Cognitive Biases Assisting the Trump Campaign
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Bad Is Stronger Than Good
Review of General Psychology Copyright 2001 by the Educational Publishing Foundation 2001. Vol. 5. No. 4. 323-370 1089-2680/O1/S5.O0 DOI: 10.1037//1089-2680.5.4.323 Bad Is Stronger Than Good Roy F. Baumeister and Ellen Bratslavsky Catrin Finkenauer Case Western Reserve University Free University of Amsterdam Kathleen D. Vohs Case Western Reserve University The greater power of bad events over good ones is found in everyday events, major life events (e.g., trauma), close relationship outcomes, social network patterns, interper- sonal interactions, and learning processes. Bad emotions, bad parents, and bad feedback have more impact than good ones, and bad information is processed more thoroughly than good. The self is more motivated to avoid bad self-definitions than to pursue good ones. Bad impressions and bad stereotypes are quicker to form and more resistant to disconfirmation than good ones. Various explanations such as diagnosticity and sa- lience help explain some findings, but the greater power of bad events is still found when such variables are controlled. Hardly any exceptions (indicating greater power of good) can be found. Taken together, these findings suggest that bad is stronger than good, as a general principle across a broad range of psychological phenomena. Centuries of literary efforts and religious pothesis that bad is stronger than good (see also thought have depicted human life in terms of a Rozin & Royzman, in press). That is, events struggle between good and bad forces. At the that are negatively valenced (e.g., losing metaphysical level, evil gods or devils are the money, being abandoned by friends, and receiv- opponents of the divine forces of creation and ing criticism) will have a greater impact on the harmony. -
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, -
Gender De-Biasing in Speech Emotion Recognition
INTERSPEECH 2019 September 15–19, 2019, Graz, Austria Gender de-biasing in speech emotion recognition Cristina Gorrostieta, Reza Lotfian, Kye Taylor, Richard Brutti, John Kane Cogito Corporation fcgorrostieta,rlotfian,ktaylor,rbrutti,jkaneg @cogitocorp.com Abstract vertisement delivery [9], object classification [10] and image search results for occupations [11] . Machine learning can unintentionally encode and amplify neg- There are several factors which can contribute to produc- ative bias and stereotypes present in humans, be they conscious ing negative bias in machine learning models. One major cause or unconscious. This has led to high-profile cases where ma- is incomplete or skewed training data. If certain demographic chine learning systems have been found to exhibit bias towards categories are missing from the training data, models developed gender, race, and ethnicity, among other demographic cate- on this can fail to generalise when applied to new data contain- gories. Negative bias can be encoded in these algorithms based ing those missing categories. The majority of models deployed on: the representation of different population categories in the in modern technology applications are based on supervised ma- training data; bias arising from manual human labeling of these chine learning and much of the labeled data comes from people. data; as well as modeling types and optimisation approaches. In Labels can include movie reviews, hotel ratings, image descrip- this paper we assess the effect of gender bias in speech emotion tions, audio transcriptions, and perceptual ratings of emotion. recognition and find that emotional activation model accuracy is As people are inherently biased, and because models are an es- consistently lower for female compared to male audio samples. -
The Lightbulb Paradox: Evidence from Two Randomized Experiments
NBER WORKING PAPER SERIES THE LIGHTBULB PARADOX: EVIDENCE FROM TWO RANDOMIZED EXPERIMENTS Hunt Allcott Dmitry Taubinsky Working Paper 19713 http://www.nber.org/papers/w19713 NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge, MA 02138 December 2013 We are grateful to Raj Chetty, Lucas Davis, Stefano DellaVigna, Mushfiq Mubarak, Sendhil Mullainathan, Emmanuel Saez, Josh Schwartzstein, and other colleagues, as well as seminar audiences at the ASSA Annual Meeting, the Behavioral Economics Annual Meeting, Berkeley, the European Summer Symposium for Economic Theory, Harvard, the NBER Public Economics Meetings, Resources for the Future, Stanford, the Stanford Institute for Theoretical Economics, UCLA, the University of California Energy Institute, and the University of Chicago for constructive feedback. We thank our research assistants - Jeremiah Hair, Nina Yang, and Tiffany Yee - as well as management at the partner company, for their work on the in-store experiment. Thanks to Stefan Subias, Benjamin DiPaola, and Poom Nukulkij at GfK for their work on the TESS experiment. We are grateful to the National Science Foundation and the Sloan Foundation for financial support. Files containing code for replication, the in-store experiment protocol, and audio for the TESS experiment can be downloaded from Hunt Allcott's website. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer- reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications. © 2013 by Hunt Allcott and Dmitry Taubinsky. -
