Thinking About Thinking: Cognition, Science, and Psychotherapy
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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. -
Pat Croskerry MD Phd
Thinking (and the factors that influence it) Pat Croskerry MD PhD Scottish Intensive Care Society St Andrews, January 2011 RECOGNIZED Intuition Pattern Pattern Initial Recognition Executive Dysrationalia Calibration Output information Processor override T override Repetition NOT Analytical RECOGNIZED reasoning Dual Process Model Medical Decision Making Intuitive Analytical Orthodox Medical Decision Making (Analytical) Rational Medical Decision-Making • Knowledge base • Differential diagnosis • Best evidence • Reviews, meta-analysis • Biostatistics • Publication bias, citation bias • Test selection and interpretation • Bayesian reasoning • Hypothetico-deductive reasoning .„Cognitive thought is the tip of an enormous iceberg. It is the rule of thumb among cognitive scientists that unconscious thought is 95% of all thought – .this 95% below the surface of conscious awareness shapes and structures all conscious thought‟ Lakoff and Johnson, 1999 Rational blind-spots • Framing • Context • Ambient conditions • Individual factors Individual Factors • Knowledge • Intellect • Personality • Critical thinking ability • Decision making style • Gender • Ageing • Circadian type • Affective state • Fatigue, sleep deprivation, sleep debt • Cognitive load tolerance • Susceptibility to group pressures • Deference to authority Intelligence • Measurement of intelligence? • IQ most widely used barometer of intellect and cognitive functioning • IQ is strongest single predictor of job performance and success • IQ tests highly correlated with each other • Population -
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. -
“Dysrationalia” Among University Students: the Role of Cognitive
“Dysrationalia” among university students: The role of cognitive abilities, different aspects of rational thought and self-control in explaining epistemically suspect beliefs Erceg, Nikola; Galić, Zvonimir; Bubić, Andreja Source / Izvornik: Europe’s Journal of Psychology, 2019, 15, 159 - 175 Journal article, Published version Rad u časopisu, Objavljena verzija rada (izdavačev PDF) https://doi.org/10.5964/ejop.v15i1.1696 Permanent link / Trajna poveznica: https://urn.nsk.hr/urn:nbn:hr:131:942674 Rights / Prava: Attribution 4.0 International Download date / Datum preuzimanja: 2021-09-29 Repository / Repozitorij: ODRAZ - open repository of the University of Zagreb Faculty of Humanities and Social Sciences Europe's Journal of Psychology ejop.psychopen.eu | 1841-0413 Research Reports “Dysrationalia” Among University Students: The Role of Cognitive Abilities, Different Aspects of Rational Thought and Self-Control in Explaining Epistemically Suspect Beliefs Nikola Erceg* a, Zvonimir Galić a, Andreja Bubić b [a] Department of Psychology, Faculty of Humanities and Social Sciences, University of Zagreb, Zagreb, Croatia. [b] Department of Psychology, Faculty of Humanities and Social Sciences, University of Split, Split, Croatia. Abstract The aim of the study was to investigate the role that cognitive abilities, rational thinking abilities, cognitive styles and self-control play in explaining the endorsement of epistemically suspect beliefs among university students. A total of 159 students participated in the study. We found that different aspects of rational thought (i.e. rational thinking abilities and cognitive styles) and self-control, but not intelligence, significantly predicted the endorsement of epistemically suspect beliefs. Based on these findings, it may be suggested that intelligence and rational thinking, although related, represent two fundamentally different constructs. -
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. -
Using the Proposed Effect of Breakfast on Obesity to Show 2 Practices That Distort Scientific Evidence1–4
Belief beyond the evidence: using the proposed effect of breakfast on obesity to show 2 practices that distort scientific evidence1–4 Andrew W Brown, Michelle M Bohan Brown, and David B Allison Downloaded from https://academic.oup.com/ajcn/article-abstract/98/5/1298/4577332 by guest on 06 September 2019 ABSTRACT there are not insurmountable barriers to moving beyond obser- Background: Various intentional and unintentional factors influ- vational evidence, and 2) it seems like a less politically charged ence beliefs beyond what scientific evidence justifies. Two such topic than the topics of some previous publications on biased factors are research lacking probative value (RLPV) and biased reporting [eg, sugar-sweetened beverages (2, 3) and breastfeeding research reporting (BRR). (4)] to better facilitate the discussion of extrascientific factors that Objective: We investigated the prevalence of RLPV and BRR in affect the fidelity of research reporting. research about the proposition that skipping breakfast causes weight In this article, we first establish that the PEBO is only a pre- gain, which is called the proposed effect of breakfast on obesity sumption so that we can examine 2 factors that may influence the (PEBO) in this article. propensity to believe in a presumption beyond the available evi- Design: Studies related to the PEBO were synthesized by using dence. The first factor we identified is research lacking probative a cumulative meta-analysis. Abstracts from these studies were also value (RLPV), which we defined as experiments or analyses that rated for the improper use of causal language and biased interpre- are 1) about questions that have already been sufficiently an- tations. -
Working Memory, Cognitive Miserliness and Logic As Predictors of Performance on the Cognitive Reflection Test
Working Memory, Cognitive Miserliness and Logic as Predictors of Performance on the Cognitive Reflection Test Edward J. N. Stupple ([email protected]) Centre for Psychological Research, University of Derby Kedleston Road, Derby. DE22 1GB Maggie Gale ([email protected]) Centre for Psychological Research, University of Derby Kedleston Road, Derby. DE22 1GB Christopher R. Richmond ([email protected]) Centre for Psychological Research, University of Derby Kedleston Road, Derby. DE22 1GB Abstract Most participants respond that the answer is 10 cents; however, a slower and more analytic approach to the The Cognitive Reflection Test (CRT) was devised to measure problem reveals the correct answer to be 5 cents. the inhibition of heuristic responses to favour analytic ones. The CRT has been a spectacular success, attracting more Toplak, West and Stanovich (2011) demonstrated that the than 100 citations in 2012 alone (Scopus). This may be in CRT was a powerful predictor of heuristics and biases task part due to the ease of administration; with only three items performance - proposing it as a metric of the cognitive miserliness central to dual process theories of thinking. This and no requirement for expensive equipment, the practical thesis was examined using reasoning response-times, advantages are considerable. There have, moreover, been normative responses from two reasoning tasks and working numerous correlates of the CRT demonstrated, from a wide memory capacity (WMC) to predict individual differences in range of tasks in the heuristics and biases literature (Toplak performance on the CRT. These data offered limited support et al., 2011) to risk aversion and SAT scores (Frederick, for the view of miserliness as the primary factor in the CRT. -
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. -
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.