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Course Code: BPCC 101 Assignment Code: Asst /TMA /2023-24 Total Marks: 100

Assignment One Answer the following descriptive category questions in about 500 words each. Each question carries 20 marks. 1. Discuss the stages and theoretical approaches to perception. 2. Explain the process and cognitive errors in decision making. 3. Discuss the nature, scope of learning and explain learning by association. Assignment Two Answer the following short category questions in about 100 words each. Each question carries 5 marks. 4. Nature and characteristics of behaviour 5. Perceptual constancy 6. Stages of creative thinking 7. Language in children 8. Latent learning 9. Types of motivation 10. Differentiate between emotion and mood 11. Cognitive appraisal theory of emotion

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Rethinking clinical decision-making to improve clinical reasoning

Salvatore corrao.

1 Department of Internal Medicine, National Relevance and High Specialization Hospital Trust ARNAS Civico, Palermo, Italy

2 Dipartimento di Promozione della Salute Materno Infantile, Medicina Interna e Specialistica di Eccellenza “G. D’Alessandro” (PROMISE), University of Palermo, Palermo, Italy

Christiano Argano

Associated data.

The original contributions presented in this study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Improving clinical reasoning techniques is the right way to facilitate decision-making from prognostic, diagnostic, and therapeutic points of view. However, the process to do that is to fill knowledge gaps by studying and growing experience and knowing some cognitive aspects to raise the awareness of thinking mechanisms to avoid cognitive errors through correct educational training. This article examines clinical approaches and educational gaps in training medical students and young doctors. The authors explore the core elements of clinical reasoning, including metacognition, reasoning errors and cognitive biases, reasoning strategies, and ways to improve decision-making. The article addresses the dual-process theory of thought and the new Default Mode Network (DMN) theory. The reader may consider the article a first-level guide to deepen how to think and not what to think, knowing that this synthesis results from years of study and reasoning in clinical practice and educational settings.

Introduction

Clinical reasoning is based on complex and multifaceted cognitive processes, and the level of cognition is perhaps the most relevant factor that impacts the physician’s clinical reasoning. These topics have inspired considerable interest in the last years ( 1 , 2 ). According to Croskerry ( 3 ) and Croskerry and Norman ( 4 ), over 40 affective and cognitive biases may impact clinical reasoning. In addition, it should not be forgotten that both the processes and the subject matter are complex.

In medicine, there are thousands of known diagnoses, each with different complexity. Moreover, in line with Hammond’s view, a fundamental uncertainty will inevitably fail ( 5 ). Any mistake or failure in the diagnostic process leads to a delayed diagnosis, a misdiagnosis, or a missed diagnosis. The particular context in which a medical decision is made is highly relevant to the reasoning process and outcome ( 6 ).

More recently, there has been renewed interest in diagnostic reasoning, primarily diagnostic errors. Many researchers deepen inside the processes underpinning cognition, developing new universal reasoning and decision-making model: The Dual Process Theory.

This theory has a prompt implementation in medical decision-making and provides a comprehensive framework for understanding the gamma of theoretical approaches taken into consideration previously. This model has critical practical applications for medical decision-making and may be used as a model for teaching decision reasoning. Given this background, this manuscript must be considered a first-level guide to understanding how to think and not what to think, deepening clinical decision-making and providing tools for improving clinical reasoning.

Too much attention to the tip of the iceberg

The New England Journal of Medicine has recently published a fascinating article ( 7 ) in the “Perspective” section, whereon we must all reflect on it. The title is “At baseline” (the basic condition). Dr. Bergl, from the Department of Medicine of the Medical College of Wisconsin (Milwaukee), raised that his trainees no longer wonder about the underlying pathology but are focused solely on solving the acute problem. He wrote that, for many internal medicine teams, the question is not whether but to what extent we should juggle the treatment of critical health problems of patients with care for their coexisting chronic conditions. Doctors are under high pressure to discharge, and then they move patients to the next stage of treatment without questioning the reason that decompensated the clinical condition. Suppose the chronic condition or baseline was not the fundamental goal of our performance. In that case, our juggling is highly inconsistent because we are working on an intermediate outcome curing only the decompensation phase of a disease. Dr. Bergl raises another essential matter. Perhaps equally disturbing, by adopting a collective “base” mentality, we unintentionally create a group of doctors who prioritize productivity rather than developing critical skills and curiosity. We agree that empathy and patience are two other crucial elements in the training process of future internists. Nevertheless, how much do we stimulate all these qualities? Perhaps are not all part of cultural backgrounds necessary for a correct patient approach, the proper clinical reasoning, and balanced communication skills?

On the other hand, a chronic baseline condition is not always the real reason that justifies acute hospitalization. The lack of a careful approach to the baseline and clinical reasoning focused on the patient leads to this superficiality. We are focusing too much on our students’ practical skills and the amount of knowledge to learn. On the other hand, we do not teach how to think and the cognitive mechanisms of clinical reasoning.

Time to rethink the way of thinking and teaching courses

Back in 1910, John Dewey wrote in his book “How We Think” ( 8 ), “The aim of education should be to teach us rather how to think than what to think—rather improve our minds to enable us to think for ourselves than to load the memory with the thoughts of other men.”

Clinical reasoning concerns how to think and make the best decision-making process associated with the clinical practice ( 9 ). The core elements of clinical reasoning ( 10 ) can be summarized in:

• 1. Evidence-based skills,
• 2. Interpretation and use of diagnostic tests,
• 3. Understanding cognitive biases,
• 4. Human factors,
• 5. Metacognition (thinking about thinking), and
• 6. Patient-centered evidence-based medicine.

All these core elements are crucial for the best way of clinical reasoning. Each of them needs a correct learning path to be used in combination with developing the best thinking strategies ( Table 1 ). Reasoning strategies allow us to combine and synthesize diverse data into one or more diagnostic hypotheses, make the complex trade-off between the benefits and risks of tests and treatments, and formulate plans for patient management ( 10 ).

Set of some reasoning strategies (view the text for explanations).

However, among the abovementioned core element of clinical reasoning, two are often missing in the learning paths of students and trainees: metacognition and understanding cognitive biases.

Metacognition

We have to recall cognitive psychology, which investigates human thinking and describes how the human brain has two distinct mental processes that influence reasoning and decision-making. The first form of cognition is an ancient mechanism of thought shared with other animals where speed is more important than accuracy. In this case, thinking is characterized by a fast, intuitive way that uses pattern recognition and automated processes. The second one is a product of evolution, particularly in human beings, indicated by an analytical and hypothetical-deductive slow, controlled, but highly consuming way of thinking. Today, the psychology of thinking calls this idea “the dual-process theory of thought” ( 11 – 14 ). The Nobel Prize in Economic Sciences awardee Daniel Kahneman has extensively studied the dichotomy between the two modes of thought, calling them fast and slow thinking. “System 1” is fast, instinctive, and emotional; “System 2” is slower, more deliberative, and more logical ( 15 ). Different cerebral zones are involved: “System 1” includes the dorsomedial prefrontal cortex, the pregenual medial prefrontal cortex, and the ventromedial prefrontal cortex; “System 2” encompasses the dorsolateral prefrontal cortex. Glucose utilization is massive when System 2 is performing ( 16 ). System 1 is the leading way of thought used. None could live permanently in a deliberate, slow, effortful way. Driving a car, eating, and performing many activities over time become automatic and subconscious.

A recent brilliant review of Gronchi and Giovannelli ( 17 ) explores those things. Typically, when a mental effort is required for tasks requiring attention, every individual is subject to a phenomenon called “ego-depletion.” When forced to do something, each one has fewer cognitive resources available to activate slow thinking and thus is less able to exert self-control ( 18 , 19 ). In the same way, much clinical decision-making becomes intuitive rather than analytical, a phenomenon strongly affected by individual differences ( 20 , 21 ). Experimental evidence by functional magnetic resonance imaging and positron emission tomography studies supports that the “resting state” is spontaneously active during periods of “passivity” ( 22 – 25 ). The brain regions involved include the medial prefrontal cortex, the posterior cingulate cortex, the inferior parietal lobule, the lateral temporal cortex, the dorsal medial prefrontal cortex, and the hippocampal formation ( 26 ). Findings reporting high-metabolic activity in these regions at rest ( 27 ) constituted the first clear evidence of a cohesive default mode in the brain ( 28 ), leading to the widely acknowledged introduction of the Default Mode Network (DMN) concept. The DMN contains the medial prefrontal cortex, the posterior cingulate cortex, the inferior parietal lobule, the lateral temporal cortex, the dorsal medial prefrontal cortex, and the hippocampal formation. Lower activity levels characterize the DMN during goal-directed cognition and higher activity levels when an individual is awake and involved in the mental processes requiring low externally directed attention. All that is the neural basis of spontaneous cognition ( 26 ) that is responsible for thinking using internal representations. This paradigm is growing the idea of stimulus-independent thoughts (SITs), defined by Buckner et al. ( 26 ) as “thoughts about something other than events originating from the environment” that is covert and not directed toward the performance of a specific task. Very recently, the role of the DMN was highlighted in automatic behavior (the rapid selection of a response to a particular and predictable context) ( 29 ), as opposed to controlled decision making, suggesting that the DMN plays a role in the autopilot mode of brain functioning.

In light of these premises, everyone can pause to analyze what he is doing, improving self-control to avoid “ego-depletion.” Thus, one can actively switch between one type of thinking and the other. The ability to make this switch makes the physician more performing. In addition, a physician can be trained to understand the ways of thinking and which type of thinking is engaged in various situations. This way, experience and methodology knowledge can energize Systems 1 and 2 and how they interact, avoiding cognitive errors. Figure 1 summarizes all the concepts abovementioned about the Dual Mode Network and its relationship with the DMN.

Graphical representation of the characteristics of Dual Mode Network, including the relationship between the two systems by Default Mode Network (view the text for explanations).

Emotional intelligence is another crucial factor in boosting clinical reasoning for the best decision-making applied to a single patient. Emotional intelligence recognizes one’s emotions. Those others label different feelings appropriately and use emotional information to guide thinking and behavior, adjust emotions, and create empathy, adapt to environments, and achieve goals ( 30 ). According to the phenomenological account of Fuchs, bodily perception (proprioception) has a crucial role in understanding others ( 31 ). In this sense, the proprioceptive skills of a physician can help his empathic understanding become elementary for empathy and communication with the patient. In line with Fuchs’ view, empathic understanding encompasses a bodily resonance and mediates contextual knowledge about the patient. For medical education, empathy should help to relativize the singular experience, helping to prevent that own position becomes exclusive, bringing oneself out of the center of one’s own perspective.

Reasoning errors and cognitive biases

Errors in reasoning play a significant role in diagnostic errors and may compromise patient safety and quality of care. A recently published review by Norman et al. ( 32 ) examined clinical reasoning errors and how to avoid them. To simplify this complex issue, almost five types of diagnostic errors can be recognized: no-fault errors, system errors, errors due to the knowledge gap, errors due to misinterpretation, and cognitive biases ( 9 ). Apart from the first type of error, which is due to unavoidable errors due to various factors, we want to mention cognitive biases. They may occur at any stage of the reasoning process and may be linked to intuition and analytical systems. The most frequent cognitive biases in medicine are anchoring, confirmation bias, premature closure, search satisficing, posterior probability error, outcome bias, and commission bias ( 33 ). Anchoring is characterized by latching onto a particular aspect at the initial consultation, and then one refuses to change one’s mind about the importance of the later stages of reasoning. Confirmation bias ignores the evidence against an initial diagnosis. Premature closure leads to a misleading diagnosis by stopping the diagnostic process before all the information has been gathered or verified. Search satisficing blinds other additional diagnoses once the first diagnosis is made posterior probability error shortcuts to the usual patient diagnosis for previously recognized clinical presentations. Outcome bias impinges on our desire for a particular outcome that alters our judgment (e.g., a surgeon blaming sepsis on pneumonia rather than an anastomotic leak). Finally, commission bias is the tendency toward action rather than inaction, assuming that only good can come from doing something (rather than “watching and waiting”). These biases are only representative of the other types, and biases often work together. For example, in overconfidence bias (the tendency to believe we know more than we do), too much faith is placed in opinion instead of gathered evidence. This bias can be augmented by the anchoring effect or availability bias (when things are at the forefront of your mind because you have seen several cases recently or have been studying that condition in particular), and finally by commission bias—with disastrous results.