Information to Users
INFORMATION TO USERS This manuscript has been reproduced from the microfilm master. UMI films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer. The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely affect reproduction. In the unlikely event that the author did not send UMI a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. Oversize materials (e.g., maps, drawings, charts) are reproduced by sectioning the original, beginning at the upper left-hand comer and continuing from left to right in equal sections with small overlaps. Each original is also photographed in one exposure and is included in reduced form at the back of the book. Photographs included in the original manuscript have been reproduced xerographically in this copy. Higher quality 6” x 9” black and white photographic prints are available for any photographs or illustrations appearing in this copy for an additional charge. Contact UMI directly to order. UMI A Bell & Howell Information Company 300 North Zeeb Road, Ann Arbor MI 48106-1346 USA 313/761-4700 800/521-0600 BIASES IN IMPRESSION FORMATION. A DEMONSTRATION OF A BIVARIATE MODEL OF EVALUATION DISSERTATION Presented in Partial Fulfillment for the Requirements for the Degree Doctor of Philosophy in the Graduate School of the Ohio State University By Wendi L. -
Optimism Bias and Differential Information Use in Supply Chain Forecasting
Optimism bias and differential information use in supply chain forecasting Robert Fildes Dept. Management Science, Lancaster University, LA1 4YX, UK Email: [email protected] Paul Goodwin*, School of Management, University of Bath Bath, BA2 7AY,UK Tel: 44-1225-383594 Fax: 44-1225-386473 Email: [email protected] August 2011 * Corresponding author Acknowledgements This research was supported by a grant from SAS and the International Institute of Forecasters. 1 Abstract Companies tend to produce over optimistic forecasts of demand. Psychologists have suggested that optimism, at least in part, results from selective bias in processing information. When high demand is more desirable than low demand information favouring high demand may carry more weight than information that suggests the opposite. To test this, participants in an experiment used a prototypical forecasting support system to forecast the demand uplift resulting from sales promotion campaigns. One group was rewarded if demand exceeded an uplift of 80%. All of the participants were also supplied with information some of which supported an uplift of greater than 80% and some of which suggested that the uplift would fall below this value. This enabled an assessment to be made of the extent to which those who were rewarded if the uplift exceeded 80% paid more attention to the positive information than those who were not rewarded. While the experiment provided strong evidence of desirability bias there was no evidence that the group receiving rewards for sales exceeding the threshold made greater use of positive reasons. Possible explanations for this are discussed, together with the implications for forecasting support system design. -
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. -
The Threat of Appearing Prejudiced and Race-Based Attentional Biases Jennifer A
PSYCHOLOGICAL SCIENCE Research Report The Threat of Appearing Prejudiced and Race-Based Attentional Biases Jennifer A. Richeson and Sophie Trawalter Department of Psychology and C2S: The Center on Social Disparities and Health at the Institute for Policy Research, Northwestern University ABSTRACT—The current work tested whether external heightened anxiety in anticipation of, as well as during, those motivation to respond without prejudice toward Blacks is interracial interactions that they are unable to avoid (Plant, associated with biased patterns of selective attention that 2004; Plant & Devine, 1998). Research by Amodio has found, reflect a threat response to Black individuals. In a dot- furthermore, that such high-EM individuals automatically probe