Behavioural Insights It Ain’T What You Do, It’S the Way That You Do It
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Ambiguity Aversion in Qualitative Contexts: a Vignette Study
Ambiguity aversion in qualitative contexts: A vignette study Joshua P. White Melbourne School of Psychological Sciences University of Melbourne Andrew Perfors Melbourne School of Psychological Sciences University of Melbourne Abstract Most studies of ambiguity aversion rely on experimental paradigms involv- ing contrived monetary bets. Thus, the extent to which ambiguity aversion is evident outside of such contexts is largely unknown, particularly in those contexts which cannot easily be reduced to numerical terms. The present work seeks to understand whether ambiguity aversion occurs in a variety of different qualitative domains, such as work, family, love, friendship, exercise, study and health. We presented participants with 24 vignettes and measured the degree to which they preferred risk to ambiguity. In a separate study we asked participants for their prior probability estimates about the likely outcomes in the ambiguous events. Ambiguity aversion was observed in the vast majority of vignettes, but at different magnitudes. It was predicted by gain/loss direction but not by the prior probability estimates (with the inter- esting exception of the classic Ellsberg ‘urn’ scenario). Our results suggest that ambiguity aversion occurs in a wide variety of qualitative contexts, but to different degrees, and may not be generally driven by unfavourable prior probability estimates of ambiguous events. Corresponding Author: Joshua P. White ([email protected]) AMBIGUITY AVERSION IN QUALITATIVE CONTEXTS: A VIGNETTE STUDY 2 Introduction The world is replete with the unknown, yet people generally prefer some types of ‘unknown’ to others. Here, an important distinction exists between risk and uncertainty. As defined by Knight (1921), risk is a measurable lack of certainty that can be represented by numerical probabilities (e.g., “there is a 50% chance that it will rain tomorrow”), while ambiguity is an unmeasurable lack of certainty (e.g., “there is an unknown probability that it will rain tomorrow”). -
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, -
Enhancing the Production Effect in Memory: Singing, Underlying Mechanisms, and Applications
ENHANCING THE PRODUCTION EFFECT IN MEMORY: SINGING, UNDERLYING MECHANISMS, AND APPLICATIONS by Chelsea Karmel Quinlan Submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy at Dalhousie University Halifax, Nova Scotia December 2017 © Copyright by Chelsea Karmel Quinlan, 2017 TABLE OF CONTENTS LIST OF TABLES .......................................................................... v LIST OF FIGURES ....................................................................... vi ABSTRACT ................................................................................ vii LIST OF ABBREVIATIONS USED .................................................. viii ACKNOWLEDGEMENTS ................................................................ ix CHAPTER 1: INTRODUCTION ......................................................... 1 1.1 INTRODUCTION .................................................................................................. 2 1.2 HISTORY OF THE PRODUCTION EFFECT ........................................................... 4 1.3 A REVIEW OF THEORETICAL PERSPECTIVES .................................................. 11 1.4 MUSIC AND MEMORY ....................................................................................... 17 1.5 CHAPTER SUMMARY, RATIONALE, AND CURRENT EXPERIMENTS ................ 19 CHAPTER 2: EXTENDING THE BOUNDARIES OF THE PRODUCTION EFFECT .................................................................................... 24 2.1 ABSTRACT ........................................................................................................ -
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. -
Detection of Confirmation and Distinction Biases in Visual
