Political Bullshit Receptivity and Its Correlates: a Cross-Cultural Validation of The
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Illusory Correlation and Valenced Outcomes by Cory Derringer BA
Title Page Illusory Correlation and Valenced Outcomes by Cory Derringer BA, University of Northern Iowa, 2012 MS, Missouri State University, 2014 Submitted to the Graduate Faculty of The Dietrich School of Arts and Sciences in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Pittsburgh 2019 Committee Membership Page UNIVERSITY OF PITTSBURGH DIETRICH SCHOOL OF ARTS AND SCIENCES This dissertation was presented by Cory Derringer It was defended on April 12, 2019 and approved by Timothy Nokes-Malach, Associate Professor, Department of Psychology Julie Fiez, Professor, Department of Psychology David Danks, Professor, Department of Psychology, Carnegie Mellon University Thesis Advisor/Dissertation Director: Benjamin Rottman, Associate Professor, Department of Psychology ii Copyright © by Cory Derringer 2019 iii Abstract Illusory Correlation and Valenced Outcomes Cory Derringer, PhD University of Pittsburgh, 2019 Accurately detecting relationships between variables in the environment is an integral part of our cognition. The tendency for people to infer these relationships where there are none has been documented in several different fields of research, including social psychology, fear learning, and placebo effects. A consistent finding in these areas is that people infer these illusory correlations more readily when they involve negative (aversive) outcomes; however, previous research has not tested this idea directly. Four experiments yielded several empirical findings: Valence effects were reliable and robust in a causal learning task with and without monetary outcomes, they were driven by relative rather than absolute gains and losses, and they were not moderated by the magnitude of monetary gains/losses. Several models of contingency learning are discussed and modified in an attempt to explain the findings, although none of the modifications could reasonably explain valence effects. -
•Understanding Bias: a Resource Guide
Community Relations Services Toolkit for Policing Understanding Bias: A Resource Guide Bias Policing Overview and Resource Guide The Community Relations Service (CRS) is the U.S. Justice Department’s “peacemaker” agency, whose mission is to help resolve tensions in communities across the nation, arising from differences of race, color, national origin, gender, gender identity, sexual orientation, religion, and disability. CRS may be called to help a city or town resolve tensions that stem from community perceptions of bias or a lack of cultural competency among police officers. Bias and a lack of cultural competency are often cited interchangeably as challenges in police- community relationships. While bias and a lack of cultural competency may both be present in a given situation, these challenges and the strategies for addressing them differ appreciably. This resource guide will assist readers in understanding and addressing both of these issues. What is bias, and how is it different from cultural competency? The Science of Bias Bias is a human trait resulting from our tendency and need to classify individuals into categories as we strive to quickly process information and make sense of the world.1 To a large extent, these processes occur below the level of consciousness. This “unconscious” classification of people occurs through schemas, or “mental maps,” developed from life experiences to aid in “automatic processing.”2 Automatic processing occurs with tasks that are very well practiced; very few mental resources and little conscious thought are involved during automatic processing, allowing numerous tasks to be carried out simultaneously.3 These schemas become templates that we use when we are faced with 1. -
Illusory Correlation in Children
The Pennsylvania State University The Graduate School Department of Psychology ILLUSORY CORRELATION IN CHILDREN: COGNITIVE AND MOTIVATIONAL BIASES IN CHILDREN’S GROUP IMPRESSION FORMATION A Thesis in Psychology by Kristen Elizabeth Johnston © 2000 Kristen Elizabeth Johnston Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy May, 2000 We approve the thesis of Kristen E. Johnston. Date of Signature ___________________________________________ ______________ Kelly L. Madole Assistant Professor of Psychology Thesis Advisor ___________________________________________ ______________ Janis E. Jacobs Vice President of Administration and Associate Professor Of Psychology and Human Development Thesis Advisor ___________________________________________ ______________ Janet K. Swim Associate Professor of Psychology ___________________________________________ ______________ Jeffrey Parker Assistant Professor of Psychology ___________________________________________ ______________ Susan McHale Associate Professor of Psychology ___________________________________________ ______________ Keith Crnic Department Head and Professor of Psychology iii ABSTRACT Despite the ubiquity and sometimes devastating consequences of stereotyping, we know little about the origins and development of these processes. The current research examined one way in which false stereotypes about minority groups may be developed, which is called illusory correlation. Research with adults has shown that when people are told about behaviors associated -