Novice vs. expert approaches

The reasoning strategies used by novices are different from those used by experts ( 34 ). Experts can usually gather beneficial information with highly effective problem-solving strategies. Heuristics are commonly, and most often successfully, used. The expert has a saved bank of illness scripts to compare and contrast the current case using more often type 1 thinking with much better results than the novice. Novices have little experience with their problems, do not have time to build a bank of illness scripts, and have no memories of previous similar cases and actions in such cases. Therefore, their mind search strategies will be weak, slow, and ponderous. Heuristics are poor and more often unsuccessful. They will consider a more comprehensive range of diagnostic possibilities and take longer to select approaches to discriminate among them. A novice needs specific knowledge and specific experience to become an expert. In our opinion, he also needs special training in the different ways of thinking. It is possible to study patterns, per se as well. It is, therefore, likely to guide the growth of knowledge for both fast thinking and slow one.

Moreover, learning by osmosis has traditionally been the method to move the novice toward expert capabilities by gradually gaining experience while observing experts’ reasoning. However, it seems likely that explicit teaching of clinical reasoning could make this process quicker and more effective. In this sense, an increased need for training and clinical knowledge along with the skill to apply the acquired knowledge is necessary. Students should learn disease pathophysiology, treatment concepts, and interdisciplinary team communication developing clinical decision-making through case-series-derived knowledge combining associative and procedural learning processes such as “Vienna Summer School on Oncology” ( 35 ).

Moreover, a refinement of the training of communicative skills is needed. Improving communication skills training for medical students and physicians should be the university’s primary goal. In fact, adequate communication leads to a correct diagnosis with 76% accuracy ( 36 ). The main challenge for students and physicians is the ability to respond to patients’ individual needs in an empathic and appreciated way. In this regard, it should be helpful to apply qualitative studies through the adoption of a semi-structured or structured interview using face-to-face in-depth interviews and e-learning platforms which can foster interdisciplinary learning by developing expertise for the clinical reasoning and decision-making in each area and integrating them. They could be effective tools to develop clinical reasoning and decision-making competencies and acquire effective communication skills to manage the relationship with patient ( 37 – 40 ).

Clinical reasoning ways

Clinical reasoning is complex: it often requires different mental processes operating simultaneously during the same clinical encounter and other procedures for different situations. The dual-process theory describes how humans have two distinct approaches to decision-making ( 41 ). When one uses heuristics, fast-thinking (system 1) is used ( 42 ). However, complex cases need slow analytical thinking or both systems involved ( 15 , 43 , 44 ). Slow thinking can use different ways of reasoning: deductive, hypothetic-deductive, inductive, abductive, probabilistic, rule-based/categorical/deterministic, and causal reasoning ( 9 ). We think that abductive and causal reasoning need further explanation. Abductive reasoning is necessary when no deductive argument (from general assumption to particular conclusion) nor inductive (the opposite of deduction) may be claimed.

In the real world, we often face a situation where we have information and move backward to the likely cause. We ask ourselves, what is the most plausible answer? What theory best explains this information? Abduction is just a process of choosing the hypothesis that would best explain the available evidence. On the other hand, causal reasoning uses knowledge of medical sciences to provide additional diagnostic information. For example, in a patient with dyspnea, if considering heart failure as a casual diagnosis, a raised BNP would be expected, and a dilated vena cava yet. Other diagnostic possibilities must be considered in the absence of these confirmatory findings (e.g., pneumonia). Causal reasoning does not produce hypotheses but is typically used to confirm or refute theories generated using other reasoning strategies.

Hypothesis generation and modification using deduction, induction/abduction, rule-based, causal reasoning, or mental shortcuts (heuristics and rule of thumbs) is the cognitive process for making a diagnosis ( 9 ). Clinicians develop a hypothesis, which may be specific or general, relating a particular situation to knowledge and experience. This process is referred to as generating a differential diagnosis. The process we use to produce a differential diagnosis from memory is unclear. The hypotheses chosen may be based on likelihood but might also reflect the need to rule out the worst-case scenario, even if the probability should always be considered.

Given the complexity of the involved process, there are numerous causes for failure in clinical reasoning. These can occur in any reasoning and at any stage in the process ( 33 ). We must be aware of subconscious errors in our thinking processes. Cognitive biases are subconscious deviations in judgment leading to perceptual distortion, inaccurate assessment, and misleading interpretation. From an evolutionary point of view, they have developed because, often, speed is more important than accuracy. Biases occur due to information processing heuristics, the brain’s limited capacity to process information, social influence, and emotional and moral motivations.

Heuristics are mind shortcuts and are not all bad. They refer to experience-based techniques for decision-making. Sometimes they may lead to cognitive biases (see above). They are also essential for mental processes, expressed by expert intuition that plays a vital role in clinical practice. Intuition is a heuristic that derives from a natural and direct outgrowth of experiences that are unconsciously linked to form patterns. Pattern recognition is just a quick shortcut commonly used by experts. Alternatively, we can create patterns by studying differently and adequately in a notional way that accumulates information. The heuristic that rules out the worst-case scenario is a forcing mind function that commits the clinician to consider the worst possible illness that might explain a particular clinical presentation and take steps to ensure it has been effectively excluded. The heuristic that considers the least probable diagnoses is a helpful approach to uncommon clinical pictures and thinking about and searching for a rare unrecognized condition. Clinical guidelines, scores, and decision rules function as externally constructed heuristics, usually to ensure the best evidence for the diagnosis and treatment of patients.

Hence, heuristics are helpful mind shortcuts, but the exact mechanisms may lead to errors. Fast-and-frugal tree and take-the-best heuristic are two formal models for deciding on the uncertainty domain ( 45 ).

In the recent times, clinicians have faced dramatic changes in the pattern of patients acutely admitted to hospital wards. Patients become older and older with comorbidities, rare diseases are frequent as a whole ( 46 ), new technologies are growing in a logarithmic way, and sustainability of the healthcare system is an increasingly important problem. In addition, uncommon clinical pictures represent a challenge for clinicians ( 47 – 50 ). In our opinion, it is time to claim clinical reasoning as a crucial way to deal with all complex matters. At first, we must ask ourselves if we have lost the teachings of ancient masters. Second, we have to rethink medical school courses and training ones. In this way, cognitive debiasing is needed to become a well-calibrated clinician. Fundamental tools are the comprehensive knowledge of nature and the extent of biases other than studying cognitive processes, including the interaction between fast and slow thinking. Cognitive debiasing requires the development of good mindware and the awareness that one debiasing strategy will not work for all biases. Finally, debiasing is generally a complicated process and requires lifelong maintenance.

We must remember that medicine is an art that operates in the field of science and must be able to cope with uncertainty. Managing uncertainty is the skill we have to develop against an excess of confidence that can lead to error. Sound clinical reasoning is directly linked to patient safety and quality of care.

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SC and CA drafted the work and revised it critically. Both authors have approved the submission of the manuscript.

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Cognitive Biases and Errors in Decision Making - Explained

What are Biases and Errors in Decision Making?

Written by Jason Gordon

Updated at August 21st, 2023

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What are the Common Biases & Errors in Decision-Making?

Some common decision-making errors and biases are as follows:

Overconfidence Bias

Individuals overestimate or have excessive confidence in their ability to predict or foresee future events. This will cause the decision maker to unsupported or risky decisions.

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• Hindsight Bias

This is the tendency of individuals to see past mistakes or occurrences as obvious. After the event has occurred, individuals believe that they did or should have seen it coming. This is important when evaluating others decisions.

Anchoring Effect

Anchoring is when someone attaches themselves to an initial bit of information. In decision-making, it entails people placing too much emphasis on the single piece of information. This can cause the decision maker to fail to consider other important information.

Framing Bias

Framing bias is an individuals response to how a situation or decision is presented. This can lead to individuals being deceived or manipulated by third parties.

Escalation of Commitment

This is a tendency of individuals to continue to follow what has proven to be a negative or unproductive course of action. Also known as the sunk cost fallacy or sunk cost bias, because the tendency is motivated by an unwillingness to admit that they are wrong or accept that resources are lost or wasted (they may be able to recover the investment).

Immediate Gratification

This is the tendency to make the immediacy of a potential solution to a problem or situation the most important criteria. The result is the failure to consider all available options and settling for a sub-part outcome form a decision that fails to deliver all available value.

Selective Perception

This is the tendency to see a particular situation or issue from a chosen perspective. This is related to the team-based mentality. We see all situations or issues through a common lens that influences our ability to understand alternative or conflicting points of view or alternatives.

• Confirmation Bias

Confirmation bias is to actively look for information or facts in a situation that supports a particular choice or decision. This approach causes the decision maker to ignore evidence to the contrary. This can also cause a failure to consider contrary information of positions.

• Availability Bias

Availability bias is a focus on immediate information or situations that come to ones mind. The result is that we tend to believe the information or experience that we recall or demonstrative or explicative of a situation or scenario. This comes at the expense of looking for additional information that could lead to a further understanding of the situation. As such, a decision is made on limited or superficial information.

Representation Bias

This is the tendency to believe a situation is indicative of a greater tendency. That is, it is related to stereotyping. The decision maker believes that the situation represents all of the characteristics of the population of which it is a part. It causes a failure in the perception of ones ability to predict a given outcome or result.

Randomness Bias

This is the tendency to see a pattern in otherwise random data or information. We increasingly seek to harness new sources of information in the decision-making process. Our search for meaning in information leads to an unreasonable reliance on insignificant results.

Self-Serving Bias

This is ones tendency to attribute the positive results of a decision or situation to ones own actions or decision. Likewise, it causes individuals to attribute negative consequences to factors outside of our control. This can cause an inability to accurately assess or affect a situation through decision making.

Fundamental Attribution Error

The tendency for people to over-emphasize personality-based explanations for behaviors observed in others while under-emphasizing the role and power of situational influences on the same behavior.

Rationalization

The process of constructing a logical justification for a decision that was originally arrived at through a non-rational decision process. Can be conscious, but is mostly subconscious.

Bandwagon Effect

The tendency to do (or believe) things because many other people do (or believe) the same.

Status Quo Bias

The tendency for people to like things to stay relatively the same. The preference towards alternatives that maintain or perpetuate the current situation even when better alternatives exist.

Illusion of Control

The tendency for human beings to believe they can control or at least influence outcomes which they clearly cannot.

Prudence Trap

When faced with high-stakes decisions, we tend to adjust our estimates or forecast to be "on the safe side".

Recallability Trap

Giving undue weight to recent, dramatic events.

Sunk Cost Bias

To make choices in a way that justifies past choices, even when the past choices no longer seem valid.

Loss Aversion

The tendency for people to strongly prefer avoiding losses than acquiring gains.

• Survivorship Bias

For example, the frequent mistake to forget to include companies that no longer exist in research reports studying various forms of corporate performance.

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Decision-Making Mistakes Related to Bias

In their book Decision Traps , Russo and Shoemaker reveal the ten most common mistakes in decision-making , many of which are related to cognitive bias:

• Plunging In : Beginning to gather information and reach conclusions too early.
• Frame Blindness : Creating a mental framework for your decision.
• Lack of Frame Control : Failing to define the problem in more than one way.
• Overconfidence in Your Judgment : Failing to gather key factual information.
• Shortsighted Shortcuts : Relying inappropriately on “rules of thumb”.
• Shooting from the Hip : Failing to follow a systematic procedure when making the final decision.
• Group Failure : Failing to manage the group decision-making process.
• Fooling Yourself About Feedback : Failing to interpret the evidence from past outcomes correctly.
• Not Keeping Track : Failing to keep systematic records to track the results of your decisions.
• Failure to Audit Your Decision Process : Failing to create an organized approach to understand your own decision-making.

