some MTurk experiments, disparities have been evidenced between laboratory and online data collection ( Crump et al., 2013 ). Further clarifications about quality, such as consistency or interpretability ( Abdolkhani et al., 2020 ), are also needed for data collected using wearables.

Beyond updating data collection practices, a significant portion of human behavior research might change course to focus on the effects of the pandemic. For example, the incidence of mental disorders or of negative effects on psychological and physical well-being, particularly across populations of interest (e.g., recovered patients, caregivers, and healthcare workers), are crucial areas of inquiry. Many researchers might feel hard-pressed to not miss out on studying this critical period and embark on hastily planned and conducted studies. Multiplication and fragmentation of efforts are likely, for instance, by conducting highly overlapping surveys in widely accessible and oversampled populations (e.g., university students). Moreover, rushed planning is bound to lead to taking shortcuts and cutting corners in study design and conduct, e.g., skipping pre-registration or even ethical committee approval or using not validated measurement tools, like ad hoc surveys. Surveys using non-probability and convenience samples, especially for social and mental health problems, frequently produce biased and misleading findings, particularly for estimates of prevalence ( Pierce et al., 2020 ). A significant portion of human behavior research that re-oriented itself to study the pandemic could result in to a heap of non-reproducible, unreliable, or overlapping findings.

Human behavior studies could also aim to inform the planning and enforcement of public health responses in the pandemic. Behavioral scientists might focus on finding and testing ways to increase adherence to NPIs or to lessen the negative effects of isolation, particularly in vulnerable groups, e.g., the elderly or the chronically ill and their caretakers. Studies could also attempt to elucidate factors that make individuals uncollaborative with recommendations from public health authorities. Though all of these topics are important, important caveats must be considered. Psychology and neuroscience have been affected by a crisis in reproducibility and credibility, with several established findings proving unreliable and even non-reproducible ( Button et al., 2013 ; Open Science Collaboration, 2015 ). It is crucial to ensure that only robust and reproducible results are applied or even proposed in the context of a serious public health crisis. For instance, the possible influence of psychological factors on susceptibility to infection and potential psychological interventions to address them could be interesting topics. However, the existing literature is marked by inconsistency, heterogeneity, reverse causality, or other biases ( Falagas et al., 2010 ). Even for robust and reproducible findings, translation is doubtful, particularly when these are based on convenience samples or on simplified and largely artificial experimental contexts. For example, the scarcity of medical resources (e.g., N-95 masks, drugs, or ventilators) in a pandemic with its unavoidable ethical conundrum about allocation principles and triage might appeal to moral reasoning researchers. Even assuming, implausibly, that most of the existent research in this area is robust, translation to dramatic real-life situations and highly specialized contexts, such as intensive care, would be difficult and error-prone. Translation might not even be useful, given that comprehensive ethical guidance and decision rules to support medical professionals already exist ( Emanuel et al., 2020 ).

The COVID-19 pandemic and the corresponding global public health response pose significant and lasting difficulties for human behavior research. In many contexts, such as laboratories with limited resources and uncertain funding, challenges will lead to a reduced research output, which might have further domino effects on securing funding and retaining researchers. As a remedy, modifying data collection practices is useful but insufficient. Conversely, adaptation might require the implementation of radical changes—producing less research but of higher quality and more utility ( Cristea and Naudet, 2019 ). To this purpose, we advocate for the acceleration and generalization of proposed structural reforms (i.e., “open science”) in how research is planned, conducted, and reported ( Munafò et al., 2017 ; Cristea and Naudet, 2019 ) and summarize six key recommendations.

First, a definitive move from atomized and fragmented experimental research to large-scale collaboration should be encouraged through incentives from funders and academic institutions alike. In the current status quo, interdisciplinary research has systematically lower odds of being funded ( Bromham et al., 2016 ). Conversely, funders could favor top-down funding on topics of prominent interest and encourage large consortia with international representativity and interdisciplinarity over bottom-up funding for a select number of excellent individual investigators. Second, particularly for research focused on the pandemic, relevant priorities need to be identified before conducting studies. This can be achieved through assessing the concrete needs of the populations targeted (e.g., healthcare workers, families of victims, individuals suffering from isolation, disabilities, pre-existing physical and mental health issues, and the economically vulnerable) and subsequently conducting systematic reviews so as to avoid fragmentation and overlap. To this purpose, journals could require that some reports of primary research also include rapid reviews ( Tricco et al., 2015 ), a simplified form of systematic reviews. For instance, The Lancet journals require a “Research in context” box, which needs to be based on a systematic search. Study formats like Registered Reports, in which a study is accepted in principle after peer review of its rationale and methods ( Hardwicke and Ioannidis, 2018 ), are uniquely suited for this change. Third, methodological rigor and reproducibility in design, conduct, analysis, and reporting should move to the forefront of the human behavior research agenda ( Cristea and Naudet, 2019 ). For example, preregistration of studies ( Nosek et al., 2019 ) in a public repository should be widely employed to support transparent reporting. Registered reports ( Hardwicke and Ioannidis, 2018 ) and study protocols are formats that ensure rigorous evaluation of the experimental design and statistical analysis plan before commencing data collection, thus making sure shortcuts and methodological shortcomings are eliminated. Fourth, data and code sharing, along with the use of publicly available datasets (e.g., 1000 Functional Connectomes Project, Human Connectome Project), should become the norm. These practices allow the use of already-collected data to be maximized, including in terms of assessing reproducibility, conducting re-analyses using different methods, and exploring new hypotheses on large collections of data ( Cristea and Naudet, 2019 ). Fifth, to reduce publication bias, submission of all unpublished studies, the so-called “file drawer,” should be encouraged and supported. Reporting findings in preprints can aid this desideratum, but stronger incentives are necessary to ensure that preprints also transparently and completely report conducted research. The Preprint Review at eLife ( Elife, 2020 ), in which the journal effectively takes into review manuscripts posted on the preprint server BioRxiv, is a promising initiative in this direction. Journals could also create study formats specifically designed for publishing studies that resulted in inconclusive findings, even when caused by procedural issues, e.g., unclear manipulation checks, insufficient stimulus presentation times, or other technical errors. This would both aid transparency and help other researchers better prepare their own experiments. Sixth, peer review of both articles and preprints should be regarded as on par with the production of new research. Platforms like Publons help track reviewing activity, which could be rewarded by funders and academic institutions involved in hiring, promotion, or tenure ( Moher et al., 2018 ). Researchers who manage to publish less during the pandemic could still be compensated for the onerous activity of peer review, to the benefit of the entire community.

Of course, individual researchers cannot implement such sweeping changes on their own, without decisive action from policymakers like funding bodies, academic institutions, and journals. For instance, decisions related to hiring, promotion, or tenure of academics could reward several of the behaviors described, such as complete and transparent publication regardless of the results, availability of data and code, or contributions to peer review ( Moher et al., 2018 ). Academic institutions and funders should acknowledge the slowdown of experimental research during the pandemic and hence accelerate the move toward more “responsible indicators” that would incentivize best publication practices over productivity and citations ( Moher et al., 2018 ). Funders could encourage submissions leveraging existing datasets or developing tools for data re-use, e.g., to track multiple uses of the same dataset. Journals could stimulate data sharing by assigning priority to manuscripts sharing or re-using data and code, like re-analyses, or individual participant data meta-analyses.

Author Contributions

CG and IC contributed equally to this manuscript in terms of its conceivement and preparation. All authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

This work was carried out within the scope of the project “use-inspired basic research”, for which the Department of General Psychology of the University of Padova has been recognized as “Dipartimento di eccellenza” by the Ministry of University and Research.

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Keywords: open science, data sharing, social distancing, preprint, preregistration, coronavirus disease, neuroimaging, experimental psychology

Citation: Gentili C and Cristea IA (2020) Challenges and Opportunities for Human Behavior Research in the Coronavirus Disease (COVID-19) Pandemic. Front. Psychol. 11:1786. doi: 10.3389/fpsyg.2020.01786

Received: 29 April 2020; Accepted: 29 June 2020; Published: 10 July 2020.

Reviewed by:

Copyright © 2020 Gentili and Cristea. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Claudio Gentili, c.gentili@unipd.it

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Human Behavior & Decision-Making

The Power of Noticing: What the Best Leaders See

This book will examine the common failure to notice critical information due to bounded awareness. The book will document a decade of research showing that even successful people fail to notice the absence of critical and readily available information in their environment due to the human tendency to focus on a limited set of information. This work is still in its formative stages, and I welcome comments about how bounded awareness affects you and your organization and how you have created solutions to such problems.

This book will examine the common failure to notice critical information due to bounded awareness. The book will document a decade of research showing that even successful people fail to notice the absence of critical and readily available information in their environment due to the human tendency to focus on a limited set of information. This...

Time, Money, and Morality

Money, a resource that absorbs much daily attention, seems to be present in much unethical behavior thereby suggesting that money itself may corrupt. This research examines a way to offset such potentially deleterious effects—by focusing on time, a resource that tends to receive less attention than money but is equally ubiquitous in our daily lives. Across four experiments, we examine whether shifting focus onto time can salvage individuals' ethicality. We found that implicitly activating the construct of time, rather than money, leads individuals to behave more ethically by cheating less. We further found that priming time reduces cheating by making people reflect on who they are. Implications for the use of time versus money primes in discouraging or promoting dishonesty are discussed.

Money, a resource that absorbs much daily attention, seems to be present in much unethical behavior thereby suggesting that money itself may corrupt. This research examines a way to offset such potentially deleterious effects—by focusing on time, a resource that tends to receive less attention than money but is equally ubiquitous in our daily...

The (Perceived) Meaning of Spontaneous Thoughts

Spontaneous thoughts, the output of a broad category of uncontrolled and inaccessible higher-order mental processes, arise frequently in everyday life. The seeming randomness by which spontaneous thoughts arise might give people good reason to dismiss them as meaningless. We suggest that it is precisely the lack of control over and access to the processes by which they arise that leads people to perceive spontaneous thoughts to reveal meaningful self-insight. Consequently, spontaneous thoughts potently influence judgment. A series of experiments provides evidence supporting two hypotheses. First, we hypothesize that the more a thought is perceived to be spontaneous, the more it is perceived to provide meaningful self-insight. Participants perceived more spontaneous kinds of thought to reveal greater self-insight than more controlled kinds of thought in Study 1 (e.g., intuition versus deliberation), and perceived thoughts with the same content and target to reveal greater self-insight when spontaneously than deliberately generated in Studies 2 and 3 (i.e., childhood memories and impressions formed, respectively). Second, we hypothesize that greater self-insight attributed to thoughts that are (perceived to be) spontaneous leads those thoughts to more potently influence judgment. Participants felt more sexually attracted to an attractive person whom they thought of spontaneously than deliberately in Study 4, and reported their commitment to a current romantic relationship would be more affected by the spontaneous than deliberate recollection of a good or bad experience with their partner in Study 5. Much human thought arises unbidden, spontaneously intruding upon consciousness. The thought and name of a former lover might come to mind during dinner with one's spouse. Or worse, it may be blurted out during an intimate moment. Because no trace of the past lover is present, the thought lacks an apparent cause. In the latter case it almost certainly occurs without intent, given its potential consequences. The seeming randomness of such thoughts might provide reason to dismiss them as the wanderings of a restless mind. We propose that it is precisely the lack of control over and access to the process by which spontaneous thoughts come to mind that leads them to be perceived to reveal special self-insight. Drawing on previous theory and research, we propose that the greater self-insight they are attributed leads spontaneous thoughts to exert a greater impact on attitudes and behavior than similar deliberate thoughts. Compare a wife's thought of a former lover while perusing her yearbook to that same thought during an intimate moment with her husband. In the former case, the reason for the production of that thought is clear ("I thought of him because I looked at his picture while reminiscing about the past"). In the latter case, she lacks both control over the thought and access to its origin. We suggest that its apparent spontaneity should lead her to attribute it special meaning ("Why would I think of him in this moment unless it is important?"), and it should consequently exert a greater influence on her judgment ("I must still have feelings for him"). In this paper, we report a series of five studies examining how the perceived spontaneity of thought influences the extent to which it is believed to yield meaningful self-insight and influences judgment.

Spontaneous thoughts, the output of a broad category of uncontrolled and inaccessible higher-order mental processes, arise frequently in everyday life. The seeming randomness by which spontaneous thoughts arise might give people good reason to dismiss them as meaningless. We suggest that it is precisely the lack of control over and access to the...