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. -
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. -
Running Head: GENERAL MISCONCEPTIONS and THINKING STYLES 1
Running head: GENERAL MISCONCEPTIONS AND THINKING STYLES 1 A new measure of general misconceptions: Relations with rational, intuitive, critical thinking, and monetary incentives Marle Wortelboer Master thesis Economic Psychology Tilburg School of Social and Behavioral Sciences 1st Supervisor: W.W.A. Sleegers 2nd Supervisor: F. van Leeuwen March 22th 2019 GENERAL MISCONCEPTIONS AND THINKING STYLES 2 Abstract Past research indicated several issues regarding measurements of (psychological) misconceptions. In this paper, it was aimed to develop a new Test of General Knowledge and Misconceptions (TOGKAM) based on recent suggestions to address previous limitations of Bensley et al. (2014). In order to validate this TOGKAM, relations with rational- and intuitive thinking styles and critical thinking dispositions were tested. It was hypothesized that an Intuitive-Experiential thinking style was positively correlated with TOGKAM scores, whereas a Rational-Analytic thinking style and Critical Thinking dispositions were negatively correlated with TOGKAM scores. In addition, it was examined whether monetary incentives decreased the endorsement of misconceptions. Hypothesized was, that incentivized participants were more likely to use rational and analytic thinking styles and subsequently endorsed fewer misconceptions. Results of this study supported all the hypotheses. Keywords: misconceptions, intuitive- and rational thinking styles, critical thinking, dual- processing. GENERAL MISCONCEPTIONS AND THINKING STYLES 3 A new measure of general misconceptions: Relations with rational, intuitive, critical thinking, and monetary incentives Misconceptions are widely held beliefs that contradict established scientific evidence (Gardner & Brown, 2013; Sak, 2011). Misconceptions, or sometimes called myths, are stable and often deeply rooted beliefs about the world. Common misconceptions such as ‘bats are blind and navigate through echolocation’ or ‘the Chinese wall is the only man-made structure visible from space with the naked eye, are often part of ‘folk wisdom’ (McCutcheon, 1991). -
Evaluation of the Sensitivity of Cognitive Biases in the Design of Artificial Intelligence M Cazes, N Franiatte, a Delmas, J-M André, M Rodier, I Chraibi Kaadoud
Evaluation of the sensitivity of cognitive biases in the design of artificial intelligence M Cazes, N Franiatte, A Delmas, J-M André, M Rodier, I Chraibi Kaadoud To cite this version: M Cazes, N Franiatte, A Delmas, J-M André, M Rodier, et al.. Evaluation of the sensitivity of cognitive biases in the design of artificial intelligence. Rencontres des Jeunes Chercheurs en Intelligence Artificielle (RJCIA’21) Plate-Forme Intelligence Artificielle (PFIA’21), Jul 2021, Bordeaux, France. pp.30-37. hal-03298746 HAL Id: hal-03298746 https://hal.archives-ouvertes.fr/hal-03298746 Submitted on 23 Jul 2021 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. Evaluation of the sensitivity of cognitive biases in the design of artificial intelligence. M. Cazes1 *, N. Franiatte1*, A. Delmas2, J-M. André1, M. Rodier3, I. Chraibi Kaadoud4 1 ENSC-Bordeaux INP, IMS, UMR CNRS 5218, Bordeaux, France 2 Onepoint - R&D Department, Bordeaux, France 3 IBM - University chair "Sciences et Technologies Cognitiques" in ENSC, Bordeaux, France 4 IMT Atlantique, Lab-STICC, UMR CNRS 6285, F-29238 Brest, France Abstract AI system any system that involves at least one of the follo- The reduction of algorithmic biases is a major issue in wing elements: an AI symbolic system (e.g. -
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 -
Selective Hypothesis Testing
Psychonomic Bulletin & Review 1998,5(2), 197-220 Selective hypothesis testing DAVID M. SANBONMATSU and STEVEN S. POSAVAC University ofUtah, Salt Lake City, Utah FRANK R. KARDES University ofCincinnati, Cincinnati, Ohio and SUSAN p. MANTEL University ofToledo, Toledo, Ohio A diverse set ofbiases that have been found to characterize judgment may be similarly mediated by a process ofselective hypothesis testing. Ourpaper begins with a definition ofselective hypothesis test ing and an explanation ofhow andwhen this process leads to error. We then review a diverse and often disconnected set offmdings in the person perception, judgment, cognition, attitudes, attribution, and rule discovery literatures thatcanbe explained by this process, Finally, we examine the question ofwhy the selective testing of hypotheses occurs. Although the psychological literature suggests that selec tive hypothesis testing contributes to a variety of errors, in many contexts it may be a useful and effi cient strategy that leads to satisfactory judgment. The proliferation ofresearch on human judgment dur Although a vast body of research is reviewed that indi ing the last three decades has led to the identification of cates that a multitude oferrors may result from selective a multiplicity of errors that may characterize the obser hypothesis testing, in general this judgmental strategy is vations, interpretations, and evaluations ofeveryday life not necessarily a bad one. As we shall suggest, in many (see, e,g" Kahneman & Tversky, 1982; Nisbett & Ross, contexts -
How a Machine Learns and Fails 2019