Related Topics

• Judgment Noise
• Satisficing
• Base Rate Fallacy
• Path Dependence
• Outcome Bias 
• Cognitive Dissonance
• Horn Effect Bias
• Just World Effect
• Paralysis by Analysis

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BPCC 101 Solved Assignment 2023-24

INTRODUCTION TO PSYCHOLOGY

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Assignment Code: ASST/ BPCC 101/2023-24

Assignment One

Answer the following in about 500 words each.

Q1. Discuss the stages and theoretical approaches to perception.

Q2. Explain the process and cognitive errors in decision making.

Q3. Discuss the nature, scope of learning and explain learning by association.

Assignment Two

Answer the following in about 100 words each.

Q4. Nature and characteristics of behaviour

Q5. Perceptual constancy

Q6. Stages of creative thinking

Q7. Language in children

Q8. Latent learning

Q9. Types of motivation

Q10. Differentiate between emotion and mood

Q11. Cognitive appraisal theory of emotion

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BPCS 186: IGNOU BAG Solved Assignment 2022-2023

• 1 Assignment One
• 2 Answer the following questions in about 500 words each. Each question carries 20 marks.
• 3 1. Explain the various models of stress with the help of suitable diagrams.
• 4 2. Describe the effect of stress on performance and productivity.
• 5 3. Explain the nature of coping and describe the coping styles.
• 6 Assignment Two
• 7 Answer the following questions in about 100 words each. Each question carries 5 marks.
• 8 4. Explain the various sources of stress with the help of suitable examples.
• 9 5. Describe perfectionism as a factor contributing to stress proneness.
• 10 6. Explain the Jacobson’s Progressive Muscle Relaxation.
• 11 7. Describe Yoga as a technique of stress management.
• 12 8. Discuss ABCDE model with the help of suitable example.
• 13 9. Describe various techniques of time management.
• 14 10. Explain effective communication as an interpersonal skill.
• 15.1 How to Download BPCS 186 Solved Assignment?
• 15.2 Is the BPCS 186 Solved Assignment Free?
• 15.3 What is the last submission date for BPCS 186 Solved Assignment?

Assignment One

Answer the following questions in about 500 words each. each question carries 20 marks., 1. explain the various models of stress with the help of suitable diagrams..

Ans: Stress can be defined as the physical, emotional, or psychological response to a stressor, which is any event or situation that causes stress. There are various models of stress that have been proposed over the years to help understand the nature and dynamics of stress. In this essay, we will discuss three of the most widely recognized models of stress: the transactional model, the general adaptation syndrome (GAS) model, and the cognitive appraisal model.

• The Transactional Model of Stress: The transactional model of stress was proposed by Richard Lazarus and Susan Folkman in 1984. According to this model, stress is a result of the interaction between an individual and their environment. The model proposes that stress is not just a response to an external stimulus but is also a result of the individual’s cognitive appraisal of the situation. The model consists of two stages: primary appraisal and secondary appraisal.

Primary Appraisal: The primary appraisal is the initial evaluation of the situation, which determines whether it is stressful or not. In this stage, the individual evaluates the situation based on its potential to harm, challenge, or benefit them. If the situation is seen as a threat or a challenge, it is likely to be considered stressful.

Secondary Appraisal : The secondary appraisal occurs after the individual has determined that the situation is stressful. In this stage, the individual evaluates their ability to cope with the situation. If the individual feels that they have the resources to cope, the stress response may be reduced, but if they feel they do not have the resources to cope, the stress response may be increased.

• The General Adaptation Syndrome (GAS) Model: The general adaptation syndrome (GAS) model was proposed by Hans Selye in the 1950s. According to this model, stress causes a physiological response in the body that can be divided into three stages: the alarm reaction stage, the resistance stage, and the exhaustion stage.

Alarm Reaction Stage: The alarm reaction stage is the initial stage of the body’s response to stress. During this stage, the body activates its fight or flight response, which is the body’s natural response to stress. This stage is characterized by an increase in heart rate, blood pressure, and respiration rate.

Resistance Stage: The resistance stage occurs when the body tries to adapt to the stressor. During this stage, the body tries to maintain a state of readiness to respond to the stressor. The body releases hormones such as cortisol and adrenaline, which help the body to maintain its level of energy.

Exhaustion Stage: The exhaustion stage occurs when the body’s resources are depleted due to prolonged exposure to stress. During this stage, the body’s immune system becomes weakened, and the individual becomes more susceptible to illness and disease.

• The Cognitive Appraisal Model: The cognitive appraisal model was proposed by Richard Lazarus in the 1960s. According to this model, stress is a result of an individual’s cognitive appraisal of a situation. The model consists of two stages: primary appraisal and secondary appraisal.

Secondary Appraisal: The secondary appraisal occurs after the individual has determined that the situation is stressful. In this stage, the individual evaluates their ability to cope with the situation. If the individual feels that they have the resources to cope, the stress response may be reduced, but if they feel they do not have the resources to cope, the stress response may be increased.

2. Describe the effect of stress on performance and productivity.

Ans: Stress can have a significant impact on an individual’s performance and productivity. While a certain level of stress can be motivating and help individuals perform better, chronic or excessive stress can have detrimental effects on performance and productivity. In this essay, we will discuss the effects of stress on performance and productivity in more detail.

• Cognitive Functioning: Stress can negatively impact cognitive functioning, including attention, memory, and decision-making. When an individual is under stress, the body releases hormones such as cortisol, which can interfere with the functioning of the prefrontal cortex, the part of the brain responsible for cognitive functions. As a result, individuals may have difficulty focusing, recalling information, and making decisions.
• Work Quality: Excessive stress can also affect the quality of work an individual produces. When individuals are stressed, they may rush through tasks or make mistakes, which can negatively impact the quality of their work. Additionally, stress can affect an individual’s creativity, making it more difficult for them to come up with new ideas or solutions.
• Physical Symptoms: Stress can also manifest in physical symptoms such as headaches, fatigue, and muscle tension, which can negatively impact an individual’s ability to perform work tasks. These physical symptoms can also lead to increased absenteeism, which can further reduce productivity.
• Interpersonal Relations : Stress can also negatively impact interpersonal relations, which can have an indirect impact on performance and productivity. When individuals are stressed, they may become irritable, moody, or withdrawn, which can affect their relationships with coworkers or customers. This can lead to a decrease in job satisfaction and can also impact the quality of work produced.
• Burnout: Chronic stress can lead to burnout, a state of physical and emotional exhaustion characterized by feelings of cynicism and a reduced sense of accomplishment. Burnout can lead to decreased motivation and productivity, and can also increase the likelihood of absenteeism and turnover.

3. Explain the nature of coping and describe the coping styles.

Ans: Coping can be defined as the process of dealing with, managing, and adapting to stress, adversity, or difficult situations. Coping is a vital aspect of human functioning, as it helps individuals to handle and overcome the challenges they face in life. Coping can be seen as a response to stress, and it involves both cognitive and behavioral efforts aimed at reducing the negative impact of a stressful situation. Coping can be seen as a dynamic process that is influenced by a range of factors, including the individual’s personality, the nature of the stressor, the social support available to the individual, and the coping strategies that are employed.

Coping styles can be described as the different ways in which individuals respond to stress and difficult situations. There are several coping styles that individuals may employ, and these can be broadly categorized into two main types: problem-focused coping and emotion-focused coping. Problem-focused coping involves efforts to directly address the problem or stressor, while emotion-focused coping involves efforts to manage the emotions associated with the stressor.

Problem-focused coping is an active and problem-solving-oriented approach to coping. This type of coping style involves taking concrete steps to address the problem or stressor. For example, problem-focused coping may involve seeking information about the stressor, developing a plan of action, and taking steps to implement that plan. This coping style is often effective in situations where the individual has some control over the stressor, and where the stressor can be addressed through concrete actions.

Emotion-focused coping, on the other hand, is a more passive and emotion-regulation-oriented approach to coping. This type of coping style involves efforts to manage the emotional response to the stressor, rather than directly addressing the stressor itself. Emotion-focused coping may involve strategies such as cognitive reappraisal, seeking social support, or engaging in activities that distract from the stressor. This coping style is often effective in situations where the individual has little control over the stressor, or where the stressor cannot be directly addressed.

In addition to these broad categories of coping styles, there are also several specific coping strategies that individuals may employ. These strategies include problem-solving, planning, positive reappraisal, acceptance, avoidance, denial, and venting. Problem-solving and planning involve actively addressing the stressor and developing a plan to address it. Positive reappraisal involves finding positive aspects of the stressor or situation. Acceptance involves accepting the stressor and finding ways to cope with it. Avoidance involves avoiding the stressor or situation altogether. Denial involves denying that the stressor exists or minimizing its impact. Venting involves expressing emotions related to the stressor.

Assignment Two

Answer the following questions in about 100 words each. each question carries 5 marks., 4. explain the various sources of stress with the help of suitable examples..

Ans: Stress can originate from various sources, including environmental, psychological, and physiological factors. Environmental stressors include noise pollution, traffic, and crowding. For instance, living in a noisy and polluted urban area can lead to stress due to constant exposure to loud noises and poor air quality. Psychological stressors involve interpersonal conflict, job pressure, and financial stress. For example, a worker may experience stress when facing a high workload, tight deadlines, or job insecurity. Physiological stressors include illness, injury, or physical exhaustion. For instance, an athlete may experience stress when trying to perform at a high level while dealing with pain or fatigue. Understanding the different sources of stress is critical to developing effective stress management strategies.

5. Describe perfectionism as a factor contributing to stress proneness.

Ans: Perfectionism is a personality trait characterized by the pursuit of high standards and the tendency to be overly critical of one’s own performance. While striving for excellence can be motivating, perfectionism can lead to stress proneness. Perfectionists tend to set unrealistic expectations for themselves, leading to feelings of inadequacy and self-doubt when they cannot meet these standards. They may engage in constant self-criticism and rumination over perceived failures, leading to negative emotions such as anxiety and depression. Perfectionists may also experience stress when faced with ambiguous situations or when things do not go according to plan. To manage stress related to perfectionism, it is essential to practice self-compassion, realistic goal-setting, and learning to accept mistakes as a natural part of the learning process.

6. Explain the Jacobson’s Progressive Muscle Relaxation.

Ans: Jacobson’s Progressive Muscle Relaxation is a relaxation technique that involves the systematic tensing and relaxing of muscle groups throughout the body. The goal of this technique is to help individuals become more aware of their physical sensations and to reduce muscle tension, which is often associated with stress and anxiety.

The technique involves two main steps. In the first step, the individual tenses a specific muscle group for about 5-10 seconds, such as clenching their fists or tightening their shoulders. In the second step, the individual releases the tension and allows the muscles to relax completely for about 10-20 seconds. The process is repeated with different muscle groups throughout the body, starting from the feet and working up to the head.

By regularly practicing Jacobson’s Progressive Muscle Relaxation, individuals can learn to recognize when they are experiencing muscle tension and can apply the technique to help alleviate the physical symptoms of stress and anxiety. The technique is easy to learn and can be practiced almost anywhere, making it a popular relaxation technique for those looking to manage stress and anxiety.

7. Describe Yoga as a technique of stress management.

Ans: Yoga is a technique of stress management that involves physical postures, breathing exercises, and meditation. Yoga is designed to promote relaxation, reduce stress, and improve overall well-being. The practice of yoga involves a combination of physical movements and mental focus, which can help individuals to become more aware of their physical and mental states and to manage their stress response more effectively.

Yoga can help to reduce stress by promoting physical relaxation and reducing muscle tension. The physical postures, or asanas, are designed to stretch and strengthen the body, which can help to alleviate physical symptoms of stress such as tension headaches, back pain, and muscle tightness. The breathing exercises, or pranayama, can help to calm the mind and promote relaxation, which can help to reduce feelings of anxiety and stress.

Yoga also incorporates meditation techniques, which can help individuals to become more aware of their thoughts and emotions and to develop a more positive outlook on life. By practicing mindfulness and focusing on the present moment, individuals can learn to manage their stress response more effectively and to develop a greater sense of inner peace and well-being.

