Superiority Illusion Arises from Resting-State Brain Networks Modulated by Dopamine
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
A Task-Based Taxonomy of Cognitive Biases for Information Visualization
A Task-based Taxonomy of Cognitive Biases for Information Visualization Evanthia Dimara, Steven Franconeri, Catherine Plaisant, Anastasia Bezerianos, and Pierre Dragicevic Three kinds of limitations The Computer The Display 2 Three kinds of limitations The Computer The Display The Human 3 Three kinds of limitations: humans • Human vision ️ has limitations • Human reasoning 易 has limitations The Human 4 ️Perceptual bias Magnitude estimation 5 ️Perceptual bias Magnitude estimation Color perception 6 易 Cognitive bias Behaviors when humans consistently behave irrationally Pohl’s criteria distilled: • Are predictable and consistent • People are unaware they’re doing them • Are not misunderstandings 7 Ambiguity effect, Anchoring or focalism, Anthropocentric thinking, Anthropomorphism or personification, Attentional bias, Attribute substitution, Automation bias, Availability heuristic, Availability cascade, Backfire effect, Bandwagon effect, Base rate fallacy or Base rate neglect, Belief bias, Ben Franklin effect, Berkson's paradox, Bias blind spot, Choice-supportive bias, Clustering illusion, Compassion fade, Confirmation bias, Congruence bias, Conjunction fallacy, Conservatism (belief revision), Continued influence effect, Contrast effect, Courtesy bias, Curse of knowledge, Declinism, Decoy effect, Default effect, Denomination effect, Disposition effect, Distinction bias, Dread aversion, Dunning–Kruger effect, Duration neglect, Empathy gap, End-of-history illusion, Endowment effect, Exaggerated expectation, Experimenter's or expectation bias, -
MEMORY BIASES and DEPRESSIVE REALISM a Dissertation Submitted
MEMORY BIASES AND DEPRESSIVE REALISM A dissertation submitted to Kent State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy by Michael T. Moore August 2009 Dissertation written by Michael T. Moore B.A., State University of New York at Stony Brook, 2000 M.A., Kent State University, 2005 Ph.D., Kent State University, 2009 Approved by __________________________________, David M. Fresco, Ph.D., Co-Chair, Doctoral Dissertation Committee __________________________________, T. John Akamatsu, Ph.D., Co-Chair, Doctoral Dissertation Committee __________________________________, Maria S. Zaragoza, Ph. D., Member, Doctoral Dissertation Committee __________________________________, Barbara L. Drew, Ph.D., R.N., Member, Doctoral Dissertation Committee __________________________________, Richard Adams, Ph. D., College of Arts & Sciences Graduate Faculty Representative Accepted by __________________________________, Doug L. Delahanty, Ph.D., Interim Chair, Department of Psychology __________________________________, Timothy S. Moerland, Ph.D., Dean, College of Arts and Sciences ii TABLE OF CONTENTS ACKNOWLEDGMENTS………………………………………………………...………………iv INTRODUCTION…………………………………………………………………………………1 Hypotheses……………………………………………………………………………….17 METHOD………………………………………………………………………………………...18 Participants and Procedure……………………………………………………………….18 Power Analysis…………………………………………………………………………..19 Measures…………………………………………………………………………………20 RESULTS………………………………………………………………………………………...26 DISCUSSION…………………………………………………………………………………….35 -
Optimism Bias and Illusion of Control in Finance Professionals
November, 28th – P13 Look ma(rket), No Hands! Optimism Bias and Illusion of Control in Finance Professionals Francesco Marcatto, Giovanni Colangelo, Donatella Ferrante University of Trieste, Department of Life Sciences, Psychology Unit Gaetano Kanizsa, Trieste, Italy Abstract behavior, whereas extreme optimists display financial habits The optimism bias is the tendency to judge one’s own risk as and behavior that are generally not considered prudent (Puri less than the risk of others. In the present study we found that & Robinson, 2007). Moreover, analysts have been shown to also finance professionals (N = 60) displayed an optimism be systematically overoptimistic in their forecasts about the bias when forecasting the return of an investment made by earnings potential of firms at the time of their initial public themselves or by a colleague of the same expertise. Using a offerings, and this optimism increases as the length of the multidimensional approach to the assessment