Advancing Transformation with Behavioral Science a Toolkit for Post-Secondary Institutions (And Beyond)
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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. -
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. -
Sad, Thus True. Negativity Bias in Judgments of Truth Benjamin E
Sad, thus true. Negativity bias in judgments of truth Benjamin E. Hilbig To cite this version: Benjamin E. Hilbig. Sad, thus true. Negativity bias in judgments of truth. Journal of Experimental Social Psychology, Elsevier, 2009, 45 (4), pp.983. 10.1016/j.jesp.2009.04.012. hal-00687909 HAL Id: hal-00687909 https://hal.archives-ouvertes.fr/hal-00687909 Submitted on 16 Apr 2012 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Accepted Manuscript Sad, thus true. Negativity bias in judgments of truth Benjamin E. Hilbig PII: S0022-1031(09)00093-6 DOI: 10.1016/j.jesp.2009.04.012 Reference: YJESP 2250 To appear in: Journal of Experimental Social Psychology Received Date: 24 October 2008 Revised Date: 30 March 2009 Accepted Date: 4 April 2009 Please cite this article as: Hilbig, B.E., Sad, thus true. Negativity bias in judgments of truth, Journal of Experimental Social Psychology (2009), doi: 10.1016/j.jesp.2009.04.012 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. -
Negativity Bias, Negativity Dominance, and Contagion
Personality and Social Psychology Review Copyright © 2001 by 2001, Vol. 5, No. 4, 296–320 Lawrence Erlbaum Associates, Inc. Negativity Bias, Negativity Dominance, and Contagion Paul Rozin and Edward B. Royzman Department of Psychology and Solomon Asch Center for Study of Ethnopolitical Conflict University of Pennsylvania We hypothesize that there is a general bias, based on both innate predispositions and experience, in animals and humans, to give greater weight to negative entities (e.g., events, objects, personal traits). This is manifested in 4 ways: (a) negative potency (negative entities are stronger than the equivalent positive entities), (b) steeper nega- tive gradients (the negativity of negative events grows more rapidly with approach to them in space or time than does the positivity of positive events, (c) negativity domi- nance (combinations of negative and positive entities yield evaluations that are more negative than the algebraic sum of individual subjective valences would predict), and (d) negative differentiation (negative entities are more varied, yield more complex conceptual representations, and engage a wider response repertoire). We review evi- dence for this taxonomy, with emphasis on negativity dominance, including literary, historical, religious, and cultural sources, as well as the psychological literatures on learning, attention, impression formation, contagion, moral judgment, development, and memory. We then consider a variety of theoretical accounts for negativity bias. We suggest that 1 feature of negative events that make them dominant is that negative enti- ties are more contagious than positive entities. Brief contact with a cockroach will usually render taminated—that is, lowered in social status—by a delicious meal inedible. -
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. -
John Collins, President, Forensic Foundations Group
On Bias in Forensic Science National Commission on Forensic Science – May 12, 2014 56-year-old Vatsala Thakkar was a doctor in India but took a job as a convenience store cashier to help pay family expenses. She was stabbed to death outside her store trying to thwart a theft in November 2008. Bloody Footwear Impression Bloody Tire Impression What was the threat? 1. We failed to ask ourselves if this was a footwear impression. 2. The appearance of the impression combined with the investigator’s interpretation created prejudice. The accuracy of our analysis became threatened by our prejudice. Types of Cognitive Bias Available at: http://en.wikipedia.org/wiki/List_of_cognitive_biases | Accessed on April 14, 2014 Anchoring or focalism Hindsight bias Pseudocertainty effect Illusory superiority Levels-of-processing effect Attentional bias Hostile media effect Reactance Ingroup bias List-length effect Availability heuristic Hot-hand fallacy Reactive devaluation Just-world phenomenon Misinformation effect Availability cascade Hyperbolic discounting Recency illusion Moral luck Modality effect Backfire effect Identifiable victim effect Restraint bias Naive cynicism Mood-congruent memory bias Bandwagon effect Illusion of control Rhyme as reason effect Naïve realism Next-in-line effect Base rate fallacy or base rate neglect Illusion of validity Risk compensation / Peltzman effect Outgroup homogeneity bias Part-list cueing effect Belief bias Illusory correlation Selective perception Projection bias Peak-end rule Bias blind spot Impact bias Semmelweis -
The Negativity Bias, Revisited: Evidence from Neuroscience Measures and an Individual Differences Approach