8. Discuss ABCDE model with the help of suitable example.

Ans: The ABCDE model is a cognitive behavioral therapy (CBT) technique that helps individuals to identify and challenge negative thoughts and beliefs that may be contributing to their stress or emotional distress. The model involves breaking down negative thoughts and beliefs into five main components:

A – Activating event : The event or situation that triggered the negative thought or belief.

B – Beliefs : The negative thought or belief about the event or situation.

C – Consequences : The emotional and behavioral consequences of the negative thought or belief.

D – Dispute : The process of challenging and disputing the negative thought or belief.

E – Effects : The effects of disputing the negative thought or belief, including a more positive and realistic outlook.

For example, let’s say someone receives negative feedback on a work project (activating event). They may have the negative belief that they are not good enough and will never succeed in their job (belief). This negative belief can lead to feelings of anxiety and self-doubt (consequences). Using the ABCDE model, the individual can challenge and dispute this belief by asking questions such as “Is it really true that I am not good enough?” or “What evidence do I have to support this belief?” (dispute). By doing so, they may come to realize that this negative belief is not entirely accurate or helpful (effects). They may then be able to develop a more positive and realistic belief, such as “I may have made some mistakes, but I can learn from them and improve in the future.”

Overall, the ABCDE model is a useful tool for identifying and challenging negative thoughts and beliefs, which can help individuals to manage their stress and improve their emotional well-being.

9. Describe various techniques of time management.

Ans: Effective time management involves prioritizing tasks, setting realistic goals, and allocating time to different activities based on their level of importance and urgency. Here are some techniques that can help with time management:

• To-do lists: Create a daily or weekly to-do list that outlines the tasks that need to be accomplished. This can help to prioritize tasks and ensure that nothing is forgotten.
• Prioritization : Determine the level of importance and urgency of each task and prioritize them accordingly. This can help to ensure that the most important tasks are completed first.
• Time blocking : Allocate specific time slots for different tasks, such as work-related tasks, exercise, family time, and hobbies. This can help to ensure that enough time is dedicated to each activity.
• Pomodoro technique : Break tasks into 25-minute intervals, with short breaks in between. This can help to improve focus and productivity.
• Delegation : Delegate tasks to others, when possible, to free up time for other important tasks.
• Avoid multitasking : Focus on one task at a time, as multitasking can lead to decreased productivity and increased stress.
• Set realistic goals : Set realistic goals that can be achieved within a specific time frame. This can help to avoid feeling overwhelmed and increase motivation.
• Learn to say no : It is important to recognize when saying yes to a request will interfere with other important tasks. Learning to say no can help to prioritize tasks and manage time effectively.

10. Explain effective communication as an interpersonal skill.

Ans: Effective communication is a crucial interpersonal skill that involves the ability to convey ideas, thoughts, and feelings in a clear, concise, and respectful manner. It involves the exchange of information between two or more people and is essential for building and maintaining positive relationships with others.

Effective communication involves several key components, including:

• Listening : Active listening involves paying attention to what others are saying and showing an interest in their thoughts and feelings.
• Clarity : Communicating in a clear and concise manner, using simple and easy-to-understand language.
• Empathy : Understanding and considering the other person’s perspective, thoughts, and feelings.
• Non – verbal communication : Paying attention to non-verbal cues, such as body language, tone of voice, and facial expressions.
• Feedback : Providing feedback in a constructive and respectful manner, while being open to receiving feedback from others.
• Respect : Communicating in a respectful and courteous manner, while avoiding judgment, criticism, or negativity.

Effective communication is essential for building strong interpersonal relationships, both in personal and professional settings. It can help to resolve conflicts, establish trust, and promote collaboration and teamwork. By developing effective communication skills, individuals can improve their ability to express their thoughts and feelings, as well as understand and appreciate the perspectives of others.

11. Explain JOHARI window.

Ans: The Johari Window is a model used to help individuals better understand their communication and relationships with others. It was developed by psychologists Joseph Luft and Harry Ingham in 1955. The model is named after its creators (Johari being a combination of their first names Joe and Harry).

The model is a four-quadrant grid , with the dimensions being the information known to oneself (what is known to me) and the information known to others (what is known to others) . The quadrants are:

• Open Area or Arena: This quadrant represents the information that is known to both the individual and others. It includes things like strengths, weaknesses, skills, experiences, and knowledge that are shared openly.
• Blind Area or Blindspot: This quadrant represents the information that is not known to the individual but is known to others. It includes things like habits, behaviors, or attitudes that the individual may be unaware of, but that others can see.
• Hidden Area or Façade: This quadrant represents the information that is known to the individual but is not shared with others. It includes things like fears, insecurities, and other personal information that the individual may choose to keep private.
• Unknown Area or Unknown: This quadrant represents the information that is unknown to both the individual and others. It includes things like undiscovered talents, repressed emotions, or other aspects of the individual that are yet to be revealed.

The goal of the JOHARI Window is to increase the size of the open area, which can be achieved through increased self-awareness and effective communication with others. By sharing more about themselves with others, individuals can reduce the size of the hidden area and increase the size of the open area. Similarly, by receiving feedback from others, individuals can reduce the size of the blind area and increase their self-awareness.

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Perspectives and Trends in Education and Technology pp 185–197 Cite as

Cognitive Biases in the Investment Decision Process

• Patrick Silva 7 ,
• Jorge Mendonça   ORCID: orcid.org/0000-0002-8359-139X 8 ,
• Luís M. P. Gomes   ORCID: orcid.org/0000-0001-9792-1049 9 &
• Lurdes Babo   ORCID: orcid.org/0000-0001-5090-8736 9  
• Conference paper
• First Online: 03 January 2023

698 Accesses

1 Citations

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 320))

Behavioral finance aims to understand the reasoning patterns of investors, constrained by emotional processes, and how they influence the decision-making process. The main purpose of this work is to evaluate individuals’ decision-making behavior under risk/uncertainty. The methodological procedure adopted exploratory research with data collection through a questionnaire grounded by the prospect theory with 329 valid responses from individuals in Portugal. The results are aligned with those obtained in Kahneman and Tversky's study on the effects of prospect theory. Moreover, they support different behaviors between non-investors (more risk-averse) and investors (more risk-prone), and consistency is observed between those familiar or not with the concept of behavioral finance. Overall, individuals’ decision-making behavior seems to be influenced by cognitive biases and the intuitive system. The findings are important because they highlight the importance of strategic financial literacy plans.

• Behavioral finance
• Decision making
• Prospect theory

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Lobão, J.: Finanças Comportamentais; Almedina (2012)

Google Scholar  

Kahneman, D., Tversky, A.: Prospect thoery. Econometrica 47 (2), 263–292 (1979)

Article   MathSciNet   MATH   Google Scholar  

Ruggeri, K., Alí, S., Berge, M.L., Bertoldo, G., Bjørndal, L.D., Cortijos-Bernabeu, A., Gibson, S.P.: Replicating patterns of prospect theory for decision under risk. Nat. Hum. Behav. 4 , 622–633 (2020). https://doi.org/10.1038/s41562-020-0886-x

Article   Google Scholar  

Baker, H.K., Nofsinger, J.R.: Behavioral Finance: Investors, Corporations, and Markets. Wiley (2010)

Kaizer, D.L., Rodrigues, E.L., Ferreira, A.R.: Decisões Financeiras à Luz Da Economia Comportamental: Estudo Comparativo Dos Alunos Dos Cursos de Economia e Administração de Uma Universidade Federal Brasileira. Rev. Fac. Ciencias Econ. 29 (2), 225–248 (2021). https://doi.org/10.18359/rfce.5551 .

Carneiro, P.E.A.: Limites Na Racionalidade Das Análises de Risco Soberano: Testes Econométricos, Erros, Finanças Comportamentais e Noise Rater Risk, Universidade de Brasília (2006)

Silva, W.V., Del Corso, J.M., da Silva, S.M., de Oliveira, E.: Finanças Comportamentais: Análise Do Perfil Comportamental Do Investidor e Do Propenso Investidor. Rev. Eletrônica Ciência Adm. 7 (2), 1–14 (2008)

Oliveira, J.N., Montibeler, E.E.: Finanças Comportamentais: Um Estudo Sobre a Aversão a Perda Na Tomada De Decisão. Rev. Eletrônica Estácio Pap. 4 (2), 147–162 (2017)

Barros, D.S., Felipe, S., José, I.: Teoria Do Prospecto: Evidências Aplicadas Em Finanças Comportamentais. Rev. Adm. FACES J. 14 (4), 86–105 (2015)

Singh, R.: Behavioural finance—The basic foundations. ASBM J. Manag. 2 (1), 89–99 (2009)

Bernoulli, D.: Exposition of a new theory on the measuremente of risk. Econometrica 22 , 23–36 (1954)

von Neumann, J., Morgenstern, O.: Theory of Games and Economic Behavior. Princeton University Press (1944)

Costa, D.F.: Ensaios Sobre Vieses Cognitivos No Processo de Tomada de Decisão Gerencial, Tese de doutoramento. Universidade Federal de Lavras, Minas Gerais (2017)

Rogers, P., Favato, V., Securato, J.R.: Efeito Educação Financeira No Processo de Tomada de Decisões Em Investimentos: Um Estudo a Luz Das Finanças Comportamentais. In: II Congresso ANPCONT-Associação Nacional dos Programas de Pós-Graduação em Ciências Contábeis, Salvador/BA (2008)

Bortoli, D.: Teoria Do Prospecto, Traços Da Personalidade, Teste de Reflexão Cognitiva e Avaliação Do Perfil Do Investidor: Um Estudo Em Finanças Comportamentais., Tese de Mestrado em Economia, Universidade Federal de Santa Catarina (2016)

Pan, Z.: A review of prospect theory. J. Hum. Resour. Sustain. Stud. 7 , 98–107 (2019). https://doi.org/10.4236/jhrss.2019.71007

Tversky, A., Kahneman, D.: Rational choice and the framing of decisions. J. Bus. Vol. 59, No. 4, Part 2: The Behavioral Foundation of Economic Theory. pp. S251-S278. Foundations 59 (4), 251–278 (Oct 1986)

Vasconcelos, A.F., Antunes, G.A., Silva, C.A.T.: Avaliação de Perdas e Ganhos Nas Decisões Financeiras: Uma Investigação à Luz Da Prospect Theory. Rev. Evidenciação Contábil Finanças 2 (1), 22–38 (2014)

Yoshinaga, C.E., Ramalho, T.B.: Finanças Comportamentais No Brasil: Uma Aplicação Da Teoria Da Perspectiva Em Potenciais Investidores. Rev. Bras. Gest. Negocios 16 (53), 594–615 (2014). https://doi.org/10.7819/rbgn.v16i52.1865

Johnson, D.D., Tierney, D.: Bad world: the negativity bias in international politics. Int. Secur. 43 (3), 96–140 (2019). https://doi.org/10.1162/isec_a_00336

Michaelson, Z.: Biases in choices about fairness: psychology and economic inequality. Judgm. Decis. Mak. 10 (2) (2015)

Thaler, R.H., Johnson, E.J.: Gambling with the house money and trying to break even: the effects of prior outcomes on risky choice. Manag. Sci. 36 (6), 643–660 (1990)

Reina, D., Dorow, A., Júnior, J.S.M., Reina, D.R.M., Nunes, P.: Behavioral Finance: Um Estudo Sobre a Correlação Entre a Heurística Da Ancoragem e a Tomada de Decisão Sob Risco Em Investimentos. Rev. Informação Contábil 3 , 83–98 (2009)

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Acknowledgements

This work is financed by Portuguese national funds through FCT—Fundação para a Ciência e Tecnologia, under the project UIDB/05422/2020.

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Silva, P., Mendonça, J., Gomes, L.M.P., Babo, L. (2023). Cognitive Biases in the Investment Decision Process. In: Mesquita, A., Abreu, A., Carvalho, J.V., de Mello, C.H.P. (eds) Perspectives and Trends in Education and Technology . Smart Innovation, Systems and Technologies, vol 320. Springer, Singapore. https://doi.org/10.1007/978-981-19-6585-2_17

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Cognitive biases and financial decisions of potential investors during Covid-19: an exploration

Arab Gulf Journal of Scientific Research

ISSN : 1985-9899

Article publication date: 4 July 2023

This paper aims to identify, examine, and present an empirical research design of behavioral finance of potential investors during Covid-19.