of risk forecast period increases (McNichols & O’Brien, 1997; perception, we found that participants’ forecasts were biased Rajan & Servaes, 1997). To the best of our knowledge, not because they judged negative consequences as less likely for themselves, but because they were overconfident in their however, the presence of the optimism bias/unrealistic ability to avoid and control them. comparative optimism in financial risk (i.e., judging personal risk of losing money as lower than other investors’ Keywords: Optimism bias; unrealistic comparative risk) has never been subject of empirical testing. optimism; financial risk; investment risk; perceived control; illusion of control. The main aim of this study is to investigate whether the optimism bias can be observed also in experts in the field of Introduction financial risk. -
Power and Illusory Control
Research Paper No. 2009 Illusory Control: A Generative Force Behind Power’s Far-Reaching Effects Nathanael J. Fast Deborah H. Gruenfeld Niro Sivanathan Adam D. Galinsky December 2008 R ESEARCH P APER S ERIES Electronic copy available at: http://ssrn.com/abstract=1314952 Running Head: POWER AND ILLUSORY CONTROL Illusory Control: A Generative Force Behind Power’s Far-Reaching Effects Nathanael J. Fast and Deborah H Gruenfeld Stanford University Niro Sivanathan London Business School Adam D. Galinsky Northwestern University *In press at Psychological Science Please address correspondence concerning this article to Nathanael J. Fast, Department of Organizational Behavior, Stanford University, 518 Memorial Way, Stanford, CA 94305; email: [email protected]. Electronic copy available at: http://ssrn.com/abstract=1314952 Power and Illusory Control 2 Abstract Three experiments demonstrated that the experience of power leads to an illusion of personal control. Regardless of whether power was experientially primed (Experiments 1 and 3) or manipulated through manager-subordinate roles (Experiment 2), it led to perceived control over outcomes that were beyond the reach of the powerholder. Furthermore, this illusory control mediated the influence of power on several self-enhancement and approach-related effects found in the power literature, including optimism (Experiment 2), self-esteem (Experiment 3), and action-orientation (Experiment 3), demonstrating its theoretical importance as a generative cause and driving force behind many of power’s far-reaching effects. A fourth experiment ruled out an alternative explanation: that positive mood, rather than illusory control, is at the root of power’s effects. The discussion considers implications for existing and future research on the psychology of power, perceived control, and positive illusions. -
Behavioral Biases on Investment Decision: a Case Study in Indonesia
Kartini KARTINI, Katiya NAHDA / Journal of Asian Finance, Economics and Business Vol 8 No 3 (2021) 1231–1240 1231 Print ISSN: 2288-4637 / Online ISSN 2288-4645 doi:10.13106/jafeb.2021.vol8.no3.1231 Behavioral Biases on Investment Decision: A Case Study in Indonesia Kartini KARTINI1, Katiya NAHDA2 Received: November 30, 2020 Revised: February 07, 2021 Accepted: February 16, 2021 Abstract A shift in perspective from standard finance to behavioral finance has taken place in the past two decades that explains how cognition and emotions are associated with financial decision making. This study aims to investigate the influence of various psychological factors on investment decision-making. The psychological factors that are investigated are differentiated into two aspects, cognitive and emotional aspects. From the cognitive aspect, we examine the influence of anchoring, representativeness, loss aversion, overconfidence, and optimism biases on investor decisions. Meanwhile, from the emotional aspect, the influence of herding behavior on investment decisions is analyzed. A quantitative approach is used based on a survey method and a snowball sampling that result in 165 questionnaires from individual investors in Yogyakarta. Further, we use the One-Sample t-test in testing all hypotheses. The research findings show that all of the variables, anchoring bias, representativeness bias, loss aversion bias, overconfidence bias, optimism bias, and herding behavior have a significant effect on investment decisions. This result emphasizes the influence of behavioral factors on investor’s decisions. It contributes to the existing literature in understanding the dynamics of investor’s behaviors and enhance the ability of investors in making more informed decision by reducing all potential biases. -