Past, Present and Future Research on Multiple Identities: Toward an Intrapersonal Network Approach

Psychologists, sociologists, and philosophers have long recognized that people have multiple identities—based on attributes such as organizational membership, profession, gender, ethnicity, religion, nationality, and family role(s) and that these multiple identities shape people's actions in organizations. The current organizational literature on multiple identities, however, is sparse and scattered and has yet to fully capture this foundational idea. I review and organize the literature on multiple identities into five different theoretical perspectives: social psychological; microsociological; psychodynamic and developmental; critical; and intersectional. I then propose a way to take research on multiple identities forward using an intrapersonal identity network approach. Moving to an identity network approach offers two advantages: first, it enables scholars to consider more than two identities simultaneously, and second, it helps scholars examine relationships among identities in greater detail. This is important because preliminary evidence suggests that multiple identities shape important outcomes in organizations, such as individual stress and well-being, intergroup conflict, performance, and change. By providing a way to investigate patterns of relationships among multiple identities, the identity network approach can help scholars deepen their understanding of the consequences of multiple identities in organizations and spark novel research questions in the organizational literature.

Psychologists, sociologists, and philosophers have long recognized that people have multiple identities—based on attributes such as organizational membership, profession, gender, ethnicity, religion, nationality, and family role(s) and that these multiple identities shape people's actions in organizations. The current organizational literature on...

Cheating More for Less: Upward Social Comparisons Motivate the Poorly Compensated to Cheat

Intuitively, people should cheat more when cheating is more lucrative, but we find that the effect of performance-based pay rates on dishonesty depends on how readily people can compare their pay rate to that of others. In Experiment 1, participants were paid 5 cents or 25 cents per self-reported point in a trivia task, and half were aware that they could have received the alternative pay rate. Lower pay rates increased cheating when the prospect of a higher pay rate was salient. Experiment 2 illustrates that this effect is driven by the ease with which poorly compensated participants can compare their pay to that of others who earn a higher pay rate. Our results suggest that low pay rates are, in and of themselves, unlikely to promote dishonesty. Instead, it is the salience of upward social comparisons that encourages the poorly compensated to cheat.

Intuitively, people should cheat more when cheating is more lucrative, but we find that the effect of performance-based pay rates on dishonesty depends on how readily people can compare their pay rate to that of others. In Experiment 1, participants were paid 5 cents or 25 cents per self-reported point in a trivia task, and half were aware that...

Investors Prefer Entrepreneurial Ventures Pitched by Attractive Men

Entrepreneurship is a central path to job creation, economic growth, and prosperity. In the earliest stages of start-up business creation, the matching of entrepreneurial ventures to investors is critically important. The entrepreneur's business proposition and previous experience are regarded as the main criteria for investment decisions. Our research, however, documents other critical criteria that investors use to make these decisions: the gender and physical attractiveness of the entrepreneurs themselves. Across a field setting (three entrepreneurial pitch competitions in the United States) and two experiments, we identify a profound and consistent gender gap in entrepreneur persuasiveness. Investors prefer pitches presented by male entrepreneurs compared with pitches made by female entrepreneurs, even when the content of the pitch is the same. This effect is moderated by male physical attractiveness: attractive males were particularly persuasive, whereas physical attractiveness did not matter among female entrepreneurs.

Entrepreneurship is a central path to job creation, economic growth, and prosperity. In the earliest stages of start-up business creation, the matching of entrepreneurial ventures to investors is critically important. The entrepreneur's business proposition and previous experience are regarded as the main criteria for investment decisions. Our...

Appetite, Consumption, and Choice in the Human Brain

Although linked, researchers have long distinguished appetitive from consummatory phases of reward processing. Recent improvements in the spatial and temporal resolution of neuroimaging techniques have allowed researchers to separately visualize different stages of reward processing in humans. These techniques have revealed that evolutionarily conserved circuits related to affect generate distinguishable appetitive and consummatory signals, and that these signals can be used to predict choice and subsequent consumption. Review of the literature surprisingly suggests that appetitive rather than consummatory activity may best predict future choice and consumption. These findings imply that distinguishing appetite from consumption may improve predictions of future choice and illuminate neural components that support the process of decision making.

Although linked, researchers have long distinguished appetitive from consummatory phases of reward processing. Recent improvements in the spatial and temporal resolution of neuroimaging techniques have allowed researchers to separately visualize different stages of reward processing in humans. These techniques have revealed that evolutionarily...

Ever since their origins about three decades ago, the Behavioral Science areas of economics, ethics and managerial psychology have been rapidly evolving. In the 1980's and 1990's, early work by Max Bazerman in judgment and negotiation , Matthew Rabin in behavioral economics , and James Sebenius in negotiations was instrumental in shaping research on Human Behavior & Decision-Making. Today, our research focuses on individual and interactive judgment and decision making and explores the role of personal bias, cognition and learning, time, perception, ethics and morality, and emotion.

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Human Behavior Research: The Complete Guide

Bryn Farnsworth

Introduction to Human Behavior Research and Studies

Academic and commercial researchers alike are aiming toward a deeper understanding of how humans act, make decisions, plan, and feel. Advances in wearable sensor technology along with procedures for multi-modal data acquisition and analysis have lately been enabling researchers all across the globe to tap into previously unknown secrets of the human brain and mind.

Still, as emphasized by Makeig and colleagues (2009), the most pivotal challenge lies in the systematic observation and interpretation of how distributed brain processes support our natural, active, and flexibly changing behavior and cognition.

We all are active agents, continuously engaged in attempting to fulfill bodily needs and mental desires within complex and ever-changing surroundings while interacting with our environment. Brain structures have evolved that support cognitive processes targeted towards the optimization of outcomes for any of our body-based behaviors.

In this complete guide to understanding human behavior research, you’ll get a full run-down of how to get started with analyzing the systems, emotions and cognition that make humans tick, using scientifically credible methods such as biosensor research.

N.B. this post is an excerpt from our Human Behavior Guide. You can download your free copy below and get even more insights into human behavior.

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Table of Contents

So what exactly is behavior.

In scientific research, human behavior is a complex interplay of three components: actions, cognition, and emotions.

Sounds complicated? Let’s address them one by one.

Actions are Behavior

An action denotes everything that can be observed, either with bare eyes or measured by physiological sensors. Think of an action as an initiation or transition from one state to another – at a movie set, the director shouts “action” for the next scene to be filmed.

Behavioral actions can take place on various time scales, ranging from muscular activation to sweat gland activity, food consumption, or sleep.

Cognitions are Behavior

Cognitions describe thoughts and mental images you carry with you, and they can be both verbal and nonverbal. “I have to remember to buy groceries,” or “I’d be curious to know what she thinks of me,” can be considered verbal cognitions. In contrast, imagining how your house will look like after remodeling could be considered a nonverbal cognition.

Cognitions comprise skills and knowledge – knowing how to use tools in a meaningful manner (without hurting yourself), sing karaoke songs or being able to memorize the color of Marty McFly’s jacket in “Back to the Future” (it’s red).

Emotions are Behavior

Commonly, an emotion is any relatively brief conscious experience characterized by intense mental activity, and a feeling that is not characterized as resulting from either reasoning or knowledge. This usually exists on a scale, from positive (pleasurable) to negative (unpleasant).

Other aspects of physiology that are indicative of emotional processing – such as increased heart rate or respiration rate caused by increased arousal – are usually hidden to the eye. Similar to cognitions, emotions cannot be observed directly. They can only be inferred indirectly by tracking facial electromyographic activity (fEMG),  analyzing facial expressions , monitoring arousal using ECG, galvanic skin response (GSR) , respiration sensors, or self-reported measures, for example.

What is the Study of Human Behavior?

The study of human behavior is a fascinating and complex field that delves into the myriad ways individuals think, act, and interact. This multidisciplinary approach draws from psychology, sociology, anthropology, and even biology to provide comprehensive insights into human actions and societal dynamics.

Exploring Human Behavior Studies

Human behavior studies strive to understand the ‘why’ behind actions and reactions. This exploration involves examining both innate and learned behaviors, how environmental factors shape actions, and the impact of mental processes on decision-making. Researchers employ various methods, from controlled laboratory experiments to field observations, ensuring a holistic understanding of human behavior in diverse contexts.

Significance of Studying Humans

Understanding human behavior is crucial for numerous reasons. It aids in predicting responses in different situations, which is invaluable in areas like marketing, policy-making, and therapy. Additionally, studying behavior helps address social issues, improve educational approaches, and enhance interpersonal relationships. By understanding the underlying motivations and factors influencing actions, we can foster more empathetic and effective interactions within society.

Applying Insights from Human Behavior Studies

The practical applications of human behavior studies are vast and varied. In healthcare, these insights assist in developing better patient care strategies and public health initiatives. In business, understanding consumer behavior drives marketing and product development. In education, insights into learning patterns lead to more effective teaching methods. Furthermore, in the realm of public policy, this knowledge informs laws and regulations that consider the behavioral tendencies of the populace.

The study of human behavior is not just an academic pursuit but a tool that, when wielded wisely, can significantly enhance the quality of life and societal progress. This field continues to evolve, promising even greater insights and applications in the future.

Everything is Connected

Actions, cognitions and emotions do not run independently of each other – their proper interaction enables you to perceive the world around you, listen to your inner wishes and respond appropriately to people in your surroundings. However, it is hard to tell what exactly is cause and effect – turning your head (action) and seeing a familiar face might cause a sudden burst of joy (emotion) accompanied by an internal realization (cognition):

Action = emotion (joy) + cognition (“hey, there‘s Peter!”)

In other cases, the sequence of cause and effect might be reversed: Because you’re sad (emotion) and ruminating on relationship issues (cognition), you decide to go for a walk to clear your head (action).

Emotion (sadness) + cognition (“I should go for a walk“) = action

Takeaways: What You Should Know…

Humans are active consumers of sensory impressions.

You actively move your body to achieve cognitive goals and desires, or to get into positive (or out of negative) emotional states. In other words: While cognition and emotion cannot be observed directly, they certainly drive the execution of observable action. For example, through moving your body to achieve cognitive goals and desires, or to get into positive (or out of negative) emotional states.

Cognitions are specific to time and situations

The former is important as you have to couple responses dynamically to stimuli, dependent on intentions and instructions. This allows you to respond to one and the same stimulus in near-unlimited ways. Stability, by contrast, is crucial for maintaining lasting stimulus-response relationships, allowing you to respond consistently to similar stimuli.

Imagination and abstract cognition are body-based

Even abstract cognitions (devoid of direct physical interaction with the environment) are body-based. Imagining limb movements triggers the same brain areas involved when actually executing the movements. When you rehearse material in working memory, the same brain structures used for speech perception and production are activated (Wilson, 2001).

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Learning and Behavior

When we talk about behavior, we need to consider how it is acquired. Learning denotes any acquisition process of new skills and knowledge, preferences, attitudes and evaluations, social rules and normative considerations.

You surely have heard of the “nature – nurture” debate – in the past, there has been quite some fighting about whether behavior was solely driven by genetic predispositions (nature) or environmental factors (nurture).

Today, it’s no longer a question of either/or. There simply is too much evidence for the impact of nature and nurture alike – behavior is considered to be established by the interplay of both factors.

Current theoretical frameworks also emphasize the active role of of the agent in acquiring new skills and knowledge. You are able to develop and change yourself through ongoing skill acquisition throughout life, which can have an impact on a neurological level. Think of it as assigning neuroscientific processes to the phrase “practice makes perfect”.

Classical Conditioning

Classical conditioning refers to a learning procedure in which stimulus-response pairings are learned – seeing tasty food typically triggers salivation (yummy!), for example. While food serves as unconditioned stimulus, salivation is the unconditioned response.

Unconditioned stimulus -> unconditioned response

Seeing food -> salivation

If encountering food is consistently accompanied by a (previously) neutral stimulus such as ringing a bell, a new stimulus-response pairing is learned.

unconditioned stimulus + conditioned stimulus -> unconditioned response

seeing food + hearing bell -> salivation

The bell becomes a conditioned stimulus and is potent enough to trigger salivation even in absence of the actual food.

conditioned stimulus – > response

hearing bell -> salivation

Described as generalization, this learning process was first studied by Ivan Pavlov and team (1927) through experiments with dogs, which is why classical conditioning is also referred to as Pavlovian conditioning.

Today, classical conditioning is one of the most widely understood basic learning processes.

Operant Conditioning

Operant conditioning, also called instrumental conditioning, denotes a type of learning in which the strength of a behavior is modified by the consequences (reward or punishment), signaled via the preceding stimuli.

In both operant and classical conditioning behavior is controlled by environmental stimuli – however, they differ in nature. In operant conditioning, behavior is controlled by stimuli which are present when a behavior is rewarded or punished.

Operant conditioning was coined by B.F. Skinner. As a behaviorist, Skinner believed that it was not really necessary to look at internal thoughts and motivations in order to explain behavior. Instead, he suggested to only take external, observable causes of human behavior into consideration.

According to Skinner, actions that are followed by desirable outcomes are more likely to be repeated while those followed by undesirable outcomes are less likely to be repeated. In this regard, operant conditioning relies on a fairly simple premise: Behavior that is followed by reinforcement will be strengthened and is more likely to occur again in the future.