Design/methodology/approach

A well-structured questionnaire was designed; a survey was conducted among potential investors using convenience sampling, and 200 valid responses were collected. The research work uses multiple regression and discriminant function analysis to evaluate the influence of cognitive factors on the financial decision-making of investors.

Recency and familiarity bias are proven to have the highest significant impact on the financial decisions of investors followed by confirmation bias. Overconfidence bias had a negligible effect on the decision-making process of the respondents and found insignificant.

Research limitations/implications

Covid-19 is a temporary phase that may lead to changes in financial behavior and investors’ decisions in the near future.

Practical implications

The paper will help academicians, scholars, analysts, practitioners, policymakers and firms dealing with capital markets to execute their job responsibilities with respect to the cognitive bias in terms of taking financial decisions.

Originality/value

The present investigation attempts to fill the gap in the literature on the intended topic because it is evident from literature on the chosen subject that no study has been undertaken to evaluate the impact of cognitive biases on financial behavior of investors during Covid-19.

• Financial decisions
• Cognitive biases
• Confirmation
• Familiarity
• Overconfidence

Mohanty, S. , Patnaik, B.C.M. , Satpathy, I. and Sahoo, S.K. (2023), "Cognitive biases and financial decisions of potential investors during Covid-19: an exploration", Arab Gulf Journal of Scientific Research , Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/AGJSR-12-2022-0296

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Copyright © 2023, Stutee Mohanty, B.C.M. Patnaik, Ipseeta Satpathy and Suresh Kumar Sahoo

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1. Introduction

Investors scout for opportunities in various sectors to construct optimal portfolios. Therefore, they tend to be careful and invest in a planned manner to earn decent profits in competitive markets. Their behavior towards investment is either rational or irrational, depending on the different school of thought followed. Financial theories cannot explain investors’ aberrations when making investment decisions ( Chang, 2008 ). Hence, equal attention may be paid to behavioral finance. This explains how cognitive bias influences investors’ decision-making processes. Cognitive biases profoundly impact financial behavior and decisions ( Chaudhary, 2013 ). This study examines the influence of selected cognitive biases on investors' financial decisions, namely recency, familiarity, confirmation, and overconfidence. Recency bias leads investors to make financial decisions based on recent occurrences rather than historical ones. This occurs when information about the immediately occurring stimuli forms the premise of the expected outcome of the next stimuli ( Kalm & Norris, 2018 ). Investors affected by familiarity bias make financial decisions about how a particular investment choice is known to them. It determines the penchant of investors to make secured investments with hard-earned money ( Speidell, 2009 ). Confirmation bias is the search for data that supports an individual’s preconceived notions and beliefs ( Nickerson, 1998 ). Overconfidence bias leads to the consideration of deceptive data and makes investors believe that they are better than others, leading them to overestimate their capabilities and success quotient ( Čuláková, Kotrus, Uhlírová, & Jirásek, 2017 ). Covid-19 has made the situation around us more uncertain. Hence, this study attempts to explore cognitive biases and financial decisions within the limits of Covid-19 (see Figure 1 ).

The data collected for this study has been utilized to investigate whether the above-mentioned biases share a significant relationship with financial decisions taken by investors in India during Covid-19. The motivation of this study is based on the fact that despite of several pioneering research work on behavioral finance in the world, the focus in India has always mostly remained on traditional finance and its theories which are relevant but do not capture the whole essence of investor behavior. The work on literature review suggests behavioral finance in India has majorly been examined in the pre-Covid period and work in the post-Covid period is limited. These works also do not highlight the need for a deeper investigation of behavioral finance and cognitive biases. Thus, academicians, scholars, practitioners, financial analysts, policymakers, investment firms, and banks do not appreciate the significance of behavioral finance and the gain/loss they incur by not customizing portfolios for clients based on their mindset. It is contended that investors and firms that implement behavioral finance theories are more likely to stay relevant in the field of investment. This study is therefore, expected to contribute a new perspective to the existing literature with the purpose of identifying, examining, and presenting an empirical research design of behavioral finance of potential investors during Covid-19. It is divided into five sections. Section 1 is the introduction and section 2 elaborately explains the existing literature, conceptual framework, and hypotheses development on this subject matter. Section 3 explores the research methodology inculcates in analyzing the data collected for the study. Section 4 discusses the data analysis and results of the study. Finally, section 5 depicts the conclusions, implications, limitations and suggestions for future research.

2. Significance of the study

Investors are indecisive about their financial choices because of several cognitive biases. This study will be immensely helpful to individual investors as well as financial institutions such as brokerage firms, banks, and retail investors. It also aims to study the behavior of future investors, which will aid analysts in constructing a robust portfolio for their clients.

3. Research objective

To examine the influence of cognitive biases on investors’ financial decisions during Covid-19.

4. Literature review, theoretical framework, and hypotheses development

The decision-making process is complex and influenced by many internal and external factors. Behavioral factors contribute to the choice of multi-baggers in the Indian securities market ( Chauhan, Gupta, & Gupta, 2022 ). Indian investors prioritize ESG factors that influence their investing decisions ( Sood et al. , 2023 ). Halal standard implementation in the Palestinian food sector is a major driver of its financial and stock market performance ( Amer, 2023 ). Review work conducted on existing literature indicated diverse opinions on the kind of relationship recency, familiarity, confirmation, and overconfidence bias shared with financial decision-making.

Behavioral biases including recency bias significantly influence Indian investors’ financial decision-making process ( Jain & Kesari, 2022 ). Recency bias overpowers experience and complexity while making financial decisions ( Arnold, Collier, Leech, & Sutton, 2002 ). Recency bias is proven to have a profound effect on investment decision-making ( Sulistiawan & Wijaya, 2015 ).

South African investors prominently showcase familiarity bias while choosing companies for investment purposes ( Vries, Erasmus, & Gerber, 2017 ). Familiarity bias significantly negatively impacts investors’ portfolio diversification decisions ( Nurcahya & Dewi, 2021 ). Investment decision-making is significantly influenced by familiarity bias among investors ( Rosyidah and Pratikto, 2022 ).

Confirmation bias has a statistically significant but deleterious impact on the development of behavioral biases during financial decision-making ( Weixiang, Qamruzzaman, Rui, & Kler, 2022 ). It is the most recurrent bias among professionals while making an investment choice ( Berthet, 2022 ). The financial decisions of investors have a negligible but significant effect by confirmation bias ( Sharma & Kumar, 2022 ).

Investors are overconfident about their investment decisions, skills, knowledge, ability to choose stocks, control of the portfolio, future investment plans, and stock market information for which they require multiple approaches ( Trehan, 2016 ). Overconfidence bias has a significant positive influence on the investment decisions of Pakistani investors ( Riyaz & Iqbal, 2015 ). There is a positive relationship between overconfidence bias with a mediating role of risk tolerance corresponding to financial decision-making ( Mallik, Hanif, & Azhar, 2019 ).

The authors attempt to study the available literature holistically and cover the existing work related to recency, confirmation, familiarity, and overconfidence bias ( Trehan & Sinha, 2021 ; Trehan, 2016 ).

4.1 Theoretical framework and hypotheses development

Decision Theory describes the freedom with which a person takes a decision. It deals with the behavior that leads to choosing one option over the other and achieving one’s pre-determined goal. There are two types of decision theories namely, normative and descriptive. Normative decision theory explains how a decision should be made, and descriptive decision theory emphasizes various factors that contribute to decision-making ( Hansen, 1994 ). Behavioral and psychological theories also influence the human decision-making process. Behavioral reasoning theory (BRT) is an innovative theory that explains how beliefs, reasons, motives, intentions, and behavior determine the decisions of an individual ( Sahu, Padhy, & Dhir, 2020 ). Behavioral finance is an extensive branch of finance that studies the influence of human behavior on finance and financial decision-making. It analyzes the psychological and sociological impact on human beings based on their behavior and mind ( Bikas, Jureviciene, Dubinskas, & Novickytė, 2013 ). It integrates cognitive psychology with finance and restricts traditional finance theories to determine the irrational financial decision-making of an individual ( Chauhan et al. , 2014 ). Individuals use shortcuts called Heuristics to make decisions in complex and uncertain situations ( van Noordt & Misuraca, 2022 ). Cognitive and psychological biases affect all human beings' behavior and decisions, including investors ( Ady, 2018 ). This led the researchers to investigate the behavior of investors in investing regarding cognitive and psychological biases.

4.1.1 Financial decision and recency bias

Belief-adjustment theory examines the sequence in which information is presented to determine the existence of recency bias in the decision-making process of an investor. It indicates a mix of good and bad news when presented in sequence, leading to recency bias in an investor’s financial decisions ( Hogarth & Einhorn, 1992 ). Recency bias occurs when investors change their initial beliefs and make subsequent decisions based on new information at their disposal ( Hogarth & Einhorn, 1992 ). This is the change in the financial behavior and decisions of an investor when the initial belief is changed by new information ( Hartono, 2004 ). Recency bias attracts investors’ attention to the latest information ( Nasution & Supriyadi, 2017 ). The sequential presentation of information leads to recency bias among investors and affects their financial decisions ( Aprayuda, Misra, & Kartika, 2021 ). Recency bias is more effective among female investors than male investors ( Onsomu, 2014 ). Investors showcase changes in behavior and financial decisions based on the training and knowledge they obtain. Behavioral changes have been observed in knowledgeable and trained investors ( Dilla & Steinbart, 2005 ). It exists in financial markets and is highly influential on investors’ decision-making processes ( Alvia & Sulistiawan, 2010 ). It has the highest impact on investors’ financial decisions ( Bashir, Ilyas, & Farrukh, 2009 ). Investors have a strong inclination to be prone to recency bias, which has a high impact on their behavior and financial decisions ( Lathe, Jain, & Anand, 2020 ). It has a substantial impact on investors’ financial decision-making processes ( Zahera & Bansal, 2018 ). Recency bias exists among Indian investors if there is any change in the shareholding pattern of a stock ( Singh, Bala, Dey, & Filieri, 2022 ). It plays a prominent role in investment decision-making as investors react based on the latest published research articles on capital markets and stocks ( Bihari, Dash, Kar, Muduli, Kumar, & Luthra, 2022 ). Psychological biases like recency bias have a crucial influence on investors’ investment decisions in the state of Maharashtra, India ( Tupe & Lokhande, 2021 ). Indonesian investors exhibit recency bias upon receiving any important capital markets or stock-related pieces of information in the middle of the trading process ( Armansyah et al. , 2022 ). Arab investors are significantly affected by recency bias while deciding on an asset allocation that impacts the ability of a portfolio to generate long-term returns ( Pradhan, 2021 ). Recency bias based on accounting information, firm-image co-incidence, and neutral information impacts the investment decisions of Indian investors ( Sachdeva, Lehal, Gupta, & Garg, 2022 ). Investor Cognitive Psychology, Market Information, and Stock Characteristics lead to recency bias resulting in massive herding among investors in India ( Sachdeva, Lehal, Gupta, & Gupta, 2023 ).

Recency bias has a significant impact on investors’ financial decisions during Covid-19.