MITIGATING COGNITIVE BIASES in RISK IDENTIFICATION: Practitioner Checklist for the AEROSPACE SECTOR
MITIGATING COGNITIVE BIASES IN RISK IDENTIFICATION: Practitioner Checklist for the AEROSPACE SECTOR Debra L. Emmons, Thomas A. Mazzuchi, Shahram Sarkani, and Curtis E. Larsen This research contributes an operational checklist for mitigating cogni- tive biases in the aerospace sector risk management process. The Risk Identification and Evaluation Bias Reduction Checklist includes steps for grounding the risk identification and evaluation activities in past project experiences through historical data, and emphasizes the importance of incorporating multiple methods and perspectives to guard against optimism and a singular project instantiation-focused view. The authors developed a survey to elicit subject matter expert judgment on the value of the check- list to support its use in government and industry as a risk management tool. The survey also provided insights on bias mitigation strategies and lessons learned. This checklist addresses the deficiency in the literature in providing operational steps for the practitioner to recognize and implement strategies for bias reduction in risk management in the aerospace sector. DOI: https://doi.org/10.22594/dau.16-770.25.01 Keywords: Risk Management, Optimism Bias, Planning Fallacy, Cognitive Bias Reduction Mitigating Cognitive Biases in Risk Identification http://www.dau.mil January 2018 This article and its accompanying research contribute an operational FIGURE 1. RESEARCH APPROACH Risk Identification and Evaluation Bias Reduction Checklist for cognitive bias mitigation in risk management for the aerospace sector. The checklist Cognitive Biases & Bias described herein offers a practical and implementable project management Enabling framework to help reduce biases in the aerospace sector and redress the Conditions cognitive limitations in the risk identification and analysis process. -
The Optimism Bias: a Cognitive Neuroscience Perspective 1
Xjenza Online - Journal of The Malta Chamber of Scientists www.xjenza.org Doi: http://dx.medra.org/10.7423/XJENZA.2014.1.04 Review Article The Optimism Bias: A cognitive neuroscience perspective Claude J. Bajada1 1Institute of Cognitive Neuroscience, University College London, WC1N 3AR, UK Abstract. The optimism bias is a well-established 2 The Study of the Optimism psychological phenomenon. Its study has implications that are far reaching in fields as diverse as mental Bias health and economic theory. With the emerging field of cognitive neuroscience and the advent of advanced neu- roimaging techniques, it has been possible to investigate the neural basis of the optimism bias and to understand One way for scientists to test the optimism bias in the in which neurological conditions this natural bias fails. laboratory is to ask an individual to predict his chances This review first defines the optimism bias, discusses its of experiencing an event and then following up to see implications and reviews the literature that investigates whether the event transpired. The problem with this its neural basis. Finally some potential pitfalls in approach is that the outcome of an event does not al- experimental design are discussed. ways accurately represent the person's prior chances to attain that outcome; this is especially true when the outcome is a binary one. For example, we know that an Keywords Optimism bias - cognitive neuroscience - individual has an infinitesimally small chance at winning psychology - neural basis. the national lottery. If Peter predicts that he has a 75% chance of winning next week's lottery and then happens to win the lottery, this does not mean that Peter was 1 Introduction actually pessimistic in his prediction. -
Impact of Illusion of Control on Perceived Efficiency in Pakistani Financial Markets