The key concepts of operant conditioning are:

• Positive reinforcement (otherwise known just as reinforcement) occurs when a behavior is rewarding, increasing the frequency of that behavior.
• Negative reinforcement (escape) occurs when a behavior is followed by the removal of an aversive stimulus, increasing the frequency of the behavior.
• Punishment occurs when a behavior is followed by an aversive stimulus, causing a decrease in that behavior.
• Penalty occurs when a behavior is followed by the removal of a rewarding stimulus
• Extinction occurs when a behavior that had previously been reinforced is no longer effective.

These learning theories give guidance for knowing how we gather information about the world. The way in which we learn is both emotionally and physiologically appraised. This will have consequences for how we act, and carry out behaviors in the future – what we attend to, and how it makes us feel.

Decisions and Behavior

While behavior is acquired through learning, whether the acting individual decides to execute an action or withhold a certain behavior is dependent on the associated incentives, benefits and risks (“if Peter was penalized for doing this, I certainly won‘t do it!”).

But which are the factors driving our decisions? Theories such as social learning theory provide a base set of features, but one of the most influential psychological theories about decision-making actually has its origins in an economics journal.

In 1979, Daniel Kahneman & Amos Tversky published a paper proposing a theoretical framework called the Prospect Theory. This laid the foundations for Kahneman’s later thoughts and studies on human behavior, that was summarized in his bestselling book “Thinking, Fast and Slow”.

System 1 and System 2

Kahneman‘s theories were also concerned with how people process information. He proposed that there are two systems that determine how we make decisions: System 1 – which is fast but relatively inaccurate, and system 2 – which is slow but more accurate.

The theory suggests that our everyday decisions are carried out in one of these two ways, from buying our morning coffee, to making career choices. We will use different approaches depending on the circumstances.

Decision-making and Emotions

Human behavior and decision-making are heavily affected by emotions – even in subtle ways that we may not always recognize. After making an emotionally-fueled decision, we tend to continue to use the imperfect reasoning behind it, and “a mild incidental emotion in decision-making can live longer than the emotional experience itself” as pointed out by Andrade & Ariely (2009).

An example of mood manipulation affecting decision making was completed by researchers who wanted to know how a willingness to help could be affected by positive feelings.

To study their question, they placed a Quarter (25ct) clearly visible in a phone booth (yes, these things actually existed!) and waited for passers-by to find the coin. An actor working on behalf of the psychologist stepped in, asking to take an urgent phone call. Study participants who actually found the coin were significantly happier, allowing the confederate to take the call, while those who didn’t find the coin were unaffected, and more likely to say no (Isen & Levin, 1972).

Getting Started with Human Behavior Research

Research on human behavior addresses how and why people behave the way they do. However, as you have seen in the previous sections, human behavior is quite complex as it is influenced, modulated and shaped by multiple factors which are often unrecognized by the individual: Overt or covert, logical or illogical, voluntary or involuntary.

Conscious vs. unconscious behavior

Consciousness is a state of awareness for internal thoughts and feelings as well for proper perception for and uptake of information from your surroundings.

A huge amount of our behaviors are guided by unconscious processes. Just like an iceberg, there is a great amount of hidden information, and only some of it is visible with the naked eye.

Overt vs. covert behavior

Overt behavior describes any aspects of behavior that can be observed, for example body movements or (inter-)actions. Also, physiological processes such as blushing, facial expressions or pupil dilation might be subtle, but can still be obeserved. Covert processes are thoughts (cognition), feelings (emotion) or responses which are not easily seen. Subtle changes in bodily processes, for instance, are hidden to the observer‘s eye.

In this case, bio- or physiological sensors are used to aid the observation with quantitative measures as they uncover processes that are covert in the first place. Along this definition, EEG , MEG, fMRI and fNIRS all monitor physiological processes reflecting covert behavior.

Rational vs. irrational behavior

Rational behavior might be considered any action, emotion or cognition which is pertaining to, influenced or guided by reason. In contrast, irrational behavior describes actions that are not objectively logical.

Patients suffering from phobias often report an awareness for their thoughts and fears being irrational (“I know that the spider can‘t harm me”) – albeit they still cannot resist the urge to behave in a certain way.

Voluntary vs. involuntary behavior

Voluntary actions are self-determined and driven by your desires and decisions. By contrast, involuntary actions describe any action made without intent or carried out despite an attempt to prevent it. In cognitive-behavioral psychotherapy, for example, patients are exposed to problematic scenarios, also referred to as flooding, such as spiders, social exhibition or a transatlantic plane ride.

Many of our behaviors appear to be voluntary, rational, overt, and conscious – yet they only represent the tip of the iceberg for normal human behavior. The majority of our actions are involuntary, potentially irrational, and are guided by our subconscious. The way to access this other side of behavior is to examine the covert behaviors that occur as a result.

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Measuring Human Behavior

In order to describe and interpret human behavior, academic and commercial researchers have developed intricate techniques allowing for the collection of data indicative of personality traits, cognitive-affective states and problem solving strategies.

In experimental setups , specific hypotheses about stimulus-response relationships can be clarified. Generally, research techniques employed by scientists can be classified into qualitative and quantitative procedures.

Qualitative studies  gather non-numerical insights, for example by analyzing diary entries, using open questionnaires, unstructured interviews or observations. Qualitative field / usability studies, for example, aim towards understanding how respondents see the world and why they react in a specific way rather than counting responses and analyzing the data statistically.

Quantitative studies characterize statistical, mathematical or computational techniques using numbers to describe and classify human behavior. Examples for quantitative techniques include structured surveys, tests as well as observations with dedicated coding schemes. Also, physiological measurements from EEG , EMG , ECG , GS R, and other sensors produce quantitative output, allowing researchers to translate behavioral observations into discrete numbers and statistical outputs.

Behavioral Observation

Behavioral observation is one of the oldest tools for psychological research on human behavior. Researchers either visit people in their natural surroundings (field study) or invite individuals or groups to the laboratory.

Observations in the field have several benefits. Participants are typically more relaxed and less self-conscious when observed at home, at school or at the workplace. Everything is familiar to them, permitting relatively unfiltered observation of behavior which is embedded into the natural surroundings of the individual or group of interest.

However, there’s always the risk of distraction – shouting neighbors or phones ringing. Field observations are an ideal starting point of any behavioral research study. Just sitting and watching people offers tremendous amounts of insights if you’re able to focus on a specific question or aspect of behavior.

Observation in the laboratory , by contrast, allows much more experimental control. You can exclude any unwanted aspects and completely ban smart phones, control the room layout and make sure to have everything prepared for optimal recording conditions (correct lighting conditions, ensuring a quiet environment, and so on).

You can create near-realistic laboratory environments – building a typical family living room, office space or creative zone, for example, to make respondents feel at ease and facilitating more natural behavior.

Surveys and Questionnaires

Surveys and questionnaires are an excellent tool to capture self-reported behaviors and skills, mental or emotional states or personality profiles of your respondents. However, questionnaires are always just momentary snapshots and capture only certain aspects of a person’s behavior, thoughts and emotions.

Surveys and questionnaires typically measure what Kahneman would describe as system 2 processes – thoughts that are carried out slowly and deliberately. System 1 processes – thoughts that are fast and automatic – can be measured by other methods that detect quick physiological changes.

Focus groups

In market research, focus groups typically consist of a small number of respondents (about 4–15) brought together with a moderator to focus on beliefs and attitudes towards a product, service, concept, advertisement, idea or packaging. Focus groups are qualitative tools as their goal is to discuss in the group instead of coming to individual conclusions.

What are the benefits of a product, what are the drawbacks, where could it be optimized, who are ideal target populations? All of these questions can be addressed in a focus group.

Beyond Surveys and Focus Groups

While surveys and focus groups can be instrumental in understanding our conscious thoughts and emotions, there is more to human behavior than meets the eye. The subconscious mind determines how our behavior is ultimately carried out, and only a small fraction of that is accessible from traditional methodologies – using surveys and focus groups.

As some researchers have claimed, up to 90% of our actions are guided by the subconscious. While the other 10% is important, it is clear that there is much to gain by probing further than what is tested by traditional methods.

Modern approaches aim to explore the hidden and uncharted territory of the subconscious, by measuring reliable outputs that provide deeper information about what someone is really thinking.

Biosensors for Learning About Human Behaviour

In addition to observing overt behavior, you can use biosensors and measurement devices in order to understand how mind, brain and body interact.

Biosensors give access to otherwise hidden processes. These usually hidden processes (at least to an observer) can give indications about the thought processes that Daniel Kahneman would describe as belonging to System 1 – fast and largely emotionally driven reactions. These reactions are quick processes that underlie a large portion of our decision-making and our resulting behavior.

Eye tracking

offers incredible insights into visual attention above and beyond any other experimental method. While eye tracking is commonly used to monitor where we direct our eye movements at a certain point in time, it also tracks the dilation of the pupil.

Electroencephalography (EEG)

is a neuroimaging technique measuring electrical activity generated by the brain from the scalp surface using sensors (electrodes) and amplifier systems. It is ideal for assessing brain activity associated with perception, cognition, and emotional processes.

Among all biosensors, EEG has the highest time resolution , thereby revealing substantial insights into sub-second brain dynamics of engagement, motivation, frustration, cognitive workload, and further metrics associated with stimulus processing, action preparation, and execution.

functional Near-Infrared Spectroscopy (fNIRS)

fNIRS records the diffusion of near-infrared light by human skull, scalp and brain tissue, allowing researchers to monitor cerebral blood flow in specified brain regions. While fNIRS is a relatively new technology, it has already proven to be a very promising tool in human behavior research.

Magnetic Resonance Imaging (MRI)

Whenever you would like to accomplish brain imaging with excellent spatial resolution, Magnetic Resonance Imaging (MRI) is the method of choice. MRI can be used to generate structural scans of high spatial precision, representing an accurate and highly precise 3D rendering of the respondent’s brain.

For examining dynamic changes in the brain, functional MRI (fMRI) can be used. The scanner uses magnetic fields and radio frequencies to measure changes in oxygenated and de-oxygenated blood flow in specific regions of the brain, that can then be related to cognitive processes.

Electrodermal activity (EDA)

EDA also referred to as galvanic skin response (GSR) , reflects the amount of sweat secretion from sweat glands in our skin. Increased sweating results in higher skin conductivity. When exposed to emotional stimulation, we “sweat emotionally” – particularly on our forehead, hands and feet. Just as pupil dilation, skin conductance is controlled subconsciously, therefore offering tremendous insights into the unfiltered, unbiased emotional arousal of a person.

Facial Expressions

As facial expressions are tied to our inner emotions, and our emotions rule so much of our behavior, studying facial expressions gives an insight into the reasons for our actions .

Facial expression analysis is a non-intrusive method that assesses head position and orientation, micro-expressions (such as lifting of the eyebrows or opening of the mouth) and global facial expressions of basic emotions (joy, anger, surprise, etc.) using a webcam placed in front of the respondent. Facial data is extremely helpful to validate metrics of engagement, workload or drowsiness.

Electromyographic (EMG

Electromyographic (EMG) sensors monitor the electric energy generated by bodily movements of the face, hands or fingers, etc. You can use EMG to monitor muscular responses to any type of stimulus material to extract even subtle activation patterns associated with consciously controlled hand/finger movements (startle reflex). Also, facial EMG can be used to track smiles and frowns in order to infer one’s emotional valence.

Electorcardiography (EEG)

Track heart rate, or pulse, from electrocardiography (ECG) electrodes or optical sensors (Photoplethysmogram; PPG) to get insights into respondents’ physical state, anxiety and stress levels (arousal), and how changes in physiological state relate to their actions and decisions.

How to Put it Together for Human Behavior Psychology

While biosensor and imaging methods present unparalleled access into an individual‘s thoughts, feelings, and emotions, the best way to understand someone in entirety is to complement the measurements with more traditional methods, such as with surveys and focus groups.

By combining the measures, we‘re able to interpret both parts of what Kahneman described as System 1 and System 2 – both fast, emotionally driven decisions, as well as slow and deliberate decisions. Utilizing the insights offered by both routes of investigation gives a whole view of the thoughts and behaviors that an individual possesses.

The grid below summarizes the two methods in an overview, and shows how using both can answer a wide array of questions.

Human Behavior Metrics

Metrics are derived from observation or sensor data and reflect cognitive-affective processes underlying overt and covert actions. Typically, they are extracted using computer-based signal pre-processing techniques and statistics. In the following, we will describe the most important metrics in human behavior research.

