DOCSLIB.ORG

Revised 11 December 2019 Presentation 12/13/2019 K Harris 1

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Revised 11 December 2019 Presentation 12/13/2019 K Harris 1 Revised 11 December 2019 Presentation 12/13/2019 Cognitive Biases: Effects on mental disorders and treatment Keith Harris, PhD 13 December 2019 Lucia Heffernan Presentation take-aways . What are cognitive biases and heuristics? - Common cognitive biases - Why we evolved them and why they’re useful . Mismatch to the modern world - The world the mind was made for - Fine-tuning in childhood and early experience . Contributing factors in (several) mental disorders - Potential impact on treatment - Therapeutic strategies and work-arounds 2 Also interesting but not addressed today . Heuristics and intuition in everyday life . The socio-cultural facilitation of (some) cognitive biases . Cognitive distortions in personality disorders . The neurological underpinnings of cognitive biases . Cognitive biases in sales and politics . The irrationality of behavioral economics . Cognitive shortcuts in other species (hominins & dolphins) 3 K Harris 1 Revised 11 December 2019 Presentation 12/13/2019 What are cognitive biases? - Cognitive biases are a set of automatic (unconscious) tendencies to distort perceptions or situations in a predictable direction or manner - Cognitive biases evolved because they’re useful - But in some cases, these lead to problematic inferences and maladaptive responses to reality 4 5 Background - The concept of cognitive biases was introduced by the cognitive psychologists Amos Tversky and Danny Kahneman in 1972 - Initially eleven “cognitive illusions” - Familiar examples: (1) Tendency to generalize from small samples (2) Misunderstanding of laws of chance (3) Overconfidence based on limited information 6 K Harris 2 Revised 11 December 2019 Presentation 12/13/2019 Example of a typical cognitive bias 1:30 7 A (partial) current list of identified biases Ambiguity effect Ben Franklin effect Continued influence effect Anchoring or focalism Berkson's paradox Contrast effect Anthropocentric thinking Bias blind spot Courtesy bias Anthropomorphism Bizarreness effect Cross-race effect Attentional bias Choice-supportive bias Cryptomnesia Attribute substitution Clustering illusion Curse of knowledge Automation bias Compassion fade Declinism Availability cascade Confirmation bias Decoy effect Availability heuristic Congruence bias Default effect Backfire effect Conjunction fallacy Denomination effect Bandwagon effect Conservatism Disposition effect Base rate fallacy Consistency bias Distinction effect Belief bias Context effect Dread aversion 8 9 K Harris 3 Revised 11 December 2019 Presentation 12/13/2019 (What’s important?) (What to attend to?) (What’s too complex (What needs immediate or ambiguous?) action?) 10 https://upload.wikimedia.org/wikipedia/commons/6/65/Cognitive_bias_codex_en.svg 11 Expert Decision-Making The Recognition-primed Decision (RPD) Model 12 K Harris 4 Revised 11 December 2019 Presentation 12/13/2019 What our cognitive apparatus evolved to do Despite widespread claims to the contrary, the human mind is not worse than rational . but may often be better than rational … - Cosmides and Tooby, 1994 The issue “is not whether the cognitive feature is accurate or logical, but rather how well it solves a particular problem.” - Haselton, Nettle & Murray, 2016 13 The world the mind was made for Human life until perhaps 10,000 years ago: - Small communities (e.g., hunter-gatherer bands) - Sustained personal relationships with relations and nonrelations - Minimal privacy of behaviors or feelings - The complex social dynamics found in small, intensely-bound groups - Expectations that all members contribute (no freeloaders) - Capacity for shared intentionality, and collectively-shared intentionality: close collaboration - Generalized competence along with some division of labor - Lifelong ties to an adult partner with allowance for separation and repairing (in most groups, pair-bonding) - Sense of real and meaningful connection to the immediate environment 14 Need for cognitive efficiency Early human environments presented complex cognitive demands with little tolerance for error or delay 15 K Harris 5 Revised 11 December 2019 Presentation 12/13/2019 Why cognitive biases evolved - Signal from noise (pattern recognition) - Limited information Opportunity cost - Need for speed Assessment of Situation - Social complexity No Risk Risk - Risk aversion Actual Situation No Risk True negative False Positive - Cognitive costs - Survival of the most Risk False negative True Positive competent Danger, perhaps mortal 16 17 The algorithm as a modern metaphor 18 K Harris 6 Revised 11 December 2019 Presentation 12/13/2019 Genetic susceptibility and early environment 19 “Scripts” first form in childhood - Our adult interpersonal behaviors, feelings and thoughts are often influenced by automated cognitive “scripts” that may rely on innate cognitive and emotional tendencies - These are first formed through experience and observation (shaping) in childhood - These scripts can be very useful or very problematic in adulthood - A cognitive script is a sequence of emotions, thoughts and/or behaviors elicited by specific contexts 20 Clinical issues with biases and scripts Common scripts and cognitive biases can be involved in various psychiatric issues or mental disorders - Thought disorders (e.g., psychoses) - Mood disorders (i.e., depressive disorders) - Personality disorders - Phobias - Eating disorders - Etc. 21 K Harris 7 Revised 11 December 2019 Presentation 12/13/2019 Thought Disorders 22 Representative thought disorder indications - Illogical thinking - Loose associations or obscurity of speech - Incoherence - Tangentiality - Circumstantiality - Delusions - Poor reality contact - Unusual beliefs 23 Cognitive biases in thought disorders Deficits in “theory of mind” are possibly associated with - Difficulties with attribution (e.g., “Hostile attribution bias”) - Illusion of transparency - Paranoia Cognitive impairments may underlie - Jumping to conclusions - Intentionalizing - Catastrophizing - Emotional reasoning - Dichotomous thinking - Increased need for closure - Intolerance of ambiguity 24 K Harris 8 Revised 11 December 2019 Presentation 12/13/2019 Complications for treatment Challenges - Establishing therapeutic alliance - Cognitive processing impairments - Dearth of social-interaction possibilities - Over-reliance of patients on the treatment system General principles - Faulty cognitions in psychosis are important, comprehensible, and modifiable - Effective treatment is directed toward decreasing distress and increasing well-being 25 Treatment process and goals Pre-treatment assessment - Comprehensive psych-social history and current situation - Evaluation of cognitive functioning - Consideration of patient’s family and support system - Consultation with patient’s psychiatrist (and GP if needed) Treatment aims regarding faulty cognitive biases - Map maladaptive cognitive biases onto symptoms and problems - Work on biases must be integrated with other treatments - The most malleable biases are usually addressed first 26 Illustrative case: Arthur Presenting problems involving cognitive distortions - Auditory hallucinations - Delusions - Unusual beliefs Brief history - Onset in early adolescence - Social withdrawal - Family discord following onset of symptoms 27 K Harris 9 Revised 11 December 2019 Presentation 12/13/2019 28 Course of care: Arthur Relevant assessment issues - Need for multiple sources of information - Patient’s limited insight - Patient’s limited investment Treatment aims regarding cognitive distortions - Therapeutic alliance - Increased tolerance for ambiguity - Decreased conviction regarding false perceptions - Develop new cognitive habits 29 Paranoid perceptions in psychosis 2:43 30 K Harris 10 Revised 11 December 2019 Presentation 12/13/2019 Depressive Disorders 31 Examples of cognitive