EuroVis Workshop on Trustworthy Visualization (TrustVis) (2019) R. Kosara, K. Lawonn, L. Linsen, and N. Smit (Editors) Detection of Confirmation and Distinction Biases in Visual Analytics Systems . A. Nalcaci , D. Girgin , S. Balki , F. Talay, H. A. Boz and S. Balcisoy 1Sabanci University, Faculty of Engineering & Natural Sciences, Istanbul, Turkey Abstract Cognitive bias is a systematic error that introduces drifts and distortions in the human judgment in terms of visual decompo- sition in the direction of the dominant instance. It has a significant role in decision-making process by means of evaluation of data visualizations. This paper elaborates on the experimental depiction of two cognitive bias types, namely Distinction Bias and Confirmation Bias, through the examination of cognate visual experimentations. The main goal of this implementation is to indicate the existence of cognitive bias in visual analytics systems through the adjustment of data visualization and crowdsourc- ing in terms of confirmation and distinction biases. Two distinct surveys that include biased and unbiased data visualizations which are related to a given data set were established in order to detect and measure the level of existence of introduced bias types. Practice of crowdsourcing which is provided by Amazon Mechanical Turk have been used for experimentation purposes through prepared surveys. Results statistically indicate that both distinction and confirmation biases has substantial effect and prominent significance on decision-making process. CCS Concepts • Human-centered computing ! Empirical studies in visualization; Visualization design and evaluation methods; 1. Introduction cording to existing studies, usual heuristic errors involve confirma- tion bias, which characterizes people’s approaches to receive the Visual perception is the combination of physical and thoughtful de- confirmatory corroboration of a pre-existing hypothesis and dis- sign that provides the ability to interpret the environment and the miss contrary information. -
A Neural Network Framework for Cognitive Bias
fpsyg-09-01561 August 31, 2018 Time: 17:34 # 1 HYPOTHESIS AND THEORY published: 03 September 2018 doi: 10.3389/fpsyg.2018.01561 A Neural Network Framework for Cognitive Bias Johan E. Korteling*, Anne-Marie Brouwer and Alexander Toet* TNO Human Factors, Soesterberg, Netherlands Human decision-making shows systematic simplifications and deviations from the tenets of rationality (‘heuristics’) that may lead to suboptimal decisional outcomes (‘cognitive biases’). There are currently three prevailing theoretical perspectives on the origin of heuristics and cognitive biases: a cognitive-psychological, an ecological and an evolutionary perspective. However, these perspectives are mainly descriptive and none of them provides an overall explanatory framework for the underlying mechanisms of cognitive biases. To enhance our understanding of cognitive heuristics and biases we propose a neural network framework for cognitive biases, which explains why our brain systematically tends to default to heuristic (‘Type 1’) decision making. We argue that many cognitive biases arise from intrinsic brain mechanisms that are fundamental for the working of biological neural networks. To substantiate our viewpoint, Edited by: we discern and explain four basic neural network principles: (1) Association, (2) Eldad Yechiam, Technion – Israel Institute Compatibility, (3) Retainment, and (4) Focus. These principles are inherent to (all) neural of Technology, Israel networks which were originally optimized to perform concrete biological, perceptual, Reviewed by: and motor functions. They form the basis for our inclinations to associate and combine Amos Schurr, (unrelated) information, to prioritize information that is compatible with our present Ben-Gurion University of the Negev, Israel state (such as knowledge, opinions, and expectations), to retain given information Edward J. -
Elaborative Encoding, the Ancient Art of Memory, and the Hippocampus