4.1.2 Financial decision and familiarity bias

Investors exhibit familiarity bias when they choose stocks of acquainted companies ( Vries et al. , 2017 ). It exists among investors and affects their financial decisions when buying stocks in the market ( Bashir & Maqsood, 2018 ). It is prevalent among investors and influences their financial decision-making processes, as evidence indicates that familiar investments are preferred by them ( Cao, Han, Hirshleifer, & Zhang, 2011 ). Geographical location and gender also lead to familiarity bias and its impact on investors’ financial decisions. It is highest in the USA and lowest in Asia ( Levy, Frethey-Bentham, & Cheung, 2020 ). Authors found financial decision familiarity bias among American investors and U. S-grown companies ( McAndrews, 2017 ). The authors identified that familiarity bias significantly affects investors’ financial decisions in Egyptian markets ( Metawa, Hassan, Metawa, & Safa, 2019 ). Familiarity bias has a negligible impact on the decision-making process of investors in European capital markets ( Baker & Ricciardi, 2014 ). Familiarity bias has a substantial impact on the financial behavior and decisions of investors in the US and Canadian capital markets ( Baker & Nofsinger, 2002 ). Familiarity bias is found among employees in the financial service sector and positively affects their investment decisions ( Patni & Choubey, 2019 ). Familiarity bias leads to employees prioritizing their own company or sector while making financial decisions ( McAndrews, 2017 ). Financial literacy and familiarity bias are not significantly associated with each other and therefore, do not impact the investment decision-making of investors in India ( Baker, Kumar, Goyal, & Gaur, 2019 ; Chowdhary, 2020 ). It shares a significant positive relationship with home bias and that impacts the financial decisions of Indian investors ( Jain, Jain, & Jain, 2015 ). Mutual fund investors in India have a tendency of investing in schemes they are familiar with. This affects their returns on a long-term basis ( Ranjan & Sivaraman, 2021 ).

Familiarity bias substantially influences investors’ financial decision-making process during Covid-19.

4.1.3 Financial decision and confirmation bias

Investors’ financial and trading decisions are affected by the information received from virtual communities, which influences their existing beliefs. Confirmation bias hurts the decision-making process ( Park, Konana, Gu, Kumar, & Raghunathan, 2010 ). It is present among investors and influences their financial decisions, despite the support of decision systems ( Huang, Hsu, & Ku, 2012 ). Confirmation bias and its impact on financial decisions are prominent in men ( Nelson, 2014 ). An epistemic authority is present among investors but is limited by the existence of confirmation bias ( Zaleskiewicz & Gasiorowska, 2021 ). This is observed among investors in online chat rooms ( Mohamed & Sinha, 2022 ). Confirmation bias exists among investors and works against their beliefs about psychological distance, which affects their financial decisions ( Baack, Dow, Parente, & Bacon, 2015 ). Entrepreneurial investors commonly check their existing beliefs while making financial decisions ( Von Bergen and Bressler, 2018 ). Confirmation bias is observed among investors and influences their decision-making process, which helps maintain the status quo ( Chen, Cheng, Du, Xu, Jiang, & Wang, 2021 ). High-income Gujrati investors in India share a significant positive relationship with confirmation bias while investing ( Soni & Desai, 2019 ). Indian investors identify behavioral biases including confirmation bias and take corrective measures to ensure the maximization of their returns ( Dey, Stamenova, Turner, Black, & Levine, 2016 ). Confirmation bias is insignificant with respect to age and investment experience. Thus, it does not affect the financial decision-making process of investors in India ( Sujesh & Dhanya, 2019 ).

Confirmation bias positively affects investors’ decisions during the Covid-19 phase.

4.1.4 Financial decision and overconfidence bias

Overconfidence bias has a significant effect on investors’ financial decisions in Egyptian financial markets, although its impact is determined by age, gender, level of education, and experience ( Metawa et al. , 2019 ). It has a significant effect on the financial decision-making process of investors in Tehran financial markets but is limited by hindsight bias ( Sadi, Asl, Rostami, Gholipour, & Gholipour, 2011 ). It has a substantial negative impact on Pakistani investors’ financial decisions on the Islamabad Stock Exchange and is exacerbated by over-optimism bias ( Kafayat, 2014 ). Pakistani investors in Karachi City are highly influenced by the overconfidence bias ( Qasim, Hussain, Mehboob, & Arshad, 2019 ). Investors at the PSX are significantly affected by overconfidence bias, with moderating effects of financial literacy and mediating effects of risk perception ( Ahmad & Shah, 2020 ). Overtrading and overconfidence determine investor behavior and financial decisions in U.S. capital markets ( Bates, 2020 ). Only overconfidence bias has a positive effect on investors’ financial decision-making in Abu Dhabi ( Shah, Alshurideh, Dmour, & Al-Dmour, 2021 ). The presence of an overconfidence bias is responsible for panic selling, which has caused the biggest market crash ever in the history of Indian capital markets ( Kwatra, 2020 , Bhoj, 2019 ). It has a moderate influence on investor behavior ( Luu, 2014 ). This substantially affects investors’ financial decisions ( Bansal, 2020 ). Overconfidence has a significant positive effect on investors’ decision-making processes ( Qadri & Shabbir, 2004 ; Dungarwal & Tollawala, 2022 ; Salehi et al. , 2023 ). Overconfidence is relevant among investors and affects their financial behavior ( Związek, Korzo, Przybyłowicz, Górny, & Kożuchowski, 2015 ). It affects investors’ financial decisions both positively and negatively based on their market situation ( Putri, Xu, & Akkweteh, 2020 ). Overconfidence and other heuristic factors assist in structuring guidelines and investment thumb rules for investors by highlighting potential mental errors ( Vaid & Chaudhary, 2022 ). Pakistani investors’ decisions are highly influenced by overconfidence bias which results in losses in many instances ( Qasim et al. , 2019 ). Investment decision-making of individual equity investors in Punjab, India has the highest influence of overconfidence bias ( Jain, Walia, & Gupta, 2019 ).

Overconfidence bias significantly affects investors’ financial decisions amid Covid-19.

5. Research methodology

5.1 sample and procedure.

The study used a convenience sampling method for conducting the survey to examine the influence of cognitive factors on the financial decisions of investors in India. Individual practicing investors in Bhubaneswar, Kolkata, Mumbai, Ahmedabad, and Delhi were considered as the unit of analysis for the study. The research participants are believed to have given accurate responses to the questions asked in this context. A sample questionnaire was designed to fulfill the requirements of the study for the collection of data from primary sources. Out of 250 questionnaires circulated, 230 were found completed, amounting to approximately 92% of the total number of questionnaires. After thorough scrutiny of the filled-up questionnaires, 30 questionnaires were found to be incomplete, either concerning demographics or any specific question. The final sample size was 200 participants.

5.2 Measurement

Based on a review of the literature and identified research gap, a well-structured closed-ended questionnaire was designed and circulated among the respondents to tap into the different dimensions of ‘cognitive factors affecting investors’ financial decisions during the Covid-19. After a thorough discussion of the available literature regarding cognitive biases and investors’ financial decisions, we considered four independent variables (mental accounting, herd behavior, anchoring, and framing effects) and one dependent variable (financial decision). The questionnaire was divided into two parts. The first section of the questionnaire consisted of the demographic profile of the research participants and the second section consisted of the variables. It contained 24 questions related to the 4 constructs. Each construct had 6 questions. A Likert scale ranging from 5 (represents “Strongly Agree”) to 1 (represents “Strongly Disagree”) was used. The scale was previously used by ( Kamselem, Nuhu, & Liman, 2020 ; Ahmed, Noreen, Ramakrishnan, & Abdullah, 2021 ). The pilot study was used to test the validity and reliability of the research instruments. The relevance of all the items, coherence, clarification, and themes was determined by the content validity.

5.3 Data analysis techniques

The authors have used the Statistical Package for Social Science (SPSS) 27.0 for conducting Cronbach’s Alpha test to assess the reliability of the variables (both independent and dependent), Goodness of Fit to determine the effectiveness of the model structure with respect to the data used, multiple regression and discriminant function analysis to evaluate the hypothesis of the study. This research model was previously used by ( Brett & Abramowitz, 2008 ; Alayande & Bashiru, 2015 ; Hair, Sarstedt, Hopkins, & Kuppelwieser, 2014 ).

6. Data analysis

The authors attempted to provide details on the respondents’ profiles. The influence of selected cognitive biases on individual investors’ financial decisions was studied through an empirical analysis.

It is evident from the above, Table 1 , that the majority of investors are males (74.00%), followed by females (26.00%). The majority of investors fall under the age group of 31-40 years (48.00%) followed by 21-30 years (39.00%). 87% of the respondents were undergraduates, followed by postgraduates. It is found that investors with work experience of 0-5 years focus their attention more on investment, whereas investors with work experience of more than 20 years have negligible importance. It has also been identified that the majority of investors invest every month, and a negligible number of investors choose to invest according to their convenience.

Table 2 shows the reliability test for the cognitive factors studied by calculating Cronbach's alpha. Cronbach’s alphas for recency bias, familiarity bias is 0.81, confirmation bias were 0.84, 0.81, 0.87, and overconfidence bias is 0.60, respectively. Therefore, every variable is steady, except for the overconfidence bias, whose reliability is poor. Hence, its consistency is questionable, and it can vary from study to study, depending on demographics.

It is evident from Table 3 that the beta values for recency, familiarity, confirmation, and overconfidence bias were 0.71, 0.75, 0.69, and 0.12, respectively. The p -value for all the selected biases is 0.00, except for overconfidence, which has a p -value of 0.13 ( p  > 0.05). This proves that the relationship is significant for each of them but not overconfidence bias. If we compare the path coefficients of these behavioral biases, the relationship between familiarity and financial decisions is stronger than that of the others. This clarifies that familiarity bias plays a defining role in investors’ financial decision-making processes. Simultaneously, recency and confirmation biases also play a substantial role in the financial decision-making process.

Model fit is achieved from the standardized difference between observed correlation and predicted correlation. Table 4 showcases that the calculated chi-square is 4.42, RMESA is 0.08, GFI is 0.94, AGFI is 0.91, NFI is 0.92 and CFI is 0.93. All values adhere to the respective recommended values. This verifies that the studied data are a perfect fit for the designed model in the research work ( Henseler, Ringle, & Sarstedt, 2015 ).

For each discriminant function, the eigenvalue is the ratio of the between-group to the within-group sum of squares. Large eigenvalues imply superior functionality. As per Table 5 , function 1 has an eigenvalue greater than 1. Hence, this indicates that function one highly influences investors' financial decisions. The canonical correlation of the same is 0.72 or 72%, which means that function one and behavioral biases are strongly correlated.

From Table 6 above, function one has a low Wilks’ lambda. Hence, function one highly defines the financial decisions of investors. The significance value for each function is 0.00. Therefore, these three functions were statistically significant.

A structural matrix can be defined as a tool to describe the relationship between independent factors (behavioral biases in this case) with relevant discriminant functions. As can be seen in Table 7 , while recency and familiarity are strongly correlated with function one, confirmation and overconfidence correlate with functions two and three, respectively. The equation used is as follows: Z 1 = ( 0.81 ) * R e c e n c y + ( 0.80 ) * F a m i l i a r i t y Z 2 = ( 0.38 ) * C o n f i r m a t i o n   a n d   Z 3 = ( 0.31 ) * O v e r c o n f i d e n c e

On an aggregate basis, the structure matrix explains that recency and familiarity determine the financial decisions of participating individual investors.

7. Conclusion and suggestions

Behavioral biases play a crucial role in determining investors’ financial decisions globally. This study examines certain behavioral biases that were either unidentified or identified and not extensively studied, including their impact on investors' financial behavior during the ongoing Covid-19 pandemic. The structure matrix findings prove that recency and familiarity bias are more influential in this case than confirmation and overconfidence bias. Wilks’ lambda analysis is used to determine the impact of functions on investors’ financial decisions, and it concludes that function one affects decisions taken by investors’ decisions the most. The necessity of financial knowledge for an investor before making any financial decision is found to be essential for reducing the chances of being manipulated or cheated by a third party or influenced by any external factor. These findings establish the significance of cognitive bias. Therefore, investors do not always make rational financial decisions. Institutions should conduct workshops to help employees make sound financial decisions. Policymakers need to implement a proper strategy along with these institutions to improve financial knowledge to reduce the impact of such factors on the psyche of an investor. The government should set up proper brokerage firms to create awareness among individual investors. Investors should realize that a proper understanding of capital markets and investments is a prerequisite for making profitable financial decisions.

8. Implications

This work makes theoretical and practical contributions to behavioral finance subject matter and provides academicians, scholars, practitioners, analysts, policymakers, and firms with a new dimension that significantly influences financial decisions. Behavioral finance explains and demonstrates investment from a psychological perspective. This field of study examines and explains various activities in the capital markets. e.g.: The Adani Group fiasco in 2023. Still behavioral finance is a contentious field as it is a novel concept that is developing and refining itself. The findings of the study will expand the existing literature on behavioral finance in rapidly growing economies like India. Hence, the paper tries to provide a considerable perspective on cognitive factors influencing the financial decision-making process of investors.