Emotional valence

One of the most indicative aspects of emotional processing is your face. Facial expressions can be monitored either using facial electromyography (fEMG) sensors placed on certain facial muscles, or video-based facial expression analysis procedures. A very fine-tuned manual observation technique is the Facial Action Coding System (FACS) primarily designed by Paul Ekman. Trained coders, and sophisticated software, can evaluate the amount of activation of modular Action Units (AU), which represent very brief and subtle facial expressions lasting up to half a second.

Based on the sub-millisecond changes in muscular activation patterns or changes in global facial features (lifting an eyebrow, frowning, lifting up the corners of the mouth), behavioral researchers infer universal emotional states such as joy, anger, surprise, fear, contempt, disgust, sadness or confusion.

Emotional arousal

While facial expressions can provide insights into the general direction of an emotional response (positive – negative), they cannot tell the intensity of the felt emotion as described by means of arousal. Arousal refers to the physiological and psychological state of being responsive to stimuli and is relevant for any kind of regulation of consciousness, attention and information processing.

The human arousal system is considered to comprise several different but heavily interconnected neural systems in the brainstem and cortex.

Physiological arousal and emotional valence can be thought of as taking place on a scale, in which both interact with each other. The intensity of arousal therefore influences the intensity of emotion. Capturing data about both of these processes can provide more information about an individual and their behavior.

Although all of these processes are taking place on the microscopic level and cannot be observed with the eye, arousal can be measured by using several psychphysiological methods such as eye tracking, EEG, GSR, ECG, respiration, and more.

Workload and cognitive load Decisions are often made under several constraints (with respect to time, space and resources), and there is obviously a threshold in how much information you can take into consideration. Working memory represents the cognitive system responsible for transient holding and processing of information, and human cognitive-behavioral research has a particular interest in this aspect due to its crucial role in the decision-making process.

The total amount of mental effort being used in working memory is typically referred to as cognitive load.

Perception and attention Do stimuli “pop out” and elicit our interest? Do we watch a movie clip or an advertisement because it is visually captivating? For cognitive-behavioral scientists it is highly relevant to determine the level of saliency of stimuli , and whether or not it captures our attention. Saliency detection is considered to be a key attentional mechanism that facilitates learning and survival. It enables us to focus our limited perceptual and cognitive resources on the most pertinent subset of the available sensory data.

Motivation and engagement Another metric relevant for cognitive-behavioral scientists is motivation, sometimes referred to as action motivation. It describes the drive for approaching/avoiding actions, objects and stimuli.

Shopping behavior is primarily driven by engagement and the underlying motivation to buy a product, therefore it would be beneficial to infer one’s motivation already during the initial exposure with an item. EEG experiments have provided rich evidence for certain brain activation patterns reflecting increased or decreased motivational states.

Besides EEG, one’s level of attention can be determined based on eye tracking , both in lab settings as well as in real-world environments. Remote eye trackers are mounted in front of a computer or TV screen and record the respondents’ gaze position on screen.

Eye tracking glasses are the optimal choice for monitoring attentional changes in freely moving subjects, allowing you to extract measures of attention in real-world environments such as in-store shopping or package testing scenarios.

Application Fields in Study of Human Behavior Psychology

Consumer neuroscience and neuromarketing.

There is no doubt about it: Evaluating consumer preferences and delivering persuasive communication are critical elements in marketing. While self-reports and questionnaires might be ideal tools to get insights into respondents’ attitudes and awareness, they might be limited in capturing emotional responses unbiased by self-awareness and social desirability.

As only so much of our overt, conscious behavior is captured by traditional methods such as surveys and focus groups, biosensors offer a way to fill that gap.

Psychological research

Psychologists analyze how we respond emotionally towards external and internal stimuli, how we think about ourselves and others, and how we behave. In systematic studies, researchers can measure and vary stimulus properties (color, shape, duration of presentation) and social expectations in order to evaluate how personality characteristics and individual learning histories impact emotional, cognitive and perceptual processing.

Media testing and advertising

In media research, individual respondents or focus groups can be exposed to TV advertisements, trailers and full-length pilots while monitoring their behavioral responses, for example, using facial expression analysis . Identifying scenes where emotional responses were expected but the audience just didn’t “get it” is crucial to refining the appeal of the TV-program. Facial expression analysis can also be used to find the key frames that result in the most extreme facial expressions – showing when the program really landed on target.

Software UI and website design

Ideally, handling software and navigating websites should be a pleasant experience – frustration and confusion levels should certainly be kept as low as possible. Monitoring user behavior, for example based on scrolling or click-ratio as well as facial expressions, while testers browse websites or software dialogs can provide insights into the emotional satisfaction of the desired target group.

Eye tracking is a particularly useful technology, as it helps pinpoint exactly what the person is looking at during their experience with the website. When combined with other measures, it gives an insight into what exactly gave them a positive or negative feeling during the interaction.

Human behavior is a multi-faceted and dynamic field of study, requiring many points of interrogation to yield insights. Learning processes lay the foundation for determining many of our behaviors, although we are constantly changing in response to our environment. Understanding our behaviors is a tricky task, but one that we are getting ever closer to accomplishing. Traditional methods of study have taught us many things, and now biosensors can lead the way.

I hope you’ve enjoyed reading this snippet from our Complete Pocket Guide to Human Behavior – if you’d like to learn even more and become a true expert in human behavior, then download our free guide below!

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Largest quantitative synthesis to date reveals what predicts human behavior and how to change it

by Hailey Reissman, University of Pennsylvania

Pandemics, global warming, and rampant gun violence are all clear lessons in the need to move large groups of people to change their behavior. When a crisis hits, researchers, policymakers, health officials, and community leaders have to know how best to encourage people to change en masse and quickly.

Each crisis leads to rehashing the same strategies, even those that have not worked in the past, due to the lack of definitive science of what interventions work across the board combined with well intended but erroneous intuitions.

To produce evidence on what determines and changes behavior, Professor Dolores Albarracín and her colleagues from the Social Action Lab at the University of Pennsylvania undertook a review of all of the available meta-analyses—a synthesis of the results from multiple studies—to determine what interventions work best when trying to change people's behavior.

What results is a new classification of predictors of behavior and a novel, empirical model for understanding the different ways to change behavior by targeting either individual or social/structural factors.

A paper published today in Nature Reviews Psychology reports that the strategies that people assume will work—like giving people accurate information or trying to change their beliefs—do not. At the same time, others like providing social support and tapping into individuals' behavioral skills and habits as well as removing practical obstacles to behavior (e.g., providing health insurance to encourage health behaviors) can have more sizable impacts.

"Interventions targeting knowledge, general attitudes, beliefs, administrative and legal sanctions, and trustworthiness—these factors researchers and policymakers put so much weight on—are actually quite ineffective," says Albarracín. "They have negligible effects."

Unfortunately, many policies and reports are centered around goals like increasing vaccine confidence (an attitude) or curbing misinformation. Policymakers must look at evidence to determine what factors will return the investment, Albarracín says.

Co-author Javier Granados Samayoa, the Vartan Gregorian Postdoctoral Fellow at the Annenberg Public Policy Center, has noticed researchers' tendency to target knowledge and beliefs when creating behavior change interventions.

"There's something about it that seems so straightforward—you think x and therefore you do y. But what the literature suggests is that there are a lot of intervening processes that have to line up for people to actually act on those beliefs, so it's not that easy," he says.

Targeting human behavior

To change behaviors, intervention researchers focus on the two types of determinants of human behavior: individual and social-structural. Individual determinants encompass personal attributes, beliefs, and experiences unique to each person, while social-structural determinants encompass broader societal influences on people, like laws, norms, socioeconomic status, social support, and institutional policies.

The researchers' review explored meta-analyses of experiments in which specific social-structural determinants or specific individual determinants were tested for their ability to change behavior. For example, a study might test how learning more about vaccination might encourage vaccination (knowledge) or how reductions in health insurance copayment charges might encourage medication adherence (access).

These meta-analyses encompassed eight individual and eight social-structural determinants—part of the original classification made by the authors.

The results from the research are presented in the following three figures, which pertain to a. all behaviors analyzed, b. only health behaviors, and c. only environmental behaviors.

The figures present interventions with individual targets on the left, and interventions with social/structural targets on the right. For each determinant, the figures show whether the effects has been shown to be negligible, small, medium or large.

Individual determinants

The analyses researchers conducted showed that what are often assumed to be the most effective individual determinants to target with interventions were not the most effective. Knowledge (like educating people about the pros of vaccination), general attitudes (like implicit bias training), and general skills (like programs designed to encourage people to stop smoking) had negligible effects on behavior.

What was effective at an individual level was targeting habits (helping people to stop or start a behavior), behavioral attitudes (having people associate certain behaviors as being "good" or "bad"), and behavioral skills (having people learn how to remove obstacles to their behavior).

Social-structural determinants

The researchers also found that what are often assumed to be the most effective social-structural persuasive strategies were not. Legal and administrative sanctions (like requiring people to get vaccinated) and interventions to increase trustworthiness—justice or fairness within an organization or government entity—(like providing channels for voters to voice their concerns) had negligible effects on behavior.

Norms and forms to monitor and incentivize behavior had some effects, albeit small. What was most effective was focusing on targeting access (like providing flu vaccinations at work) or social support (facilitating groups of people who help one another to meet their physical activity goals).

Granados Samayoa says that knowing which behavior change interventions work at which levels will be especially crucial in the face of growing health and environmental challenges.

"When faced with massive problems like climate change , policy makers and other leaders have this desire to do something to change people's behavior for the better," says Samayoa.

"Our study provides valuable insights. Our research can inform future interventions and create programs that are actually effective, not just what people assume is effective.

Albarracín is glad policymakers will have this resource now.

"Before this study, analyses of behavior change efforts were limited to one domain, whether that was environmental science or public health. By looking at research across domains, we now have a clearer picture of how to encourage behavior change and make a difference in people's lives," she says.

"Our research provides a map for what might be effective even for behaviors nobody has studied. Just like masking because a critical behavior during the pandemic but we had no research on masking, a broad empirical study of intervention efficacy can guide future efforts for an array of behaviors we have not directly studied but that need to be promoted during a crisis."

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Examples of Human Behavior Research

We humans are an intriguing species and it's no wonder researchers have always been fascinated by understanding human behavior. We recently wrote about how to study human behavior , and different ways to measure human behavior. 

On our Behavioral Research Blog, there are plenty of examples of human behaviors and research on behavior. In this blog post we are highlighting some examples of human behavior research and behavior studies.

Table of contents

What is human behavior, cognitive neuroscience.

• Autism research in infants

Adolescent research

• On-site observational research
• Doctor-patient interaction
• Healthcare research examples
• Emotion analysis

Sensory science and eating behavior

Consumers' food choices and emotions.

• User experience research

In science, human behavior commonly refers to the way humans act and interact: the actions, thoughts, and emotions of individuals and groups. It encompasses a wide range of activities, from physical movements and interactions with the environment, to complex mental processes such as decision-making and problem-solving. It is based on and influenced by several genetic and environmental factors, such as genetic make-up, culture, and individual attitudes and values. 

Human behavior research and human behavior studies look into several research questions, like: Why do we act the way we do? How is our behavior influenced, or measured? The study of human behavior is a broad and interdisciplinary field that draws on theories and methods from psychology, sociology, anthropology, economics, and other related disciplines. The goal of this field is to understand why people behave in the ways that they do and to use this understanding to improve people's lives and solve real-world problems. 

The ultimate goal is to increase our understanding of the human experience and to use this knowledge to enhance individual and collective well-being.

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Cognitive neuroscience is the overlapping science of the ‘dry and the wet’ part of the brain: where dry represents the cognitive part (mind, emotions, and senses), and where wet represents the brain. This combination of scientific disciplines tries to explain the connection between neural activities in the brain and mental processes, in order to find answers to the questions of how neural circuits in the brain affect cognition.

This blog series addresses the interplay between the brain, behavior, and emotions, in the field of cognitive neuroscience:

• Cognitive neuroscience: the basics
• Cognitive neuroscience: Emotions
• Cognitive neuroscience: Behavior

Autism research: Observing infants to detect autism

Making the same movement multiple times, or making repetitive movements, is an important step in the development of a newborn child and them learning how to use their limbs. Repeating these movements is typical for motor development; but an increased frequency of repetitive movements can be an early indicator for neurodevelopmental disorders.

Purpura et al. conducted a retrospective analysis of video clips taken from home videos recorded by parents, to verify if a higher frequency of repetitive movements could differentiate infants with ASD from infants with Developmental Delay (DD) and Typical Development (TD), analyzing the age range between six and 12 months. 

Read more about their study here .

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How are adolescents’ emotions socialized by mothers and close friends? What can parents do to prevent escalating conflicts? What is the role of early childhood stress and inhibitory control adolescent substance use? These and more studies are excellent examples of adolescent research:

• Direct observations help develop effective interventions in adolescence
• Understanding adolescent emotions
• Studying conflict interactions between mothers and adolescents
• The role of inhibitory control on substance use in adolescence

On-site observational studies

In some cases observations for your study are best performed on-site. For example, you might want to observe people in a natural setting: at home, in a shop, in the classroom, or in the office.