Impact of Illusion…. Abasyn Journal of Social Sciences Vol. 5 No. 2 Impact of Illusion of Control on Perceived Efficiency in Pakistani Financial Markets Sidra Ajmal1 Maria Mufti2 Dr. Zulfiqar Ali Shah3 Abstract The purpose of this study is to test impact of illusion of control on perceived market efficiency. On the basis of literature review, questionnaire was developed. The study is carried out on sample of convenience of investors, financial analyst and finance scholars of Islamabad/ Rawalpindi. Questionnaire comprises of 15 items related to three forms of market efficiency and particular bias (illusion of control). The findings of the regression show significant results. This means that illusion of control has impact on perceived efficiency of Pakistani financial market. This study focuses on only individual located in Islamabad/Rawalpindi, and the sample size is also small due to time constraints. Further research can be done on a bigger scale to get the more general conclusions. This study will help investors to identify their biases and then formulate different strategies to reduce their irrational behavior. Key words: Illusion of control, market efficiency 1Sidra Ajmal, MS (Scholar), SZABIST, Islamabad. 2Maria Mufti, MS (Scholar), SZABIST, Islamabad. 3Dr. Zulfiqar Ali Shah, Associate Professor, SZABIST, Islamabad. Illusion of control is a psychological term referring to the tendency of people to overestimate their abilities to control any event, where as market efficiency is the phenomena primarily explained by Fama (1970). He had created a hypothesis that Sidra Ajmal, Maria Mufti, & Dr. Zulfiqar Ali Shah 100 Impact of Illusion…. Abasyn Journal of Social Sciences Vol. 5 No. -
Social Psychology Glossary
Social Psychology Glossary This glossary defines many of the key terms used in class lectures and assigned readings. A Altruism—A motive to increase another's welfare without conscious regard for one's own self-interest. Availability Heuristic—A cognitive rule, or mental shortcut, in which we judge how likely something is by how easy it is to think of cases. Attractiveness—Having qualities that appeal to an audience. An appealing communicator (often someone similar to the audience) is most persuasive on matters of subjective preference. Attribution Theory—A theory about how people explain the causes of behavior—for example, by attributing it either to "internal" dispositions (e.g., enduring traits, motives, values, and attitudes) or to "external" situations. Automatic Processing—"Implicit" thinking that tends to be effortless, habitual, and done without awareness. B Behavioral Confirmation—A type of self-fulfilling prophecy in which people's social expectations lead them to behave in ways that cause others to confirm their expectations. Belief Perseverance—Persistence of a belief even when the original basis for it has been discredited. Bystander Effect—The tendency for people to be less likely to help someone in need when other people are present than when they are the only person there. Also known as bystander inhibition. C Catharsis—Emotional release. The catharsis theory of aggression is that people's aggressive drive is reduced when they "release" aggressive energy, either by acting aggressively or by fantasizing about aggression. Central Route to Persuasion—Occurs when people are convinced on the basis of facts, statistics, logic, and other types of evidence that support a particular position. -
Cognitive Biases in Software Engineering: a Systematic Mapping Study
Cognitive Biases in Software Engineering: A Systematic Mapping Study Rahul Mohanani, Iflaah Salman, Burak Turhan, Member, IEEE, Pilar Rodriguez and Paul Ralph Abstract—One source of software project challenges and failures is the systematic errors introduced by human cognitive biases. Although extensively explored in cognitive psychology, investigations concerning cognitive biases have only recently gained popularity in software engineering research. This paper therefore systematically maps, aggregates and synthesizes the literature on cognitive biases in software engineering to generate a comprehensive body of knowledge, understand state of the art research and provide guidelines for future research and practise. Focusing on bias antecedents, effects and mitigation techniques, we identified 65 articles (published between 1990 and 2016), which investigate 37 cognitive biases. Despite strong and increasing interest, the results reveal a scarcity of research on mitigation techniques and poor theoretical foundations in understanding and interpreting cognitive biases. Although bias-related research has generated many new insights in the software engineering community, specific bias mitigation techniques are still needed for software professionals to overcome the deleterious effects of cognitive biases on their work. Index Terms—Antecedents of cognitive bias. cognitive bias. debiasing, effects of cognitive bias. software engineering, systematic mapping. 