View metadata, citation and similar papers at core.ac.uk brought to you by CORE BEHAVIORAL AND BRAIN SCIENCES (2013) 36, 589–659 provided by RERO DOC Digital Library doi:10.1017/S0140525X12003135 Such stuff as dreams are made on? Elaborative encoding, the ancient art of memory, and the hippocampus Sue Llewellyn Faculty of Humanities, University of Manchester, Manchester M15 6PB, United Kingdom http://www.humanities.manchester.ac.uk [email protected] Abstract: This article argues that rapid eye movement (REM) dreaming is elaborative encoding for episodic memories. Elaborative encoding in REM can, at least partially, be understood through ancient art of memory (AAOM) principles: visualization, bizarre association, organization, narration, embodiment, and location. These principles render recent memories more distinctive through novel and meaningful association with emotionally salient, remote memories. The AAOM optimizes memory performance, suggesting that its principles may predict aspects of how episodic memory is configured in the brain. Integration and segregation are fundamental organizing principles in the cerebral cortex. Episodic memory networks interconnect profusely within the cortex, creating omnidirectional “landmark” junctions. Memories may be integrated at junctions but segregated along connecting network paths that meet at junctions. Episodic junctions may be instantiated during non–rapid eye movement (NREM) sleep after hippocampal associational function during REM dreams. Hippocampal association involves relating, binding, and integrating episodic memories into a mnemonic compositional whole. This often bizarre, composite image has not been present to the senses; it is not “real” because it hyperassociates several memories. During REM sleep, on the phenomenological level, this composite image is experienced as a dream scene. -
Ack Baraly Kylee Tamera 202
EXTRINSIC AND INTRINSIC FACTORS INFLUENCING THE POSITIVE MEMORY BIAS IN AGING KYLEE TAMERA ACK BARALY A thesis submitted in partial fulfillment of the requirements for the Doctorate in Philosophy degree in Experimental Psychology at the University of Ottawa; Doctorate in Philosophy degree in Cognitive Sciences, Cognitive Psychology and Neurocognition at the Communauté Université Grenoble Alpes. School of Psychology Laboratoire de Psychologie et NeuroCognition Faculty of Social Sciences École Doctorale Ingénierie pour la Santé, la Cognition et l’Environnement University of Ottawa Communauté Université Grenoble Alpes ©Kylee Tamera Ack Baraly, Ottawa, Canada, 2020 K. T. ACK BARALY PH.D. DISSERTATION ii Acknowledgements To Patrick and Pascal, thank you for being two of the most incredibly supportive and knowledgeable supervisors. You helped enlighten my scientific mind, pushing me to question better, reflect deeper, and learn more. You are both inspiring as researchers and as mentors. And I am forever grateful to have gone through this journey with you. I would also like to thank all of the members of the Neuropsychology Lab (Ottawa) and Laboratoire de Psychologie et NeuroCognition (Chambéry and Grenoble). Thank you to all of the Master's students, honours thesis students, UROP students, lab volunteers, and research assistants who helped in more ways than I can count. Your enthusiasm and dedication to this work are truly appreciated. I am thankful for all the additional support provided by the INSPIRE Lab (Ottawa), which made collecting reliable data more efficient. A very special thank you to all of the research participants who so generously gave their time to this work. You are the pillar of scientific research and without you, none of this would have been possible. -
Communication Science to the Public
David M. Berube North Carolina State University ▪ HOW WE COMMUNICATE. In The Age of American Unreason, Jacoby posited that it trickled down from the top, fueled by faux-populist politicians striving to make themselves sound approachable rather than smart. (Jacoby, 2008). EX: The average length of a sound bite by a presidential candidate in 1968 was 42.3 seconds. Two decades later, it was 9.8 seconds. Today, it’s just a touch over seven seconds and well on its way to being supplanted by 140/280- character Twitter bursts. ▪ DATA FRAMING. ▪ When asked if they truly believe what scientists tell them, NEW ANTI- only 36 percent of respondents said yes. Just 12 percent expressed strong confidence in the press to accurately INTELLECTUALISM: report scientific findings. ▪ ROLE OF THE PUBLIC. A study by two Princeton University researchers, Martin TRENDS Gilens and Benjamin Page, released Fall 2014, tracked 1,800 U.S. policy changes between 1981 and 2002, and compared the outcome with the expressed preferences of median- income Americans, the