Another case where on-site research would be beneficial is when your participants are experiencing health issues, preventing them from travelling to your lab. Conducting your research on location enables you to study people that are otherwise difficult to reach.

In this blog post, we highlighted two cases of on-site observational studies with older age groups , conducted at home or at a healthcare facility.

Doctor-patient interactions and the use of humor

Science has proven that laughter is healthy. However, how often is humor actually used during doctor-patient interactions? To characterize the logistics of humor in medical encounters, e.g. frequency, who introduces it, or what it is about, researcher Phillips and her team analyzed audio/video-recorded clinical encounters to describe the frequency and other features of humor in outpatient primary and specialty care visits. 

More healthcare research examples

Healthcare research - sometimes also called "medical research" or "clinical research" - refers to research that is done to learn more about healthcare outcomes. There are plenty of healthcare research examples on our Behavioral Research Blog:

• Evaluating ergonomics in healthcare – paramedics
• Evaluating the effectiveness of simulation in healthcare
• Improving patient safety
• How to assess medical team effectiveness
• Optimizing safety and efficiency in an OR
• What is simulation training?

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Emotion analysis: The emotions of people who think they're nice

What does ‘nice’ actually mean in relation to psychological variables? And does it positively correlate with self-reported levels of health, happiness, and wellbeing? Researchers of i2 media research from the Goldsmiths University of London, UK, developed a tailored questionnaire to explore this and got some interesting results . 

Read this:  Must-see TED Talks on emotions

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Sensory science focusses on the effects of sensory (like taste, smell) and metabolic signals (like nutritional properties, hormones) on the physiological and neurobiological responses underlying eating behavior .

Professor Kees de Graaf (Wageningen University & Research, The Netherlands) has been involved in sensory science and resarch into eating behavior for years. Things like measuring bite size, chewing behavior and frequency are vital in order to understand eating behavior. He hopes that in 5-10 years, we will be able to measure food intake in an accurate way without interfering with subjects.

Watch the movie below to learn more about his research .

Is there a relationship between food choice and a person’s mood? Bartkiene et al. examined the factors that influence our food choice , using facial expression analysis. 

User Experience Research

User experience (UX) is the overall experience of a person using a product (a website, app, game, e.g.) or service, and his or her emotions and attitudes while using it. UX research dives into these user experiences, often carried out in a lab.

A UX Lab is used for usability testing and user experience research . Users are observed in a specific environment while interacting with a product or system. Most UX research is conducted in state-of-the-art UX labs .

Understanding the digital world at the Social Media Lab -  In this unique lab, technology is applied to understanding user experience, behavior on social media, and much more.

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Science behind predicting and changing human behavior

H ave you felt that sinking feeling when another crisis hits, and leaders seem clueless about how to actually change people's behavior? We know changing how people think and act is vital, but the usual tactics never seem to work.

Professor Dolores Albarracín from the University of Pennsylvania has news for us: there's a science to this, and we've been doing it all wrong.

Influencing people

This research challenges a lot of our deeply held assumptions about how to influence people. For example, spending time and effort on changing what people believe often has surprisingly little impact on how they actually behave.

It turns out that focusing on practical aspects, like building new habits and removing obstacles that get in people's way, is much more effective.

The implications here are massive. This isn't just a debate about whether people get vaccinated or choose greener options. It's about fundamentally shifting how policymakers and communities approach problems. It gives us a powerful evidence-based set of tools to create real change in people's lives. If that sounds exciting, keep reading!

How behavior works

To change how people behave , we need to understand two key types of factors that influence them:

Individual determinants

These are the internal building blocks that shape each person's actions. They include things like what someone already knows about a topic, the attitudes and beliefs they've formed, and the practical skills they possess (or the ones they might need to learn).

Social-structural determinants

These are the external forces and systems that surround us, creating the context in which we live. They consist of legal frameworks and policies that restrict or encourage certain behaviors, the social pressure to conform to what others do (norms), and whether essential resources for change are readily accessible (like healthcare , education, or supportive communities).

To grasp the interplay, think of individual factors as the ingredients a person brings to the table – their knowledge, mindset, and abilities. The social-structural factors are akin to the kitchen itself – the tools available, the rules they must follow, and the broader environment that either supports or hinders their efforts.

Educating to change behavior

Let's break down the different ways we can target people's individual thoughts and motivations, and how those efforts translate into actual results. Researchers classify the impact of these strategies as "negligible," "small," "medium," or "large" when it comes to influencing behavior :

Individual Factors

• Negligible Effect: Trying to influence people through broader knowledge, general attitudes, or teaching generic skills shows shockingly little effect on their actions. In other words, facts alone or vague "awareness campaigns" are unlikely to make much of a dent.
• Small-Medium Effect: Strategies focused on shaping people's attitudes toward a specific behavior (like thinking recycling is "good") and helping them develop specific skills for that behavior demonstrate some impact, but it's limited.
• Large Effect: Helping people form strong habits around a behavior has the biggest payoff. This means finding ways to make the desired action automatic and routine. Similarly, fostering deep associations between a behavior and concepts like "good" or "bad" can be extremely powerful.

This is a critical insight for anyone trying to encourage change. The emphasis shouldn't be on making people simply know more, or hoping they'll feel a certain way. It's about embedding actions into everyday life and forging strong mental links that guide decision-making.

Laws to change behavior

Let's look at the external systems and influences that can motivate (or demotivate) behavior. As with individual factors, researchers have measured the impact of different strategies as "negligible," "small," "medium," or "large":

Social-structural factors

• Negligible effect: Surprisingly, top-down approaches like imposing laws and regulations or trying to boost trust in institutions have very little influence on people's actions. This means that threats of punishment or making a government agency seem more trustworthy won't magically change people's choices.
• Small effect: Using social pressure via norms ("what everyone else is doing") and offering incentives (like rewards) can have some effect, but it's fairly limited.
• Large effect: Providing direct support networks and removing barriers to accessing essential resources has the most significant impact when it comes to shifting behavior. This could look like peer support groups for people trying to improve their health or making it easy and affordable to access preventative healthcare.

Instead of coercion or trying to control what's inside people's heads, the biggest potential for change lies in shaping the environment around them. Support systems and easy access to what's needed create an environment where the desired behavior becomes the most natural and supported choice.

How do we change people's behavior?

The old mindset, whether applied to individuals or society, focuses on knowledge and coercion. Think of the endless warnings and information dumps coupled with the assumption that punishment will scare people into compliance. However, this new research tells us that approach is largely ineffective.

What truly works is a fundamental shift. Instead of fixating on what people believe, we need to prioritize:

Creating automatic routines

Help people integrate desired actions into their daily lives to the point where it becomes second nature. When we repeatedly perform an action, it creates and strengthens connections in our brains, making it increasingly easier to do with less conscious thought. Think of it like creating a well-worn path that you naturally follow.

The less we have to actively debate whether to do something, the more likely we are to do it without resistance. If exercising or eating healthy is automatic, we don't have to battle internal arguments or willpower.

Habits become interwoven with how we see ourselves. A person whose daily routine includes physical activity starts to think of themselves as someone who exercises, making the behavior part of their core identity.

Fostering supportive communities

Humans are social creatures, and seeing others consistently demonstrate a desired behavior makes it feel more achievable and socially acceptable . This normalizes the behavior, making it seem like the natural thing to do.

Knowing others expect us to act a certain way provides positive social pressure. Supportive groups can offer encouragement, celebrate successes, and help navigate setbacks.

Community members become resources for problem-solving, sharing tips and strategies that have worked, and providing expertise or assistance with overcoming challenges.

The sense of belonging and connection fosters feelings of acceptance and encouragement. This combats isolation and builds resilience so people are less likely to give up in the face of setbacks.

Removing barriers

Even if someone knows why a behavior is important and wants to change, practical obstacles can completely derail their efforts. It's about what's possible, not just what's ideal.

People operate within existing systems and structures that may make adopting new behaviors extremely difficult. Think of food deserts, lack of transportation, or complicated healthcare systems.

For many people, tangible changes in their environment will have a far greater impact than trying to change what they think or feel. This approach empowers positive action even if immediate shifts in mindset don't happen.

The key is identifying the specific roadblocks people face and designing interventions that dismantle them. This levels the playing field, making it possible for people to act in line with their intentions regardless of their individual circumstances.

Let's imagine the possibilities this approach unlocks:

• Quitting smoking programs: Rather than bombard smokers with grim statistics, they'd focus on the nitty-gritty of establishing new routines and stress management techniques.
• Climate initiatives: Forget endless guilt trips about carbon footprints. Green choices would become the easiest, most convenient default option for transportation, energy, and daily life.
• Healthcare systems: Healthy living would be accessible and affordable for everyone, from regular checkups to nutritious food options, regardless of income or insurance status.

The power of positive behavior change

This research isn't about making us cynical. It's about getting smarter. No more "common sense" approaches that never pan out. Instead, we've got an evidence-based roadmap.

"Before this study, analyses of behavior change efforts were limited to one domain, whether that was environmental science or public health. By looking at research across domains, we now have a clearer picture of how to encourage behavior change and make a difference in people's lives," says Professor Albarracín.

"Our research provides a map for what might be effective even for behaviors nobody has studied. Just like masking because a critical behavior during the pandemic but we had no research on masking, a broad empirical study of intervention efficacy can guide future efforts for an array of behaviors we have not directly studied but that need to be promoted during a crisis."

Ultimately, this is about understanding that changing the world starts with understanding the actual mechanics of how we humans work. And now, we have a much better manual.

The study is published in the journal Nature Reviews Psychology .

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Animal behavior research is getting better at keeping observer bias from sneaking in – but there’s still room to improve

Professor and Associate Head of Psychology, University of Tennessee

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Todd M. Freeberg does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

University of Tennessee provides funding as a member of The Conversation US.

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Animal behavior research relies on careful observation of animals. Researchers might spend months in a jungle habitat watching tropical birds mate and raise their young. They might track the rates of physical contact in cattle herds of different densities. Or they could record the sounds whales make as they migrate through the ocean.

Animal behavior research can provide fundamental insights into the natural processes that affect ecosystems around the globe, as well as into our own human minds and behavior.

I study animal behavior – and also the research reported by scientists in my field. One of the challenges of this kind of science is making sure our own assumptions don’t influence what we think we see in animal subjects. Like all people, how scientists see the world is shaped by biases and expectations, which can affect how data is recorded and reported. For instance, scientists who live in a society with strict gender roles for women and men might interpret things they see animals doing as reflecting those same divisions .

The scientific process corrects for such mistakes over time, but scientists have quicker methods at their disposal to minimize potential observer bias. Animal behavior scientists haven’t always used these methods – but that’s changing. A new study confirms that, over the past decade, studies increasingly adhere to the rigorous best practices that can minimize potential biases in animal behavior research.

Biases and self-fulfilling prophecies

A German horse named Clever Hans is widely known in the history of animal behavior as a classic example of unconscious bias leading to a false result.

Around the turn of the 20th century , Clever Hans was purported to be able to do math. For example, in response to his owner’s prompt “3 + 5,” Clever Hans would tap his hoof eight times. His owner would then reward him with his favorite vegetables. Initial observers reported that the horse’s abilities were legitimate and that his owner was not being deceptive.

However, careful analysis by a young scientist named Oskar Pfungst revealed that if the horse could not see his owner, he couldn’t answer correctly. So while Clever Hans was not good at math, he was incredibly good at observing his owner’s subtle and unconscious cues that gave the math answers away.

In the 1960s, researchers asked human study participants to code the learning ability of rats. Participants were told their rats had been artificially selected over many generations to be either “bright” or “dull” learners. Over several weeks, the participants ran their rats through eight different learning experiments.

In seven out of the eight experiments , the human participants ranked the “bright” rats as being better learners than the “dull” rats when, in reality, the researchers had randomly picked rats from their breeding colony. Bias led the human participants to see what they thought they should see.

Eliminating bias

Given the clear potential for human biases to skew scientific results, textbooks on animal behavior research methods from the 1980s onward have implored researchers to verify their work using at least one of two commonsense methods.

One is making sure the researcher observing the behavior does not know if the subject comes from one study group or the other. For example, a researcher would measure a cricket’s behavior without knowing if it came from the experimental or control group.

The other best practice is utilizing a second researcher, who has fresh eyes and no knowledge of the data, to observe the behavior and code the data. For example, while analyzing a video file, I count chickadees taking seeds from a feeder 15 times. Later, a second independent observer counts the same number.

Yet these methods to minimize possible biases are often not employed by researchers in animal behavior, perhaps because these best practices take more time and effort.