Swarthmore College Works Psychology Faculty Works Psychology 2021 The Negativity Bias, Revisited: Evidence From Neuroscience Measures And An Individual Differences Approach Catherine Norris Swarthmore College, [email protected] Follow this and additional works at: https://works.swarthmore.edu/fac-psychology Part of the Psychology Commons Let us know how access to these works benefits ouy Recommended Citation Catherine Norris. (2021). "The Negativity Bias, Revisited: Evidence From Neuroscience Measures And An Individual Differences Approach". Social Neuroscience. Volume 16, DOI: 10.1080/ 17470919.2019.1696225 https://works.swarthmore.edu/fac-psychology/1125 This work is brought to you for free by Swarthmore College Libraries' Works. It has been accepted for inclusion in Psychology Faculty Works by an authorized administrator of Works. For more information, please contact [email protected]. Social Neuroscience ISSN: 1747-0919 (Print) 1747-0927 (Online) Journal homepage: https://www.tandfonline.com/loi/psns20 The Negativity Bias, Revisited: Evidence from Neuroscience Measures and an Individual Differences Approach Catherine J. Norris To cite this article: Catherine J. Norris (2019): The Negativity Bias, Revisited: Evidence from Neuroscience Measures and an Individual Differences Approach, Social Neuroscience, DOI: 10.1080/17470919.2019.1696225 To link to this article: https://doi.org/10.1080/17470919.2019.1696225 Accepted author version posted online: 21 Nov 2019. Submit your article to this journal Article views: 23 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=psns20 Norris 1 Publisher: Taylor & Francis & Informa UK Limited, trading as Taylor & Francis Group Journal: Social Neuroscience DOI: 10.1080/17470919.2019.1696225 Running Header: NEGATIVITY BIAS, REVISITED The Negativity Bias, Revisited: Evidence from Neuroscience Measures and an Individual Differences Approach Catherine J. -
The Effects of Expertise on the Hindsight Bias
The Effects of Expertise on the Hindsight Bias A Dissertation Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Melissa A.Z. Marks Knoll, B.A., M.A. Graduate Program in Psychology * * * * * The Ohio State University 2009 Dissertation Committee: Dr. Hal R. Arkes, Advisor Dr. Thomas E. Nygren Dr. Michael C. Edwards ABSTRACT I present data from three experiments in which I explored the effects of expertise on the hindsight bias. In Experiment 1 participants read an essay about baseball or about owning a dog and then answered a 20-question true/false quiz about the baseball essay to the best of their ability (do-your-best group), as if they had not read the essay (discount group), or to the best of their ability even though they read about owning a dog (dogs group). Participants also completed a quiz about baseball rules (measure of expertise). Results demonstrated that as participants’ baseball expertise increased, their inability to act as if they had never read the essay also increased; expertise exacerbated hindsight bias. In Experiment 2, varsity baseball players and baseball non-experts answered a 20- question quiz about baseball current events. Foresight participants answered the questions, while hindsight participants were given the questions and the answers and had to give the probability that they would have known the answers had the answers not been provided. The baseball players displayed no hindsight bias, while non-experts demonstrated the bias. To test of the effects of subjective expertise on hindsight bias, participants in Experiment 3 ranked five topics in order of expertise and gave feeling-of- knowing (FOK) ratings for 100 questions from these topics. -
From Negative Biases to Positive News: Resetting and Reframing News Consumption for a Better Life and a Better World
University of Pennsylvania ScholarlyCommons Master of Applied Positive Psychology (MAPP) Master of Applied Positive Psychology (MAPP) Capstone Projects Capstones 8-15-2017 From Negative Biases to Positive News: Resetting and Reframing News Consumption for a Better Life and a Better World Henry Edwards University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/mapp_capstone Part of the Film and Media Studies Commons, Political Science Commons, and the Psychology Commons Edwards, Henry, "From Negative Biases to Positive News: Resetting and Reframing News Consumption for a Better Life and a Better World" (2017). Master of Applied Positive Psychology (MAPP) Capstone Projects. 123. https://repository.upenn.edu/mapp_capstone/123 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/mapp_capstone/123 For more information, please contact [email protected]. From Negative Biases to Positive News: Resetting and Reframing News Consumption for a Better Life and a Better World Abstract Research psychologists have found that people are subject to negative biases. These powerful biases may influence how journalists and editors produce the news and how citizens consume it. While not all news is negative, much of it is. Negatively biased news is both inaccurate and detrimental for well-being. This paper reviews literature about negative biases and the detrimental effects of negative news on well- being. Positive psychology interventions, taught in a workshop, may be able to mitigate or neutralize negative news effects. This paper presents a half-day workshop meant to combat negative biases in the news by training attendees in relevant positive psychology strategies. While the workshop is informed by research, testing will determine the extent to which it mitigates and combats the negative effects of bad news.