affluent, business interests and powerful lobbies. They concluded that average citizens “have little or no independent influence” on policy in the U.S., while the rich and their hired mouthpieces routinely get their way. “The majority does not rule,” they wrote. ▪ Anti-intellectualism and suspicion (trends). ▪ Trump world – outsiders/insiders. ▪ Erasing/re-writing history – damnatio memoriae. ▪ False news. ▪ Infoxication (CC) and infobesity. ▪ Aggregators and managed reality. ▪ Affirmation and confirmation bias. ▪ Negotiating reality. ▪ New tribalism is mostly ideational not political. ▪ Unspoken – guns, birth control, sexual harassment, race… “The amount of technical information is doubling every two years. -
Safe-To-Fail Probe Has…
Liz Keogh [email protected] If a project has no risks, don’t do it. @lunivore The Innovaon Cycle Spoilers Differentiators Commodities Build on Cynefin Complex Complicated sense, probe, analyze, sense, respond respond Obvious Chaotic sense, act, categorize, sense, respond respond With thanks to David Snowden and Cognitive Edge EsBmang Complexity 5. Nobody has ever done it before 4. Someone outside the org has done it before (probably a compeBtor) 3. Someone in the company has done it before 2. Someone in the team has done it before 1. We all know how to do it. Esmang Complexity 5 4 3 Analyze Probe (Break it down) (Try it out) 2 1 Fractal beauty Feature Scenario Goal Capability Story Feature Scenario Vision Story Goal Code Capability Feature Code Code Scenario Goal A Real ProjectWhoops, Don’t need forgot this… Can’t remember Feature what this Scenario was for… Goal Capability Story Feature Scenario Vision Story Goal Code Capability Feature Code Code Scenario Goal Oops, didn’t know about Look what I that… found! A Real ProjectWhoops, Don’t need forgot this… Can’t remember Um Feature what this Scenario was for… Goal Oh! Capability Hmm! Story FeatureOoh, look! Scenario Vision Story GoalThat’s Code funny! Capability Feature Code Er… Code Scenario Dammit! Oops! Oh F… InteresBng! Goal Sh..! Oops, didn’t know about Look what I that… found! We are uncovering be^er ways of developing so_ware by doing it Feature Scenario Goal Capability Story Feature Scenario Vision Story Goal Code Capability Feature Code Code Scenario Goal We’re discovering how to -
Ilidigital Master Anton 2.Indd
services are developed to be used by humans. Thus, understanding humans understanding Thus, humans. by used be to developed are services obvious than others but certainly not less complex. Most products bioengineering, and as shown in this magazine. Psychology mightbusiness world. beBe it more the comparison to relationships, game elements, or There are many non-business flieds which can betransfered to the COGNTIVE COGNTIVE is key to a succesfully develop a product orservice. is keytoasuccesfullydevelopproduct BIASES by ANTON KOGER The Power of Power The //PsychologistatILI.DIGITAL WE EDIT AND REINFORCE SOME WE DISCARD SPECIFICS TO WE REDUCE EVENTS AND LISTS WE STORE MEMORY DIFFERENTLY BASED WE NOTICE THINGS ALREADY PRIMED BIZARRE, FUNNY, OR VISUALLY WE NOTICE WHEN WE ARE DRAWN TO DETAILS THAT WE NOTICE FLAWS IN OTHERS WE FAVOR SIMPLE-LOOKING OPTIONS MEMORIES AFTER THE FACT FORM GENERALITIES TO THEIR KEY ELEMENTS ON HOW THEY WERE EXPERIENCED IN MEMORY OR REPEATED OFTEN STRIKING THINGS STICK OUT MORE SOMETHING HAS CHANGED CONFIRM OUR OWN EXISTING BELIEFS MORE EASILY THAN IN OURSELVES AND COMPLETE INFORMATION way we see situations but also the way we situationsbutalsotheway wesee way the biasesnotonlychange Furthermore, overload. cognitive avoid attention, ore situations, guide help todesign massively can This in. take people information of kind explainhowandwhat ofperception egory First,biasesinthecat andappraisal. ory, self,mem perception, into fourcategories: roughly bedivided Cognitive biasescan within thesesituations. forusers interaction andeasy in anatural situationswhichresults sible toimprove itpos and adaptingtothesebiasesmakes ingiven situations.Reacting ways certain act sively helpstounderstandwhypeople mas into consideration biases ing cognitive Tak humanbehavior. topredict likely less or andmore relevant illusionsare cognitive In each situation different every havior day. -
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.