Repositorium für die Medienwissenschaft Matteo Pasquinelli How a Machine Learns and Fails 2019 https://doi.org/10.25969/mediarep/13490 Veröffentlichungsversion / published version Zeitschriftenartikel / journal article Empfohlene Zitierung / Suggested Citation: Pasquinelli, Matteo: How a Machine Learns and Fails. In: spheres: Journal for Digital Cultures. Spectres of AI (2019), Nr. 5, S. 1–17. DOI: https://doi.org/10.25969/mediarep/13490. Erstmalig hier erschienen / Initial publication here: https://spheres-journal.org/wp-content/uploads/spheres-5_Pasquinelli.pdf Nutzungsbedingungen: Terms of use: Dieser Text wird unter einer Creative Commons - This document is made available under a creative commons - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0/ Attribution - Non Commercial - No Derivatives 4.0/ License. For Lizenz zur Verfügung gestellt. Nähere Auskünfte zu dieser Lizenz more information see: finden Sie hier: http://creativecommons.org/licenses/by-nc-nd/4.0/ http://creativecommons.org/licenses/by-nc-nd/4.0/ © the author(s) 2019 www.spheres-journal.org ISSN 2363-8621 #5 Spectres of AI sadfasdf MATTEO PASQUINELLI HOW A MACHINE LEARNS AND FAILS – A GRAMMAR OF ERROR FOR ARTIFICIAL INTELLIGENCE “Once the characteristic numbers are established for most concepts, mankind will then possess a new instrument which will enhance the capabilities of the mind to a far greater extent than optical instruments strengthen the eyes, and will supersede the microscope and telescope to the same extent that reason is superior to eyesight.”1 — Gottfried Wilhelm Leibniz. “The Enlightenment was […] not about consensus, it was not about systematic unity, and it was not about the deployment of instrumental reason: what was developed in the Enlightenment was a modern idea of truth defined by error, a modern idea of knowledge defined by failure, conflict, and risk, but also hope.”2 — David Bates. -
1 Embrace Your Cognitive Bias
1 Embrace Your Cognitive Bias http://blog.beaufortes.com/2007/06/embrace-your-co.html Cognitive Biases are distortions in the way humans see things in comparison to the purely logical way that mathematics, economics, and yes even project management would have us look at things. The problem is not that we have them… most of them are wired deep into our brains following millions of years of evolution. The problem is that we don’t know about them, and consequently don’t take them into account when we have to make important decisions. (This area is so important that Daniel Kahneman won a Nobel Prize in 2002 for work tying non-rational decision making, and cognitive bias, to mainstream economics) People don’t behave rationally, they have emotions, they can be inspired, they have cognitive bias! Tying that into how we run projects (project leadership as a compliment to project management) can produce results you wouldn’t believe. You have to know about them to guard against them, or use them (but that’s another article)... So let’s get more specific. After the jump, let me show you a great list of cognitive biases. I’ll bet that there are at least a few that you haven’t heard of before! Decision making and behavioral biases Bandwagon effect — the tendency to do (or believe) things because many other people do (or believe) the same. Related to groupthink, herd behaviour, and manias. Bias blind spot — the tendency not to compensate for one’s own cognitive biases. Choice-supportive bias — the tendency to remember one’s choices as better than they actually were. -
Sample Diversity and Socioecological Theory Are Essential to the Future Of
PAPER Broadening horizons: Sample diversity and COLLOQUIUM socioecological theory are essential to the future of psychological science Michael D. Gurvena,1 aDepartment of Anthropology, University of California, Santa Barbara, CA 93106 Edited by Daniel Hruschka, Arizona State University, and accepted by Editorial Board Member Susan A. Gelman April 10, 2018 (received for review December 15, 2017) The present lack of sample diversity and ecological theory in flourished in areas of anthropology and economics, but have had psychological science fundamentally limits generalizability and relatively limited impact in psychology. Psychology has been obstructs scientific progress. A focus on the role of socioecology in considered more a proximate level explanation detailing mech- shaping the evolution of morphology, physiology, and behavior anism, complementing the ultimate-level functional approach of has not yet been widely applied toward psychology. To date, behavioral ecology. Evolutionary psychology has been remark- evolutionary approaches to psychology have focused more on ably influential in showcasing hypothesized design features of finding universals than explaining variability. However, contrasts psychological mechanisms, but emphasis in practice has been between small-scale, kin-based rural subsistence societies and more on uncovering human universals than explaining variability. large-scale urban, market-based populations, have not been well The overwhelming emphasis on Western, educated, industrial- appreciated. Nor has the variability within high-income countries, ized, rich, democratic (WEIRD) samples in psychological studies or the socioeconomic and cultural transformations affecting even is now well recognized (2), but recognition has had little impact the most remote tribal populations today. Elucidating the causes on broadening the sample pools or research directions in much and effects of such broad changes on psychology and behavior is a of psychology (3).