biases in depression 2:22 32 Cognitive biases in depressive disorders Attentional biases - Sensitivity to the “spotlight” effect - Gaze bias (a form of attentional bias) Interpretive biases - Negative interpretation of ambiguous stimuli - Negative self-serving bias Memory biases - Increased retention of negative information - Rumination and inverted “adverse- event fading bias” 33 K Harris 11 Revised 11 December 2019 Presentation 12/13/2019 Common symptoms in depressive disorders - Prolonged sadness - Irritable or anxious mood - Lack of interest in normal activities - Feelings of guilt, rumination, thought insertion - Sense of worthlessness - Feelings of alienation or hopelessness - Etc. 34 Neurological view of maladaptive cognitions Belzung, C., Willner, P., & Philippot, P. (2015). Depression: From psychopathology to pathophysiology. Current Opinion in Neurobiology, 30, 24–30. https://doi.org/10.1016/j.conb.2014.08.013 35 Treatment of depressive disorders Challenges - Readiness of patient to participate in therapy - Problematic attachment issues that interfere with therapeutic alliance - Many people aren’t very good at accurate introspection (insight) General approach - Cognitive-behavioral framework - Interpersonal or psychodynamic alliance 36 K Harris 12 Revised 11 December 2019 Presentation 12/13/2019 Treatment of depressive disorders (cont’d) Pre-treatment assessment - Comprehensive psych-social history - Evaluation of client’s emotional functioning - Identification of problematic cognitive biases and scripts Therapeutic aims regarding faulty cognitive biases - Person recognizes the problematic automatic tendencies when they are triggered - Patient exerts the effort needed to modify those maladaptive scripts that are amenable
Load more

Recommended publications
  • Addressing Cognitive Biases in Augmented Business Decision Systems Human Performance Metrics for Generic AI-Assisted Decision Making
    Addressing Cognitive Biases in Augmented Business Decision Systems Human performance metrics for generic AI-assisted decision making. THOMAS BAUDEL* IBM France Lab, Orsay, France MANON VERBOCKHAVEN IBM France Lab & ENSAE VICTOIRE COUSERGUE IBM France Lab, Université Paris-Dauphine & Mines ParisTech GUILLAUME ROY IBM France Lab & ENSAI RIDA LAARACH IBM France Lab, Telecom ParisTech & HEC How do algorithmic decision aids introduced in business decision processes affect task performance? In a first experiment, we study effective collaboration. Faced with a decision, subjects alone have a success rate of 72%; Aided by a recommender that has a 75% success rate, their success rate reaches 76%. The human-system collaboration had thus a greater success rate than each taken alone. However, we noted a complacency/authority bias that degraded the quality of decisions by 5% when the recommender was wrong. This suggests that any lingering algorithmic bias may be amplified by decision aids. In a second experiment, we evaluated the effectiveness of 5 presentation variants in reducing complacency bias. We found that optional presentation increases subjects’ resistance to wrong recommendations. We conclude by arguing that our metrics, in real usage scenarios, where decision aids are embedded as system-wide features in Business Process Management software, can lead to enhanced benefits. CCS CONCEPTS • Cross-computing tools and techniques: Empirical studies, Information Systems: Enterprise information systems, Decision support systems, Business Process Management, Human-centered computing, Human computer interaction (HCI), Visualization, Machine Learning, Automation Additional Keywords and Phrases: Business decision systems, Decision theory, Cognitive biases * [email protected]. 1 INTRODUCTION For the past 20 years, Business Process Management (BPM) [29] and related technologies such as Business Rules [10, 52] and Robotic Process Automation [36] have streamlined processes and operational decision- making in large enterprises, transforming work organization.