In 2012, my colleagues and I reviewed nearly 1,000 articles published in five leading animal behavior journals between 1970 and 2010 to see how many reported these methods to minimize potential bias. Less than 10% did so. By contrast, the journal Infancy, which focuses on human infant behavior, was far more rigorous: Over 80% of its articles reported using methods to avoid bias.

It’s a problem not just confined to my field. A 2015 review of published articles in the life sciences found that blind protocols are uncommon . It also found that studies using blind methods detected smaller differences between the key groups being observed compared to studies that didn’t use blind methods, suggesting potential biases led to more notable results.

In the years after we published our article, it was cited regularly and we wondered if there had been any improvement in the field. So, we recently reviewed 40 articles from each of the same five journals for the year 2020.

We found the rate of papers that reported controlling for bias improved in all five journals , from under 10% in our 2012 article to just over 50% in our new review. These rates of reporting still lag behind the journal Infancy, however, which was 95% in 2020.

All in all, things are looking up, but the animal behavior field can still do better. Practically, with increasingly more portable and affordable audio and video recording technology, it’s getting easier to carry out methods that minimize potential biases. The more the field of animal behavior sticks with these best practices, the stronger the foundation of knowledge and public trust in this science will become.

• Animal behavior
• Research methods
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'Dance Your Ph.D.' winner on science, art, and embracing his identity

Weliton Menário Costa (center) holds a laptop while surrounded by dancers for his music video, "Kangaroo Time." From left: Faux Née Phish (Caitlin Winter), Holly Hazlewood, and Marina de Andrade. Nic Vevers/ANU hide caption

Weliton Menário Costa (center) holds a laptop while surrounded by dancers for his music video, "Kangaroo Time." From left: Faux Née Phish (Caitlin Winter), Holly Hazlewood, and Marina de Andrade.

Weliton Menário Costa grew up in rural Brazil. "I come from the countryside of the countryside of the countryside," he says. He didn't have much, but from his earliest days, he loved to sing.

"I just remember looking at the singers on television and loving them," Menário Costa recalls. "I think if I could have picked a profession — if the world was equal and you could pick anything — I would have picked 'musician.'"

He took a detour into science, but ultimately he's returned to embrace music professionally. And he recently picked up a major accolade. Menário Costa won this year's " Dance Your Ph.D ." contest, an annual competition organized by Science magazine where doctoral students and Ph.D. graduates showcase their research through dance.

Menário Costa's winning submission highlights his work on kangaroo behavior and personality, but it also celebrates his identity — and what he's had to overcome to embrace it.

'I would just sing ... every day'

When Menário Costa was a boy in Brazil, he would try to sing and dance with his younger sister outside. That's when the comments would start.

"People were always like, 'Oh, that's a girl thing, you're a f** or whatever,'" he says. "Back then, I didn't even know what it was. I just knew it was negative. It's a very sexist space and homophobic and all that."

When Menário Costa did receive a compliment, it was usually for how smart he was. So he buried himself in school and excelled. He got into a competitive high school. But even so, he was chronically anxious about what others thought of him and worried that he wasn't good enough.

Research News

Scientists studied how cicadas pee. their insights could shed light on fluid dynamics.

" So instead of going to parties and dancing or performing and doing the things I actually loved," Menário Costa says, "I would just lock myself in the room and say, 'Hey, I have homework.' But when I would shower, I would just sing ... every day."

With time, Menário Costa made it to Australia — first to study English, and then he received a scholarship to pursue his Ph.D. in behavioral ecology at the Australian National University in Canberra. His research focused on eastern gray kangaroos in Wilsons Promontory National Park in southeastern Australia.

"And my main question was, do kangaroos have personality ... different personalities?," Menário Costa explains. "And then, what's driving the behavior you see? Is it due to personality, or is it the social environment?"

It was during his Ph.D. — when Menário Costa was on this other continent half a world away from Brazil — that he managed to connect with who he really was. He came out as queer. He started singing and dancing out in the world again. And after finishing his Ph.D. amidst the struggles of COVID and bushfires, Menário Costa decided to leave science and dive into creative work.

This medieval astrolabe has both Arabic and Hebrew markings. Here's what it means

"Now I'm gonna be a singer, now I'm gonna be a dancer, and now I'm gonna be all these things I liked as a kid," he says. Menário Costa started performing at pubs and small venues, mostly singing covers. "Then, last year, I started writing as well, and performing my own original songs."

Diversity in kangaroos — and in dance

To Menário Costa, Science magazine's "Dance Your Ph.D." competition felt like "a perfect way of exposing my work as a singer songwriter ."

His submission — the song and dance "Kangaroo Time" — was born in an act of exuberant collaboration. The music video opens with Menário Costa driving to what appears to be his field site. There are a couple of kangaroo shots, but mostly it's a joyous sequence of dancers on an open landscape in Canberra — drag queens, Capoeira performers, ballet dancers, and people doing samba, salsa, hip hop, Brazilian funk, and traditional Indian dance.

This often-overlooked sea creature may be quietly protecting the planet's coral reefs

"The way they move is very different," says Menário Costa, "but also what they wear to perform is quite different. I decided to use the actual diversity we have in a dance community."

This was how Menário Costa represented one of his central findings — that kangaroos have distinctive personalities, based on how much they squirm when they're handled as joeys and at what distance subadults and adult females move away from an approaching human.

In addition, kangaroo siblings often have similar personalities, and for that Menário Costa dances alongside his own sister — the first family member to ever visit him in Australia. "One of the main reasons that made her want to come was to be in that video," he says. "It was so special having her here."

Menário Costa also discovered that when kangaroos move between groups, they adjust their behavior to conform to that of their companions. In the video, he makes his way to other groups and adopts the new dancing styles as he goes.

The main lyrics are simple, but catchy: "I'm gonna share with you... hope you don't mind... some things I learned from my kangaroo time." The phrase "kangaroo time" has a rainbow of meanings.

"It means the time I did my kangaroo research," says Menário Costa. "But [it] also means the first time I lived as a gay man. It's the first time I lived as an immigrant, five years without going home. The time of reconnection to myself, of exploring my sexuality, of bridging these beautiful communit[ies]."

Menário Costa, who now goes by the stage name WELI, says that filming this music video — when all his worlds came together in a single afternoon — feels like his most significant achievement to date. He likens his first place finish to winning the Eurovision Dance Contest.

The video ends with text emblazoned onscreen — "Differences lead to diversity. It exists within any given species; it is just natural."

• science communication
• animal behavior
• animal biology
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• Open access
• Published: 01 May 2024

Frequent disturbances enhanced the resilience of past human populations

• Philip Riris   ORCID: orcid.org/0000-0003-4244-7495 1 ,
• Fabio Silva   ORCID: orcid.org/0000-0003-1368-6331 1 ,
• Enrico Crema   ORCID: orcid.org/0000-0001-6727-5138 2 ,
• Alessio Palmisano   ORCID: orcid.org/0000-0003-0758-5032 3 ,
• Erick Robinson   ORCID: orcid.org/0000-0002-0789-3724 4 , 5 , 6 ,
• Peter E. Siegel   ORCID: orcid.org/0000-0003-1260-7646 7 ,
• Jennifer C. French 8 ,
• Erlend Kirkeng Jørgensen 9 ,
• Shira Yoshi Maezumi   ORCID: orcid.org/0000-0002-4333-1972 10 ,
• Steinar Solheim   ORCID: orcid.org/0000-0001-8293-8147 11 ,
• Jennifer Bates 12 ,
• Benjamin Davies 13 ,
• Yongje Oh 12 &
• Xiaolin Ren   ORCID: orcid.org/0000-0002-3247-437X 14  

Nature ( 2024 ) Cite this article

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• Archaeology
• Climate-change adaptation
• Climate-change impacts
• Environmental social sciences
• Social evolution

The record of past human adaptations provides crucial lessons for guiding responses to crises in the future 1 , 2 , 3 . To date, there have been no systematic global comparisons of humans’ ability to absorb and recover from disturbances through time 4 , 5 . Here we synthesized resilience across a broad sample of prehistoric population time–frequency data, spanning 30,000 years of human history. Cross-sectional and longitudinal analyses of population decline show that frequent disturbances enhance a population’s capacity to resist and recover from later downturns. Land-use patterns are important mediators of the strength of this positive association: farming and herding societies are more vulnerable but also more resilient overall. The results show that important trade-offs exist when adopting new or alternative land-use strategies.

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Understanding the range of past responses of human societies to disturbances is a global priority across the social and natural sciences and will support the development of solutions to future crises 1 , 2 , 3 . Numerous case studies have addressed past cultural collapse, transformation and persistence, although how to best characterize these processes is a subject for debate 6 . A major unresolved issue is the lack of comparability between cases of population resilience in the historical sciences 4 , 5 . Few studies explicitly model impacts, recovery and adaptation, or formally account for long-term history, which contains important and previously overlooked variation within and between cultural or environmental settings. Furthermore, a tendency to narrowly focus on responses to extreme events in both natural and social systems 7 , 8 may overemphasize local or short-term adaptive success at the expense of understanding large-scale or long-term vulnerabilities 6 , 9 . A well-known example is the shift to a narrow marine diet among the Greenland Norse that initially offset the short-term risk of crop failure yet heightened societal vulnerability to longer-term North Atlantic cooling 10 . Here, we establish a global comparative approach to long-term resilience to identify the factors that structure the response of prehistoric populations to disturbances through time. The approach measures population capacity to withstand changes, as well as the rate of recovery following a disturbance through the common proxy of radiocarbon time–frequency data 11 , 12 . Disturbances are the inferred drivers of relative reductions in population or archaeological activity in prehistory, which are described variously as recessions, downturns, busts, negative deviations or similar 12 , 13 , 14 , 15 and form the focus of this study, using summed probability distributions (SPDs) of calibrated radiocarbon dates. SPDs function as an index of relative levels of human activity, or population change, over time 16 , 17 . Population downturns are defined as periods when SPDs are significantly below expected growth trajectories in response to disturbances. Our efforts focus on two key questions: (1) how quickly do past populations recover after downturns; and (2) what factors mediate past resistance and resilience to downturns?.

To quantify patterns in population resistance–resilience, we performed a meta-analysis of 16 published study regions that used archaeological radiocarbon data to reconstruct regional palaeodemographic trends (Supplementary Table 1 ). Our approach trades specificity for a large-scale, comparative perspective that still accounts for variation between cases to focus on the emergent properties of the statistical analysis. Studies were reviewed based on three criteria: evidence for significant downturns, their scope and the inclusion of radiocarbon datasets. A lack of any single criterion resulted in the exclusion of a study. Cases with no reported downturns were not included, nor were those whose scope was restricted to specific activities within a regional radiocarbon dataset, such as flint mining 18 . Where published data have been superseded by later compilations, dates were added from the People3k database 19 , a systematic compilation of cleaned radiocarbon dates, based on the geographical area of the original study. Our global sample of regions ranges from the Arctic to the tropics and spans 30,000 years of history (Fig. 1a ). Population downturns were reproduced using our protocol ( Methods ) and resistance–resilience metrics (Fig. 1b ) were collected on 154 periods of population downturn, with a median of 8.5 periods in each region (Fig. 1c and Supplementary Tables 1 and 2 ). The numerical metrics collectively capture the severity, chronology and frequency of periods of statistically significant population downturn. Disturbances were classified into both general categories and specific drivers according to the original studies and expert interpretation of regional social, cultural and environmental history (Extended Data Table 1 ). The broad category of land use and evidence for adaptive change during, or in the wake of, downturns were also recorded. The focus of this meta-analysis is to identify the factors governing the relative depth of downturns (resistance) and the rate of recovery at their conclusion (resilience). A suite of hierarchical linear mixed models was developed to test for significant associations between parameters while controlling for regional variability ( Methods ).

a , World map of study regions made with Natural Earth. Time ranges in calendar years before present (cal bp ) and 14 C dates used in this meta-analysis are indicated for each region. b , Conceptual diagram of measuring resistance–resilience on palaeodemographic downturns against expected growth trajectories. Downturn A is longer and faster, with low resistance and low relative rate of recovery (resilience). Downturn B is shorter and slower, displaying higher resistance and higher resilience. Other combinations of high or low resistance and/or resilience are possible (Extended Data Fig. 4 ). Parameters b , x and e for the equations in Table 1 are indicated in white dots. c , Summary of disturbance types within three broad categories reported in published palaeodemographic studies. Listed proxies are examples drawn from the surveyed literature. ‘Unclear’ downturns (downturns with a lack of clear evidence for a driver) are not shown (Extended Data Table 1 ).