Financial analysts will be able to manage their investors better ( Gupta, 1991 ) and provide clients with higher returns on their investments by overcoming cognitive errors ( Iyer & Bhaskar, 2002 ). It will also help asset management companies and retail investors in the market by providing fund managers with a systematic framework to cater to their customers’ requirements. Company managers can also analyze stock performance in the capital markets by identifying investors’ behavior and framing policies and strategies accordingly. Practitioners can learn from behavioral finance theories and manage their investments by selecting accurate stocks available in the markets. Academicians and scholars can get revised literature on behavioral finance and learn new theories and their applications. Policymakers will be able to investigate market trends in depth and formulate rules and regulations based on it.

9. Limitations and future prospects

This paper is based on a few cognitive biases and their effects on investors’ financial decisions, which have not been extensively studied during the Covid-19. It is a temporary phase, and investors’ financial behavior may change when the situation normalizes. The respondents of this study might be cautious of their behavior while replying to the questionnaire provided to them. Future researchers can focus on other major psychological factors affecting the psyche of investors in the market that define their financial behavior, which makes this a promising area for future research related to this topic. They can also increase the number of cities covered, sample size studied, and use better statistical tools for a more sophisticated analysis of the collected data and then acquire more specific results that will have more pervasive implications for the financial markets and their stakeholders.

Conceptual framework

Description of respondents

Source(s): Authors’ work

Ady , S. ( 2018 ). The cognitive and psychological bias in investment decision-making behavior: (Evidence from Indonesian investor’s behavior) . Journal of Economics and Behavioral Studies .

Ahmad , M. , & Shah , S. Z. A. ( 2020 ). Overconfidence heuristic-driven bias in investment decision-making and performance: Mediating effects of risk perception and moderating effects of financial literacy . Journal of Economic and Administrative Sciences .

Ahmed , Z. , Noreen , U. , Ramakrishnan , S. A. , & Abdullah , D. F. B. ( 2021 ). What explains the investment decision-making behaviour? The role of financial literacy and financial risk tolerance . Afro-Asian Journal of Finance and Accounting , 11 ( 1 ), 1 – 19 .

Alayande , S. , & Bashiru , K. ( 2015 ). An overview and application of discriminant analysis in data analysis . IOSR Journal of Mathematics (IOSR-JM) , 11 ( 1 ), 12 – 15 .

Alvia , L. , & Sulistiawan , S. ( 2010 ). The examination of recency and knowledge effect in investment decision making: An experimental study . Indonesian Journal of Accounting Research , 13 ( 1 ), 45 – 58 .

Amer , M. ( 2023 ). Halal standards’ implementation in Palestinian food sector: Its drivers and impact on performance . Arab Gulf Journal of Scientific Research .

Aprayuda , R. , Misra , F. , & Kartika , R. ( 2021 ). Does the order of information affect investors' investment decisions? . Experimental Investigation, Journal of Accounting and Investment , 22 ( 1 ).

Armansyah , I. , Susanti , H. , James , K. , Lovell , K. , Idaiani , S. , Imah , S. , … Brooks , H. ( 2022 ). Civic engagement and mental health system strengthening in Indonesia: A qualitative examination of the views of health professionals and national key stakeholders . BMC Psychiatry , 20 ( 1 ), 1 – 15 .

Arnold , V. , Collier , P. , Leech , S. , & Sutton , S. ( 2002 ). The effect of experience and complexity on order and recency bias in decision making by professional accountants . Accounting & Finance , 40 , 109 – 134 .

Baack , D. W. , Dow , D. , Parente , R. , & Bacon , D. R. ( 2015 ). Confirmation bias in individual-level perceptions of psychic distance: An experimental investigation . Journal of International Business Studies , 46 ( 8 ), 938 – 959 .

Baker , H. K. , & Nofsinger , J. R. ( 2002 ). Psychological biases of investors . Financial Services Review , 11 ( 2 ), 97 .

Baker , H. K. , & Ricciardi , V. ( 2014 ). How biases affect investor behavior . The European Financial Review , 7 – 10 .

Baker , H. K. , Kumar , S. , Goyal , N. , & Gaur , V. ( 2019 ). How financial literacy and demographic variables relate to behavioral biases . Managerial Finance , 45 ( 1 ), 124 – 146 .

Bansal , T. ( 2020 ). Behavioral finance and COVID-19: Cognitive errors that determine the financial future . SSRN .

Bashir , T. , Ilyas , M. , & Farrukh , A. ( 2009 ). Testing the weak-form efficiency of Pakistani stock markets-an empirical study in the banking sector . European Journal of Economics, Finance and Administrative Sciences .

Bashir , T. & Maqsood , M. ( 2018 ). Impact of behavioral biases on investors decision making: Male vs female .

Bates , A. N. ( 2020 ). Investor behavior in the midst of a global pandemic . SSRN .

Berthet , V. ( 2022 ). The impact of cognitive biases on professionals’ decision-making: A review of four occupational areas . Frontiers of Psychology , 12 ( 1 ).

Bhoj , J. ( 2019 ). A study of origin and history of behaviour finance with special reference to India .

Bihari , A. , Dash , M. , Kar , S. K. , Muduli , K. , Kumar , A. , & Luthra , S. ( 2022 ). Exploring behavioural bias affecting investment decision-making: A network cluster based conceptual analysis for future research . International Journal of Industrial Engineering and Operations Management , 4 ( 1/2 ), 19 – 43 .

Bikas , E. , Jureviciene , D. , Dubinskas , P. , & Novickytė , L. ( 2013 ). Behavioural finance: the emergence and development trends . Procedia - Social and Behavioral Sciences , 82 , 870 – 876 . doi: 10.1016/j.sbspro.2013.06.363 .

Brett , D. , & Abramowitz , J. S. ( 2008 ). Is hypochondriasis related to obsessive-compulsive disorder, panic disorder, or both? An empirical evaluation . Journal of Cognitive Psychotherapy , 22 ( 2 ), 115 – 127 .

Cao , H. H. , Han , B. , Hirshleifer , S. , & Zhang , H. H. ( 2011 ). Fear of the unknown: Familiarity and economic decisions . Review of Finance , 15 ( 1 ), 173 – 206 .

Chang , C. -H. ( 2008 ). The impact of behavioral pitfalls on investors’ decisions: The disposition effect in the Taiwanese warrant market . Social Behavior and Personality: An International Journal , 36 ( 5 ), 617 – 634 .

Chaudhary , A. K. ( 2013 ). Impact of behavioral finance in investment decisions and strategies–a fresh approach . International Journal of Management Research and Business Strategy , 2 ( 2 ), 85 – 92 .

Chauhan , A. , Gupta , S. , & Gupta , S. ( 2022 ). An ISM and MICMAC approach for modelling the contributors of multibagger stocks . Asia-Pac Finance Markets .

Chauhan , S. S. , Dayyeh , B. K. A. , Bhat , Y. M. , Gottlieb , K. T. , Hwang , J. H. , Komanduri , S. , … Wallace , M. B. ( 2014 ). Confocal laser endomicroscopy . Gastrointestinal Endoscopy , 80 ( 6 ), 928 – 938 .

Chen , M. , Cheng , H. , Du , Y. , Xu , M. , Jiang , W. , & Wang , C. ( 2021 ). Two wrongs don't make a right: Combating confirmation bias in learning with label noise .

Chowdhary , M. ( 2020 ). Financial literacy and its interaction with altered investment behaviour: An analysis of the familiarity bias .

Čuláková , T. , Kotrus , P. , Uhlírová , A. , & Jirásek , M. ( 2017 ). The overconfidence bias and CEO: A literature overview .

Dey , A. K. , Stamenova , V. , Turner , G. , Black , S. E. , & Levine , B. ( 2016 ). Pathoconnectomics of cognitive impairment in small vessel disease: A systematic review . Alzheimer’s and Dementia , 12 ( 7 ), 831 – 845 .

Dilla , W. , & Steinbart , P. ( 2005 ). Relative weighting of common and unique balanced scorecard measures by knowledgeable decision makers . Behavioral Research in Accounting , 17 ( 1 ).

Dungarwal , H. , & Tollawala , N. ( 2022 ). A study on impact of behavioral biases on investment decision . International Journal of Research Publication and Reviews , 3 ( 4 ), 697 – 703 .

Gupta , R. ( 1991 ), “ Portfolio management: The process and its dynamics ”, Working Paper No. 923, (JanMar), the Indian Institute of Management, Ahmadabad .

Hair , J. F. , Jr , Sarstedt , M. , Hopkins , L. , & Kuppelwieser , V. G. ( 2014 ). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research . European Business Review , 26 ( 2 ), 106 – 121 .

Hansen , E. ( 1994 ). Issues concerning the familiarity of researchers with the research setting . Wiley Online Library .

Hartono , J. ( 2004 ). The recency effect of accounting information . Gadjah Mada International Journal of Business , 6 ( 1 ), 85 – 116 .

Hogarth , R. M. , & Einhorn , H. J. ( 1992 ). Order effects in belief updating: The belief-adjustment model . Cognitive Psychology , 24 ( 1 ), 1 – 55 .

Huang , H. H. , Hsu , J. , & Ku , C. Y. ( 2012 ). Understanding the role of computer-mediated counter-argument in countering confirmation bias . Decision Support Systems , 53 ( 3 ), 438 – 447 .

Henseler , J. , Ringle , C. M. , & Sarstedt , M. ( 2015 ). A new criterion for assessing discriminant validity in variance-based structural equation modeling . Journal of the Academy of Marketing Science , 43 ( 1 ), 115 – 135 .

Iyer , S. B. , & Bhaskar , R. K. ( 2002 ). Investor’s psychology: A study of investor behavior in the Indian, Capital Market Finance India , ( 4 ), 1357 – 1375 .

Jain , J. , Walia , N. , & Gupta , S. ( 2019 ). Evaluation of behavioral biases affecting investment decision making of individual equity investors by fuzzy analytic hierarchy process . Review of Behavioral Finance , 12 ( 3 ), 297 – 314 .

Jain , N. , & Kesari , B. ( 2022 ). The impact of behavioral biases on financial risk tolerance of investors and their decision making .

Jain , R. , Jain , P. , & Jain , C. ( 2015 ). Behavioral biases in the decision making of individual investors . IUP Journal of Knowledge Management , 13 ( 3 ).

Kafayat , A. ( 2014 ). Interrelationship of biases: Effect investment decisions ultimately, theoretical and applied economics . Asociatia Generala a Economistilor Din Romania - AGER , 0 ( 6(595) ), 85 – 110 .

Kalm , K. , & Norris , D. ( 2018 ). Visual recency bias is explained by a mixture model of internal representations . Journal of Vision , 18 ( 7 ), 1 .

Kamselem , K. M. , Nuhu , S. M. , Liman , L. M. ( 2020 ). Employee empowerment and service quality delivery: Moderating role of organizational commitment among nursing staff of Nigerian public hospitals . International Journal of Business and Technopreneurship , 10 ( 3 ), Oct 2020 , 309 322 .

Kwatra ( 2020 ). Behavioral finance and stock performance: Biases influencing the market . IOSR Journal of Humanities and Social Science (IOSR-JHSS) , 25 ( 7 ), 66 – 69 , Series 3 .

Lathe , A. S. , Jain , S. , & Anand , S. ( 2020 ). An empirical examination of the impact of locus of control on investor behavioral biases . International Journal of Management (IJM) , 11 ( 1 ), 98 – 107 .

Levy , D. S. , Frethey-Bentham , C. , & Cheung , W. K. S. ( 2020 ). Asymmetric framing effects and market familiarity: Experimental evidence from the real estate market . Journal of Property Research , 37 ( 1 ), 85 – 104 .

Luu , T. B. ( 2014 ). Behavior pattern of individual investors in stock market . International Journal of Business and Management , 9 ( 1 ), 1 – 16 .

Mallik ., Hanif ., & Azhar . ( 2019 ). The impact of overconfidence bias on investment .