attentional bias paradigm, White participants with evaluate Black targets more negatively than White targets, as low and high external motivation to respond without revealed by potentiated startle eye-blink amplitudes (Amodio, prejudice toward Blacks (i.e., low-EM and high-EM in- Harmon-Jones, & Devine, 2003), unlike people who are inter- dividuals, respectively) were presented with pairs of White nally motivated to respond in nonprejudiced ways toward and Black male faces that bore either neutral or happy Blacks. Taken together, this work suggests that exposure to facial expressions; on each trial, the faces were displayed Blacks automatically triggers negative affective reactions, in- for either 30 ms or 450 ms. The findings were consistent cluding heightened anxiety, in high-EM individuals. with those of previous research on threat and attention: Although Amodio’s work has begun to explore some of the High-EM participants revealed an attentional bias toward component processes that underlie differences between high- and neutral Black faces presented for 30 ms, but an attentional low-EM individuals’ affective and stereotypical evaluations of bias away from neutral Black faces presented for 450 ms. -
Attentional Bias and Subjective Risk in Hypochondriacal Concern. Polly Beth Hitchcock Louisiana State University and Agricultural & Mechanical College
Louisiana State University LSU Digital Commons LSU Historical Dissertations and Theses Graduate School 1993 Attentional Bias and Subjective Risk in Hypochondriacal Concern. Polly Beth Hitchcock Louisiana State University and Agricultural & Mechanical College Follow this and additional works at: https://digitalcommons.lsu.edu/gradschool_disstheses Recommended Citation Hitchcock, Polly Beth, "Attentional Bias and Subjective Risk in Hypochondriacal Concern." (1993). LSU Historical Dissertations and Theses. 5641. https://digitalcommons.lsu.edu/gradschool_disstheses/5641 This Dissertation is brought to you for free and open access by the Graduate School at LSU Digital Commons. It has been accepted for inclusion in LSU Historical Dissertations and Theses by an authorized administrator of LSU Digital Commons. For more information, please contact [email protected]. INFORMATION TO USERS This manuscript has been reproduced from the microfilm master. UMI films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer. The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely affect reproduction. In the unlikely event that the author did not send UMI a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. Oversize materials (e.g., maps, drawings, charts) are reproduced by sectioning the original, beginning at the upper left-hand corner and continuing from left to right in equal sections with small overlaps. -
Emotional Reasoning and Parent-Based Reasoning in Normal Children
Morren, M., Muris, P., Kindt, M. Emotional reasoning and parent-based reasoning in normal children. Child Psychiatry and Human Development: 35, 2004, nr. 1, p. 3-20 Postprint Version 1.0 Journal website http://www.springerlink.com/content/105587/ Pubmed link http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dop t=Abstract&list_uids=15626322&query_hl=45&itool=pubmed_docsum DOI 10.1023/B:CHUD.0000039317.50547.e3 Address correspondence to M. Morren, Department of Medical, Clinical, and Experimental Psychology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands; e-mail: [email protected]. Emotional Reasoning and Parent-based Reasoning in Normal Children MATTIJN MORREN, MSC; PETER MURIS, PHD; MEREL KINDT, PHD DEPARTMENT OF MEDICAL, CLINICAL AND EXPERIMENTAL PSYCHOLOGY, MAASTRICHT UNIVERSITY, THE NETHERLANDS ABSTRACT: A previous study by Muris, Merckelbach, and Van Spauwen1 demonstrated that children display emotional reasoning irrespective of their anxiety levels. That is, when estimating whether a situation is dangerous, children not only rely on objective danger information but also on their own anxiety-response. The present study further examined emotional reasoning in children aged 7–13 years (N =508). In addition, it was investigated whether children also show parent-based reasoning, which can be defined as the tendency to rely on anxiety-responses that can be observed in parents. Children completed self-report questionnaires of anxiety, depression, and emotional and parent-based reasoning. Evidence was found for both emotional and parent-based reasoning effects. More specifically, children’s danger ratings were not only affected by objective danger information, but also by anxiety-response information in both objective danger and safety stories. -
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.