1 INTRODUCTION OGNITIVE biases are systematic deviations from op- knowledge. No analogous review of SE research exists. The timal reasoning [1], [2]. In other words, they are re- purpose of this study is therefore as follows: curring errors in thinking, or patterns of bad judgment Purpose: to review, summarize and synthesize the current observable in different people and contexts. A well-known state of software engineering research involving cognitive example is confirmation bias—the tendency to pay more at- biases. -
Infographic I.10
The Digital Health Revolution: Leaving No One Behind The global AI in healthcare market is growing fast, with an expected increase from $4.9 billion in 2020 to $45.2 billion by 2026. There are new solutions introduced every day that address all areas: from clinical care and diagnosis, to remote patient monitoring to EHR support, and beyond. But, AI is still relatively new to the industry, and it can be difficult to determine which solutions can actually make a difference in care delivery and business operations. 59 Jan 2021 % of Americans believe returning Jan-June 2019 to pre-coronavirus life poses a risk to health and well being. 11 41 % % ...expect it will take at least 6 The pandemic has greatly increased the 65 months before things get number of US adults reporting depression % back to normal (updated April and/or anxiety.5 2021).4 Up to of consumers now interested in telehealth going forward. $250B 76 57% of providers view telehealth more of current US healthcare spend % favorably than they did before COVID-19.7 could potentially be virtualized.6 The dramatic increase in of Medicare primary care visits the conducted through 90% $3.5T telehealth has shown longevity, with rates in annual U.S. health expenditures are for people with chronic and mental health conditions. since April 2020 0.1 43.5 leveling off % % Most of these can be prevented by simple around 30%.8 lifestyle changes and regular health screenings9 Feb. 2020 Apr. 2020 OCCAM’S RAZOR • CONJUNCTION FALLACY • DELMORE EFFECT • LAW OF TRIVIALITY • COGNITIVE FLUENCY • BELIEF BIAS • INFORMATION BIAS Digital health ecosystems are transforming• AMBIGUITY BIAS • STATUS medicineQUO BIAS • SOCIAL COMPARISONfrom BIASa rea• DECOYctive EFFECT • REACTANCEdiscipline, • REVERSE PSYCHOLOGY • SYSTEM JUSTIFICATION • BACKFIRE EFFECT • ENDOWMENT EFFECT • PROCESSING DIFFICULTY EFFECT • PSEUDOCERTAINTY EFFECT • DISPOSITION becoming precise, preventive,EFFECT • ZERO-RISK personalized, BIAS • UNIT BIAS • IKEA EFFECT and • LOSS AVERSION participatory. -
Effects of Mindset on Positive Illusions
EffectEffectss ooff MindseMindsett oonn PositivPositivee IllusionIllusionss ShelleSheJleyy EE.. TayloTaylorr PetePeterr MM.. GollwitzeGoUwitzerr UniversitUniversityy oofCalifomia,f California, LoLoss AngeleAngeless UniversitUniversityy ooff KonstanKonstanzz SS.. EE.. TayloTayklrandr and JJ.. DD.. Brown'Brown'ss (1988(1988)) positioposilionn thalhalt mentallmentallyy healthhealthyy peoplpoopIee exhibiexhibitt positivpositivee illusioni11lol5ionss raiseraisess aI dilemmadikmma:: HowHow dodo pewitpeople funC1Wnfunction effectivelyeffectively ifthc:irif their perceptionsperceptions arcare positivelypositively biased?biased? UsingUsing Gollwitzer'Go/lwiucr'ss deliberative-implementadeliberative-implementall mindseffiindsett distinctiondistinction,, wv.ee assesseassessedd whethewhetherr peoplpeoplee iinn a deliberdeliber- ativativee mindsemioosett shoshoww leslesss evidenceevidence ofof positivpositivee illusionillusionss thathann peoplpc~e inin anan implementalimplemental mindsetmind$Cl... ParticPartk·- ipantipanlSs completecompletedd aa mindsemindsett tastaskk anandd assessmentassessmentss ooff moodmood,, self-perceptionsself-perceptions,, anandd perceiveperceivedd (invul(in)vul- nerabilitnerabilityy t10o riskrisk.. DeliberatioDeliberationn leledd tloworsenedo worsened moodmood,, greatevealerr perceiveperceivedd riskrisk,, anandd poorepoorerr self-percepself.percep- tionstiool,, relativrelativee ttoo implementationimplementation;; controcontroll (n(noo mindsetmindset)) participantparticipantss typicalltypicallyy scorescoredd iinn betweenbet~n.