McAndrews ( 2017 ). How familiarity bias may impact your investment decision-making . WrapManager .

Metawa , N. , Hassan , M. K. , Metawa , S. , & Safa , M. F. ( 2019 ). Impact of behavioral factors on investors’ financial decisions: Case of the Egyptian stock market . International Journal of Islamic and Middle Eastern Finance and Management .

Mohamed , R. , & Sinha , N. ( 2022 ), Am i getting it right? : A framework for attributing style factor exposures .

Nasution , & Supriyadi ( 2017 ). Effect of evidence order, the cognitive style and personality of the revision process confidence . Accounting National Symposium X .

Nelson , J. A. ( 2014 ). The power of stereotyping and confirmation bias to overwhelm accurate assessment: The case of economics, gender, and risk aversion . Journal of Economic Methodology , 21 ( 3 ), 211 – 231 .

Nickerson , R. S. ( 1998 ). Confirmation bias: A ubiquitous phenomenon in many guises . Review of General Psychology , 2 ( 2 ), 175 – 220 .

Nurcahya , M. A. , & Dewi , D. A. ( 2021 ). Implementasi Nilai Dasar Pancasila Dalam Upaya Mewujudkan Tujuan Negara di Kehidupan Sehari-Hari . Edukatif: Jurnal Ilmu Pendidikan , 3 ( 3 ), 631 – 639 .

Onsomu , Z. N. ( 2014 ). The impact of Behavioral biases on investor decisions in Kenya: Male vs Female . Impact Journals , 2 ( 6 ), 87 – 92 .

Park , J. , Konana , P. , Gu , B. , Kumar , A. , & Raghunathan , R. ( 2010 ). Confirmation bias, overconfidence, and investment performance: Evidence from stock message boards . McCombs Research Paper Series , 07 ( 10 ).

Patni , I. , & Choubey , S. ( 2019 ). Behavioral traits and investment decisions: An optimism bias on investors decision making; evidence from developing markets , Research Journal of Finance and Accounting , 6 ( 11 ).

Pradhan , A. ( 2021 ). Quantitative model for impact of behavioral biases on asset allocation decisions: A case study of investors in UAE . Journal of Asset Management , 22 , 573 – 580 .

Putri , M. D. P. W. , Xu , C. , & Akkweteh , L. N. ( 2020 ). Financial behavior during COVID-19: Cognitive errors that can define financial future . Open Journal of Social Sciences , 8 , 259 .

Qadri , S. U. , & Shabbir , M. ( 2004 ). An empirical study of overconfidence and illusion of control biases impact on investor’s decision making: An evidence from ISE . European Journal of Business and Management , 6 ( 14 ), 38 – 44 .

Qasim , M. , Hussain , R. , Mehboob , I. , & Arshad , M. ( 2019 ). Impact of herding behavior and overconfidence bias on investors’ decision-making in Pakistan . Accounting , 5 , 81 – 90 . doi: 10.5267/j.ac.2018.7.001 .

Ranjan , S. , & Sivaraman , A. K. ( 2021 ). Intelligent currency trade value prediction .

Riyaz , T. , & Iqbal , H. ( 2015 ). Impact of overconfidence, illusion of control . Self-Control and .

Rosyidah , U. , & Pratikto , H. ( 2022 ). The role of behavioral bias on financial decision making: A .

Sachdeva , M. , Lehal , R. , Gupta , S. , & Garg , A. ( 2022 ). What make investors herd while investing in the Indian stock market? A hybrid approach . Review of Behavioral Finance , 15 ( 1 ), 19 – 37 .

Sachdeva , M. , Lehal , R. , Gupta , S. , & Gupta , S. ( 2023 ). Influence of contextual factors on investment decision-making: A fuzzy-AHP approach . Journal of Asia Business Studies , 17 ( 1 ), 108 – 128 .

Sadi , R. , Asl , H. G. , Rostami , M. R. , Gholipour , A. , & Gholipour , F. ( 2011 ). Behavioral finance: The explanation of investors’ personality and perceptual biases effects on financial decisions . International Journal of Economics and Finance , 3 ( 5 ), 234 – 241 .

Sahu , A. K. , Padhy , R. K. , & Dhir , A. ( 2020 ). Envisioning the future of behavioral decision-making: A systematic literature review of behavioral reasoning theory . Australasian Marketing Journal , 28 ( 4 ), 145 – 159 .

Salehi , M. , Dalwai , T. , & Arianpoor , A. ( 2023 ). The impact of narcissism, self-confidence and auditor’s characteristics on audit report readability . Arab Gulf Journal of Scientific Research , 41 ( 2 ), 202 – 223 .

Shah , S.F. , Alshurideh , M. , Dmour , A. , & Al-Dmour , R. ( 2021 ). Understanding the influences of cognitive biases on financial decision making during normal and COVID-19 pandemic situation in the United Arab Emirates .

Sharma , V. , & Kumar , R. ( 2022 ). Examining the role of behavioural biases in financial decisions: A literature review . Journal of Positive School Psychology , 6 ( 3 ), 6685 – 6697 .

Singh , P. , Bala , H. , Dey , B. L. , & Filieri , R. ( 2022 ). Enforced remote working: The impact of digital platform-induced stress and remote working experience on technology exhaustion and subjective wellbeing . Journal of Business Research , 151 , 269 – 286 .

Soni , K. , & Desai , D. M. ( 2019 ). A study on different behavioral biases and its impact on investor’s decision making .

Sood , K. , Pathak , P. , Jain , J. , & Gupta , S. ( 2023 ). How does an investor prioritize ESG factors in India? An assessment based on fuzzy AHP . Managerial Finance , 49 ( 1 ), 66 – 87 .

Speidell , L. S. ( 2009 ). Investing in the unknown and the unknowable—behavioral finance in frontier markets . The Journal of Behavioral Finance , 10 ( 1 ), 1 – 8 .

Sujesh , C. P. , & Dhanya , K. S ( 2019 ). Influence of age and investment experience on behavioural biases of equity investors . International Journal of Creative Research Thoughts (IJCRT) , 9 ( 3 ), ISSN: 2320-2882 , 5409 – 5423 .

Sulistiawan , D. , & Wijaya , R. ( 2015 ). The role of an expert recommendation in group discussion to minimize recency bias: An experimental study in investing decision . International Journal of Applied Business and Economic Research , 13 , 4933 – 4943 .

Trehan ( 2016 ). A study of existence of overconfidence biases among investors and its impact on investment decision .

Trehan , T. , & Sinha , A. K. ( 2021 ). A study of confirmation bias among online investors in virtual communities . International Journal of Electronic Finance , 10 ( 3 ), 159 – 179 .

Tupe , V. A. , & Lokhande , M. A. ( 2021 ). A study of impact of behavioural biases on investor’s investment decision .

Vaid , A. , & Chaudhary , R. ( 2022 ). Review paper on impact of behavioral biases in financial decision- making . World Journal of Advanced Research and Reviews , 16 , 989 – 997 .

van Noordt , C. , & Misuraca , G. ( 2022 ). Artificial intelligence for the public sector: Results of landscaping the use of AI in government across the European Union. Government Information Quarterly , 39 ( 3 ), 101714 .

Von Bergen , C. W. , & Bressler ( 2018 ). Confirmation bias in entrepreneurship . Journal of Management Policy and Practice , 19 ( 3 ), 74 – 84 .

Vries , A. D. , Erasmus , P. D. , & Gerber , C. ( 2017 ). The familiar versus the unfamiliar: Familiarity bias amongst individual investors . Acta Commercii , 17 ( 1 ), 1 – 10 .

Weixiang , S. , Qamruzzaman , M. , Rui , W. , & Kler , R. ( 2022 ). An empirical assessment of financial literacy and behavioral biases on investment decision: Fresh evidence from small investor perception . Frontiers in Psychology , 13 , 977444 .

Zahera , S. A. , & Bansal , R. ( 2018 ). Do investors exhibit behavioral biases in investment decision making? A systematic review . Qualitative Research in Financial Markets , 10 ( 2 ).

Zaleskiewicz , T. , & Gasiorowska , A. ( 2021 ). Evaluating experts may serve psychological needs: Self-esteem, bias blind spot, and processing fluency explain confirmation effect in assessing financial advisors’ authority . Journal of Experimental Psychology: Applied , 27 ( 1 ), 27 – 45 .

Związek , T. , Korzo , M. A. E. , Przybyłowicz , O. M. , Górny , M. , & Kożuchowski , A. ( 2015 ). Acta Poloniae Historica . T . 111 ( 2015 ), Reviews. Acta Poloniae Historica , 111 , 179 - 210 .

Further reading

Authorone Decisions: Mediating Role of Risk Tolerance . International Journal of Research and Innovation in Social Science , 3 ( 8 ).

authorthree Systematic literature review and future research agenda . Journal of Enterprise and Development (JED) , 4 ( 1 ), 157 – 179 .

authortwo Empirical Study on Executives of Financial Service Sector . International Journal of Recent Technology and Engineering , 7 .

Bashir , T. , Rasheed , S. , Raftar , S. , Fatima , S. , & Maqsood , S. ( 2013 ). Impact of behavioral biases on investor decision making: Male vs female . Journal of Business and Management , 10 ( 3 ), 60 – 68 .

Bashir , T. , Mehmood , F. , & Khan , A. ( 2019 ). Comforting investments are rarely profitable: Impediments in investor decision making . Global Social Sciences Review .

Corzo , T. , & Prat , M. ( 2014 ). Vaquero E, behavioral finance in joseph de la Vega's confusion de Confusiones . Journal of Behavioral Finance, Journal of Behavioural Finance , 15 ( 4 ), 341 – 350 .

Deacon , B. , & Abramowitz , J. ( 2022 ). Is hypochondriasis related to obsessive-compulsive disorder, panic disorder, or both? An empirical evaluation . Journal of Cognitive Psychotherapy , 22 ( 2 ).

Faisal , S. , Shurideh , A. , Dmour , A. S. , & Al-Dmour , R. ( 2021 ). Understanding the influences of cognitive biases on financial decision making during normal and COVID-19 pandemic situation in the United Arab Emirates .

Jain , J. , Walia , N. , & Gupta , S. ( 2020 ). Evaluation of behavioral biases affecting investment decision making of individual equity investors by fuzzy analytic hierarchy process . Review of Behavioral Finance , 12 ( 3 ), 297 – 314 .

Jain , J. , Walia , N. , Gupta , S. , Aggarwal , K. , & Singh , S. ( 2022 ). A fuzzy analytical hierarchy process framework for stock selection in the Indian stock market . Journal of Pubic Affairs , 22 ( 4 ).

Mohanty , S. , Patnaik , B. C. M. , Satpathy , I. , & Sahoo , S. K. ( 2022 ). A study on cognitive factors affecting decision-making of investors during covid-19 .

Sahoo , S. K. , & Panda , J. ( 2017 ). Impact of corporate disclosure on investors' attractiveness . Amity Business Journal , 6 ( 2 ), 15 – 19 .

Singh , R. , Deb , S. , & Agarwal , S. ( 2022 ). Exhibition of familiarity bias among mutual fund investors: A study on bank employees . ABS International Journal , 9 ( 2 ), 44 – 48 .

Zaleskiewicz , T. , Gasiorowska , A. , Stasiuk , K. , Maksymiuk , R. , & Bar-Tal , Y. ( 2017 ). Lay evaluation of financial experts: The action advice effect and confirmation bias . Frontiers in Psychology , 7 , 1476 .

Acknowledgements

Corrigendum: It has come to the attention of the publisher that the article, Mohanty, S. (2023), “Cognitive biases and financial decisions of potential investors during Covid-19: an exploration”, Arab Gulf Journal of Scientific Research , Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/AGJSR-12-2022-0296 , did not include the authors B.C.M. Patnaik, Ipseeta Satpathy, and Suresh Kumar Sahoo as authors.

Our guidelines make it clear that anyone who made a significant contribution to the paper must be included as an author at submission.

B.C.M. Patnaik, Ipseeta Satpathy, and Suresh Kumar Sahoo have been included as authors in the online version of this paper.

Corresponding author

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