This approach to past resistance–resilience provides a glimpse into population-level responses to disturbances throughout human history. Results demonstrate that a single factor—the frequency of downturns—increases both the ability to withstand disturbances and to recover from them. Additionally, the frequency of downturns experienced by a given population is influenced by land use: agricultural and agropastoral populations experience significantly more downturns over time than other land-use patterns recorded during downturns (Extended Data Table 1 ). These findings collectively suggest that the global shift to food-producing economies during the Holocene (starting 11,700 calendar years before present) may have increased population vulnerability to disturbances, yet in the process enhanced their adaptive capacity through repeated exposure. Parallels to our long-term perspective on human population change in macroecology suggest that comparative approaches in the historical sciences have the potential to generate profound insights into past human–environmental relationships on a broad scale.

Cross-sectionally high resilience

The most common high-level driver of downturns, after those with a lack of evidence for a specific cause (unclear, n  = 65), is environmental ( n  = 48, 31%), followed by mixed (cultural–environmental) ( n  = 33, 21%). The most common disturbance type is aridity in the environmental category ( n  = 31), followed by mobility ( n  = 20) in the cultural category. Only a third of recorded downturns have resistance values that drop more than 50% from pre-downturn activity levels ( n  = 53, 34.4%; Fig. 2a ). Most downturns ( n  = 133, 86%) end before baseline relative population levels are attained; in other words, observed SPD values are lower at the end of most downturns than at the pre-disturbance reference value. Resilience is relatively high across all cases (median = 0.64, n  = 154), with 40% ( n  = 63) still attaining 90% of pre-downturn conditions by their end (Fig. 2b ). Full returns to SPD baseline conditions are frequently interrupted by subsequent downturns. Downturns associated with cultural drivers return the highest median resilience (0.74), whereas the median mixed (cultural–environmental) resistance is highest at 0.79. Resistance (0.65) and resilience are lowest among climate-driven downturns (0.57). However, we do not find support for significant differences in either metric across disturbance categories (analysis of variance, resistance: F  = 0.541, P  = 0.65, d.f. = 3, n  = 154; resilience: F  = 0.04, P  = 0.98, d.f. = 3, n  = 154).

a , b , Distributions of resistance ( a ) and resilience ( b ) across disturbance categories (Extended Data Table 1 ). The lower and upper hinges correspond to the 25th and 75th percentiles The upper and lower whiskers extend from the hinges to 1.5 × the interquartile range (IQR). The thick black line represents the group median. Data beyond the whiskers are individually plotted outlying points. The dashed line represents the combined data median. c , Distribution of the duration of downturns and the time to SPD minima across all recorded downturns. d , Relative pace (overall duration normalized by time to minimum) approximates a normal distribution and enables comparison of the speed of downturns. Modelled downturn durations skew towards multidecadal and centennial timescales.

Global variation in recovery rate

Initial modelling indicates that the geographical location of downturns does not affect resistance, with the exceptions of significantly higher values in the Caribbean archipelago and the Italian peninsula. Conversely, there is support for significantly lower values for resilience in three regions: the Central China Plains, the Caribbean archipelago and the Korean Peninsula, when compared with all other regions (Extended Data Table 2 ). Further examination of these cases reveals that a large minority of downturns in these three regions return negative values of resilience ( n  = 11, 39%), which are produced when the population proxy exceeds the SPD value at the start of the downturn by its end (Extended Data Fig. 4 , 12). Although the SPD population proxy remains below modelled expectations in all of these cases and therefore they are, in the strictest sense, downturns relative to the null models, the results nevertheless imply that populations in these regions were, on average, able to recover faster than the norm. Owing to the observed range of variation and its potential impact on the metrics, study region was retained as a random effect variable in the mixed-effect models.

Long-term downturns are the norm

The durations of population downturns tend towards centennial (100–500 years, n  = 48, 31%) and decadal periods (less than or equal to 50 years, n  = 47, 30.5%), with a median of 98 years across the sample. Downturns lasting longer than 500 years are a minority ( n  = 29). The time taken to reach SPD minima skews further towards decadal timescales (Fig. 2c ). Only a single downturn that commences with the 8.2-thousand-years-ago event in the Near East 20 has a time to minimum longer than a millennium (2,070 years). Both variables have a strong positive skew (duration = 2.84, time to minimum = 4.23). To control for the distribution and broad range of variation in these variables, the time to minimum was normalized by downturn duration to produce an index of its relative speed, which we term ‘pace’ (Fig. 2d ). This transformation enables comparison of the variation between downturns, with higher numbers reflecting slower downturns ( σ  = 0.55, s.d. = 0.23) and lacking support for non-normally distributed values as in the time to minimum and duration variables (Shapiro–Wilk W  = 0.98396, P  = 0.07108). Consequently, relative pace was employed as a fixed-effect candidate in the mixed-effect modelling.

Land use mediates resilience

The frequency of downturns over time by region was estimated by normalizing the cumulative number of downturns in a region by their duration (Table 1 ). We transformed this to the logarithm of events per millennium to account for its strongly skewed distribution. The variable allows us to compare the rate at which downturns occur. It consistently displays the strongest relationship to both resistance and resilience ( P  < 0.001 in both cases) (Extended Data Table 3 ) and is the only fixed variable retained by the information criterion-based selection procedure.

Collectively, these results indicate that populations experience an enhanced ability to withstand disturbances as frequency increases, as well as to recover in the aftermath (Fig. 3a,b ). Further examination of frequency of downturns shows that, among the recorded disturbance types, changes to mobility regimes (median frequency of downturns = 2.26, n  = 20) and high environmental variability (median frequency of downturns = 2.19, n  = 17) occur at the highest rate per millennium, whereas cooling occurs at the lowest rate (median frequency of downturns = 0.761, n  = 4) (Fig. 3c ). In terms of regional variation, the highest frequency of downturn is recorded in the South African Greater Cape Floristic Region (median frequency of downturns = 2.41, n  = 17 over 9,950 years) and the lowest in the Korean Peninsula (median frequency of downturns = 0.58, n  = 3 over 4,000 years).

a , b , Resilience ( a ) and resistance ( b ) are strongly influenced by the rate of downturn per millennium, with a stronger effect for resistance. Values are extracted from fitted models I and II. c , Standardized regression coefficients for significant ( P  < 0.05) mixed-model terms for n  = 154 independent samples based on a two-sided test. Hunter-gatherer was set as the reference group for land use. d , Proportions of dominant land-use types during downturns, in 1,000-year time slices. Pleistocene downturns (before 11,000 calendar years before present, n  = 24) have been combined. The 95% confidence intervals are indicated in a – c by shaded bands and error bars. * P  < 0.05, ** P  < 0.01 and *** P  < 0.001.

Treating the frequency of downturns as a response variable ( Methods ) revealed that agricultural and agropastoralist land-use patterns are associated with significantly higher rates of downturn compared with low-level food production, marine foraging or mixed subsistence. Results from this additional modelling exercise suggest that the frequency of downturns is likely to have an important effect on resistance and recovery, whereas the frequency of downturns itself covaries with the dominant pattern of land use and disturbance type in a given time and place. A larger sample size of downturns would increase the explanatory power of our approach and enhance our characterization of these relationships.

This meta-analysis has examined the potential factors influencing resistance and resilience across a broad archaeological sample, and was controlled for regional variation. The frequency of downturns is the main determinant of the observed outcomes among the examined factors. The relationship between resistance and resilience displays variable rates, although these events all tend to unfold at multidecadal to centennial timescales. Well-known historical examples support this finding: the catastrophic depopulation of indigenous groups across the Americas took place over centuries 21 , and the collapse of imperial power in Western Rome was preceded by a long period of rural depopulation 22 . Systematic data on independent population downturns in prehistory are less common. However, what data are available 23 , 24 corroborate this result: palaeodemographic downturns resolved in radiocarbon data tend to last at least one human generation but frequently much longer.

The frequency of downturns is associated with both the ability of past populations to withstand downturns and the rate of recovery following them across a broad sample of human populations. The results suggest the existence of a common mechanism among human populations that confers resilience to disturbances. The size of this interaction is greater for resistance (eta-squared ( η 2 ) = 0.46, P  < 0.001; Fig. 3b ) than resilience ( η 2  = 0.29, P  < 0.001; Fig. 3a ). In practical terms, this suggests that the ability to withstand downturns is distinct from the ability to recover in their wake. We note that this result does not imply a monocausal or deterministic relationship; there are likely to be several adaptive pathways that increase population resistance and resilience by means of the mechanism of increasing downturn frequency.

Further testing indicates that land-use practices associated with food production, such as farming and herding, are significantly and positively correlated with the frequency of downturns (Fig. 3c and Extended Data Table 3 ). From the early Holocene, the proportion of land use associated with food production in our sample of downturns also increased, as the aggregate global population became gradually more reliant on domesticated species for meeting subsistence needs (Fig. 3d ). Collectively, these trends show that although populations generally increased resistance and resilience over time, the heightened rate of downturn over time is itself likely to be linked to the historical tendency towards food-producing subsistence systems. Current archaeological evidence does not indicate that this process was unidirectional or inevitable; foraging and food production are neither mutually exclusive nor in opposition. Our synthetic findings agree with specific cases showing that the behavioural and social changes that food production entailed had trade-offs in other arenas 23 .

Traditional agricultural or agropastoral practices include a diverse range of socio-ecological systems that are often highlighted as potential sources of inspiration for solutions to current sustainability, biodiversity and conservation challenges 5 , 10 , 25 , 26 . The results suggest that population downturns or collapse are an inherent property of these systems and a potential trade-off of promoting their use. Systematic reviews in disturbance ecology indicate that frequent natural disturbances enhance the long-term resilience of key ecosystem services and that localized subsystem declines are an important mechanism through which this occurs 27 . Our study provides insight into the possible existence of an analogous relationship within our sample of human populations; higher frequencies are strongly correlated with both smaller downturns and closer matches to pre-downturn values in the SPD proxies. We suggest that humanity’s overall constant long-term population growth 28 may have been sustained due, in part, to the emergent positive feedback between vulnerability, resistance and recovery documented here.

Population decline has been termed an ‘inevitable’ feature of our species’ demographic dynamics 29 . In their systematic review of historical collapse and resilience dynamics, Cumming and Peterson 1 list depopulation as a key metric or factor in every ancient socio-ecological system studied. We anticipate that this singular status will continue undiminished. Our contribution indicates that downturns play an important role in human population history by enhancing the resilience of survivor populations. We speculate that the creation of biased cultural transmission may be responsible; downturns provide critical opportunities for landscape learning and the strengthening of local-to-regional knowledge networks to propagate through a cultural system 30 , 31 . Population downturns have been identified as potential triggers of labour investment in infrastructure, social cohesion and technological advancement 15 . Together, these mechanisms have the potential to enhance the preferential transmission of knowledge and practices that promote future resistance or resilience 10 . Raising population thresholds by intensifying land use may also heighten the risk of more serious collapse in return for increasingly marginal benefits 1 , 24 , 32 . Other non-trivial and historically contingent factors that are likely to affect outcomes are the diversity and ecology of domesticated species assemblages, degree and type of political complexity, and settlement patterning in relation to the environment. Indicators such as the cessation of monument construction, loss of literacy or economic turmoil can provide additional insight into the consequences and/or potential drivers of population downturns. These potential causal links must be rigorously tested, however, as they are not easily disentangled. A realistic model for the generative mechanism underlying the resilience of human populations will therefore have to be multiscalar and sensitive to cascading effects to account for how various exogenous impacts unfold and endogenous strategies are developed to solve them. The approach used for our comparative analysis of palaeodemographic resistance–resilience, however, does not distinguish between these elements of the studied populations. Future research may translate between our work and the microscale, the patterns of which are only truly understandable within the kind of generalist, synthetic frame of reference that we provide.

This study finds parallels in macroecology, where analogous resistance–resilience outcomes have been suggested to only fully resolve at centennial timescales or above 33 , 34 . Archaeology is uniquely suited to examining past population history, and the dynamics that underlie these trends, at such long-term timescales 35 , 36 , 37 . Understanding past societies’ responses to crises is often explicitly motivated by the goal of applying learnings from the historical sciences to present-day policy and activism, contributing to the ultimate objective of fostering resilient adaptations for the future 6 . Most archaeological work on past resilience is historically particularistic 4 , 9 and emphasizes the contingencies, decisions and practices that underwrote successful adaptations in specific times and places 38 , 39 . This specificity can be illuminating but, if the historical sciences are to play any role in fostering future resilience, improving our understanding of the processes and drivers that influence long-term, centennial-scale resilience is a necessary prerequisite 5 . Our approach has highlighted the global relationship between population change and frequency of disturbance over millennial timescales and applies across a broad geographical and chronological sample of past populations, including prehistoric examples that have been overlooked in systematic reviews of societal resilience more broadly. Improved clarity on the drivers of exposure frequency and type in the past will help to reveal the mechanism(s) behind the dynamics we describe and their potential limits, which is particularly important as environmental variability is predicted to increase in the future 40 , 41 . Archaeological and historical case studies have focused on the frequency of volcanism, warfare 42 and hydroclimatic oscillations 23 , 43 , as well as the rate of abrupt or extreme events in general 8 . Comparable evidence on different categories of downturns is necessary. Synthesis of these or similar data in a comparative framework can provide important insights into the causal links between population resilience, risk of exposure and, ultimately, the ability to recover.

Calibration and aggregation

Archaeological radiocarbon dates were collated from published studies that previously adopted null hypothesis significance testing (NHST) approaches towards prehistoric demography. Our literature search identified 16 studies that collectively span six continents and approximately 30,000 radiocarbon years (Supplementary Tables 1 and 3 – 18 ). We applied a consistent protocol to the calibration of radiocarbon assays. The calibrate function within the R package rcarbon 17 was used to convert 14 C radiocarbon years to calendar years before present. The IntCal20 (ref. 44 ) and SHCal20 (ref. 45 ) curves were applied to dates in the northern and southern hemispheres, respectively. Calibrated ages are reported as the 95.4% (two-sigma, 2 σ ) age range. Laboratory codes and site identification numbers were appended to each calibrated date range and postcalibration distributions were not normalized. To account for between-site variation in sampling intensity, several dates from a single site that are within 50 radiocarbon years of each other were pooled (‘binned’) before aggregation into regional SPDs.

Bayesian model fitting

Our protocol aimed to replicate the results of the 16 identified case studies to the greatest extent possible. To reproduce statistically significant negative population events (‘downturns’) produced by rcarbon’s ‘modelTest()’ function in the original studies, while simultaneously addressing the well-known limitations of using summed probability distributions in NHST, we adopted an alternative, Bayesian modelling approach. Markov Chain Monte Carlo (MCMC) methods implemented in the nimbleCarbon package 46 for radiocarbon data can obtain robust parameter estimates, accounting for radiocarbon measurement errors and sampling error simultaneously. Previously, this has been a major drawback of NHST approaches to aggregated radiocarbon data, with several alternatives proposed in the literature 47 , 48 , 49 . Using the MCMC-derived parameter estimates in posterior predictive checks enabled us to detect periods where expected growth trajectories were lower than the fitted model parameters and which were more robust and accurate than least-squares regression approaches applied directly on SPDs. The protocol produced outputs that are analogous to those in previous NHST studies (Extended Data Fig. 1 ).

We analysed regional SPDs separately by fitting identical bounded exponential growth models to each dataset. This common model is defined by three parameters: growth rate ( r ) and boundaries ( a and b ). A weakly informative exponentially distributed prior ( λ  = 1/000.4) was used for r to capture a broad range of potential growth rates among the cases. Parameters a and b were adopted from the original studies. Markov chain traceplots (Extended Data Figs. 2 and 3 ) evaluate chain mixing alongside model convergence (Gelman-Rubin Ȓ) and effective sample size diagnostics (Extended Data Table 5 ). Three chains of 50,000 iterations were run per region, with a burn-in of 5,000 iterations and a thinning interval of 2. To ensure comparability of results with published studies that used a logistic growth model as a null hypothesis, regional datasets were subset based on expert judgement at documented palaeodemographic transitions and treated as two separate exponential growth models. Subsetting was only performed on the Near East and Italy, Sicily and Sardinia datasets. Downturns adjacent to transition points were removed from the sample to avoid including data points introduced by the subsetting. Posterior predictive checks were executed using samples of parameter estimates obtained by the MCMC approach to simulate and back-calibrate a number of radiocarbon dates equal to the sample size. The procedure was repeated 1,000 times to derive critical envelopes.

Resilience metrics

The resilience metrics target periods when empirical SPD curves are below the 90% confidence envelopes of the fitted models, according to the posterior predictive checks (periods termed ‘downturns’). Extraction was performed using a bespoke R function modified from Riris and De Souza 12 , which is available at ref. 50 . The principal response metrics in our analysis are resistance and resilience (Extended Data Fig. 4 ). Respectively, these metrics quantify the normalized response to downturns and the rate of recovery relative to baseline conditions. Resistance is measured on SPDs using two parameters: SPD values at the start of a downturn ( b ) and at downturn minima ( x ), whereas resilience is measured across the entire period of decline until its end ( e ) relative to the minimum and baseline (Table 1 ). Resistance ranges between 0 and 1, indicating a 100% change from baseline to no change. Resilience ranges between −1 and 1, with 1 indicating full recovery by the end of the downturn. Negative values of resilience indicate that the baseline value has been exceeded, although remaining outside the expectations of the null model. Finally, zero indicates no recovery 11 , 51 . Variations in the shape of the SPD can result under comparable scenarios (that is, with similar demographic dynamics, archaeological sample sizes and disturbances) and hence can produce different results in the metrics because of calibration effects despite their similarity. Although this issue may contribute to noise and error in measurement, it is nevertheless highly unlikely to be systematic or to correlate with other variables of interest. Finally, we conservatively only consider events greater than ten years in duration for our statistical modelling. These events are at an elevated risk of being artefacts of the null model rather than true downturns with an unclear driver.

We also collected information on the start and ends of downturns, their duration, elapsed time until SPD minima were reached, the cumulative number of a downturn and the frequency of downturns (Supplementary Table 2 ). The frequency of downturns is calculated on a per downturn basis within study regions, using the cumulative number of the downturn, normalized by its duration. We report frequency of downturns for each downturn as a logarithm per millennium.

Statistical modelling

The target of our comparative analysis is the resistance and resilience of human populations to disturbance as defined in each individual study. Our approach assumed that high values reflect resilient populations that successfully reestablish growth regimes after periods of decline related to disturbance events. We also assumed that downturns are randomly distributed in time and geographical space. To account for the influence of interregional and interevent cultural variation on outcomes, we drew on expert judgement and close readings of the published literature to record the broad category and specific type of disturbance during downturns, as well as the dominant land-use type and the nature of resulting socio-cultural changes, if any (Extended Data Table 1 ). These variables provided a control on whether a given population within a cultural system retained its identity and function over time, or whether system transformation and adaptive change is archaeologically evident.

Linear mixed-effect models were executed to evaluate the presence and strength of relationships between resistance, resilience, the recorded variables and case study locations. This analysis was performed using the cAIC4 and lme4 R packages 52 , 53 ; scripts are available at ref. 50 . Initial models were defined with resistance and resilience as response variables, with only case identifiers (region) as a random effect. As observed downturns were sequential within each case, the random effect controlled for potential pseudoreplication and avoided the need to weight the data by group size. A stepwise search using Akaike’s information criterion was implemented for investigating the information gain of including fixed effects in each model in turn. These candidate models were sequentially fitted using restricted maximum likelihood. Most fixed covariates (Extended Data Table 3 ) were left out of the final models. Region was retained as a random effect in all cases, to produce two models:

Model output is summarized in Extended Data Table 3 and diagnostics are shown in Extended Data Figs. 5 and 6 . We present standardized residuals, by region and in full, as well as leverage and Cook’s distance.

To further explore the relationship between rates of downturns and resistance and resilience, we performed an additional modelling exercise with the same random effect and full suite of fixed effects, with the frequency of downturn as the independent variable (Extended Data Table 4 and Extended Data Fig. 7 ).

The effect sizes (standardized coefficients) of the significant model terms are plotted graphically in Fig. 3c and reported in full in Extended Data Table 4 . We report effect sizes in the text as η 2 , that is, the total variance explained by differences between means.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The data supporting the findings of this study are available via Zenodo at https://doi.org/10.5281/zenodo.10061467 (ref. 53 ).

Code availability

The code supporting the findings of this study is available via Zenodo at https://doi.org/10.5281/zenodo.10061467 (ref. 53 ).

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Acknowledgements

We thank P. Lockwood for comments on an earlier version of this work. We acknowledge support from the Arts and Humanities Research Council grant AH/X002217/1 (P.R.), Samsung Electronics grant A0342-20220007 (J.B.), Leverhulme Trust grant no. PLP-2019–304 (E.C.) and the Youth Innovation Promotion Association of the Chinese Academy of Sciences grant YIPA-CAS, 2022149 (X.R.).

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Philip Riris & Fabio Silva

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Enrico Crema

Department of Historical Studies, University of Turin, Torino, Italy

Alessio Palmisano

Native Environment Solutions, Boise, ID, USA

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Division of Atmospheric Sciences, Desert Research Institute, Reno, NV, USA

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The Museum of Cultural History, University of Oslo, Oslo, Norway

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Contributions

P.R. and F.S. conceptualized the study. P.R., E.C., A.P. and F.S. developed the methodology. P.R., B.D., E.K.J., Y.O., A.P., X.R., E.R., P.E.S. and S.S. carried out the investigation. P.R., E.C. and F.S. did the analysis. P.R., J.C.F. and P.E.S. wrote the article. P.R., J.B., E.C., J.C.F., E.K.J., S.Y.M., A.P., E.R., P.E.S., F.S. and S.S. edited the article. P.R., S.Y.M. and E.R. carried out the visualization.

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Correspondence to Philip Riris .

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Extended data figures and tables

Extended data fig. 1 posterior predictive checks for 16 study regions..

Regions shaded in blue indicate period below modelled growth trajectories: downturns.

Extended Data Fig. 2 Traceplots of chain mixing for the MCMC of each study region and subset.

There is adequate mixing and convergence across chains.

Extended Data Fig. 3 Traceplots of chain mixing for the MCMC of each study region and subset.

Extended data fig. 4 resistance and resilience as functions of variation in minima ( x ) and end-points ( e ) when baselines ( b ) are held constant (0.02)..

( A ) Resistance equals zero for two different values of x , when x  = 0 and when x is two times b . Negative resistance is theoretically possible but does not occur on SPDs, ( B ) Resilience as a function of x , ( C ) Resilience with a varying end point ( e ), and constant start ( b ) and minimum ( x  = 0.01). Resilience equals 1 only when e = b , ( D ) Interpretative scatterplot of indicative resistance-resilience outcomes.

Extended Data Fig. 5 Model diagnostics for Model I (Resistance).

( A ) Standardised residuals against fitted values for each study region. ( B ) All residuals versus fitted values. ( C ) Standardised residuals by study region for n  = 154 independent samples across 16 regions. The lower and upper hinges correspond to the 25th and 75th percentiles The upper and lower whiskers extend from the hinges to 1.5 * IQR (where IQR is the inter-quartile range, distance between 25th and 75th percentiles). Data beyond the whiskers are individually plotted outlying points. The black points represent the group median. ( D ) Leverage and Cook’s Distance for 154 observations.

Extended Data Fig. 6 Model diagnostics for Model II (Resilience).

( A ) Standardised residuals against fitted values for each study region. ( B ) All residuals versus fitted values. ( C ) Standardised residuals by study region for n  = 154 independent samples across 16 regions. The lower and upper hinges correspond to the 25th and 75th percentiles The upper and lower whiskers extend from the hinges to 1.5 * IQR (where IQR is the inter-quartile range, distance between 25th and 75th percentiles). The black points represent the group median. Data beyond the whiskers are individually plotted outlying points. ( D ) Leverage and Cook’s Distance for 154 observations.

Extended Data Fig. 7 Model diagnostics for Model III.

Supplementary information, supplementary information, supplementary tables 1–18., peer review file, supplementary table 1.

Summary of attributes of regions used in this study.

Supplementary Table 2

Resistance and resilience metrics extracted from periods of population downturn detected in posterior predictive checks.

Supplementary Table 3

Radiocarbon data for Arid Zone Australia.

Supplementary Table 4

Radiocarbon data for Caribbean Archipelago.

Supplementary Table 5

Radiocarbon data for China Central Plains.

Supplementary Table 6

Radiocarbon data for Circumpolar Norway.

Supplementary Table 7

Radiocarbon data for Greece.

Supplementary Table 8

Radiocarbon data for Highland and Coastal Peru.

Supplementary Table 9

Radiocarbon data for Italy, Sardinia and Sicily.

Supplementary Table 10

Radiocarbon data for Korean Peninsula.

Supplementary Table 11

Radiocarbon data for Near East.

Supplementary Table 12

Radiocarbon data for Southeastern Norway.

Supplementary Table 13

Radiocarbon data for South Africa (GCFR).

Supplementary Table 14

Radiocarbon data for South Africa (SRZ).

Supplementary Table 15

Radiocarbon data for Tropical Lowlands.

Supplementary Table 16

Radiocarbon data for Utah.

Supplementary Table 17

Radiocarbon data for Wyoming.

Supplementary Table 18

Radiocarbon data for Yukon.

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Riris, P., Silva, F., Crema, E. et al. Frequent disturbances enhanced the resilience of past human populations. Nature (2024). https://doi.org/10.1038/s41586-024-07354-8

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Received : 06 December 2023

Accepted : 26 March 2024

Published : 01 May 2024

DOI : https://doi.org/10.1038/s41586-024-07354-8

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