    [Show full text]
  • 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,
    [Show full text]
  • Moral Framing and Charitable Donation: Integrating Exploratory Social Media Analyses and Confirmatory Experimentation
    Hoover, J., et al. (2018). Moral Framing and Charitable Donation: Integrating Exploratory Social Media Analyses and Confirmatory Experimentation. Collabra: Psychology, 4(1): 9. DOI: https://doi.org/10.1525/collabra.129 ORIGINAL RESEARCH REPORT Moral Framing and Charitable Donation: Integrating Exploratory Social Media Analyses and Confirmatory Experimentation Joe Hoover*, Kate Johnson*, Reihane Boghrati†, Jesse Graham* and Morteza Dehghani‡ Do appeals to moral values promote charitable donation during natural disasters? Using Distributed Dictionary Representation, we analyze tweets posted during Hurricane Sandy to explore associations between moral values and charitable donation sentiment. We then derive hypotheses from the observed associations and test these hypotheses across a series of preregistered experiments that investigate the effects of moral framing on perceived donation motivation (Studies 2 & 3), hypothetical donation (Study 4), and real donation behavior (Study 5). Overall, we find consistent positive associations between moral care and loyalty framing with donation sentiment and donation motivation. However, in contrast with people’s perceptions, we also find that moral frames may not actually have reliable effects on charitable donation, as measured by hypothetical indications of donation and real donation behavior. Overall, this work demonstrates that theoretically constrained, exploratory social media analyses can be used to generate viable hypotheses, but also that such approaches should be paired with rigorous controlled experiments. Keywords: Moral Psychology; Charitable Donation; Natural Language Processing; Social Media The 900-mile-wide Hurricane Sandy hit the Atlantic Coast charitable donation in public discourse by modeling the of the United States on October 29th, 2012 with record- language used to frame charitable donation in the wake of breaking rainfall and 80-mile-per-hour winds.
    [Show full text]
  • Ezra M. Markowitz [email protected] |
    Ezra M. Markowitz [email protected] | www.ezramarkowitz.com Assistant Professor of Environmental Decision-Making Department of Environmental Conservation 303 Holdsworth Hall University of Massachusetts Amherst Amherst, MA 01003 Education 2012 Ph.D. Environmental Sciences, Studies & Policy. University of Oregon. Dissertation: Affective and moral roots of environmental stewardship: The role of obligation, gratitude and compassion Committee: Sara Hodges, Azim Shariff, Paul Slovic, Ron Mitchell, Kari Norgaard 2008 M.S. Psychology. University of Oregon. Thesis: Did you just see that? Making sense of environmentally relevant behavior Advisor: Bertram Malle 2007 B.A. Psychology. Vassar College. Departmental and general honors. Advisors: Randy Cornelius, Susan Trumbetta Appointments 2014- Assistant professor. University of Massachusetts Amherst. 2013-2014 Postdoctoral research fellow. Earth Institute. Columbia University. 2013-2014 Visiting postdoctoral research associate. PIIRS. Princeton University. 2013-2014 Fellow. FrameWorks Institute. Washington, DC. 2012-2013 Postdoctoral research associate. PIIRS. Princeton University. 2011-2012 Scholar-in-residence. School of Communication. American University. Professional and Teaching Experience 2012 Consultant. FrameWorks Institute. Washington, DC. 2012 Consultant. ecoAmerica. Washington, DC. 2010-2012 Gallup research scholar. Gallup. Princeton, NJ. 1 2010-2011 Director. Campus Conservation Corps. University of Oregon. 2010 Instructor. Psychology of Climate Change, Univ. of Oregon. 2009-2010 Graduate teaching fellow. Intro to Env. Studies; Statistics. Univ. of Oregon. 2008-2009 Graduate intern. Institute for a Sustainable Environment. Eugene, OR. 2008-2013 Senior staff. PolicyInteractive. Eugene, OR. Awards, Grants, Honors, Scholarships 2013 DISCCRS VIII Symposium (invited, attended). DISCCRS.org. 2012 Outstanding student paper award. Amer. Psych. Assoc. Divisions 9 & 34. 2011 Best paper award. 9th Biennial Conference on Environmental Psychology.
    [Show full text]
  • Bias and Fairness in NLP
    Bias and Fairness in NLP Margaret Mitchell Kai-Wei Chang Vicente Ordóñez Román Google Brain UCLA University of Virginia Vinodkumar Prabhakaran Google Brain Tutorial Outline ● Part 1: Cognitive Biases / Data Biases / Bias laundering ● Part 2: Bias in NLP and Mitigation Approaches ● Part 3: Building Fair and Robust Representations for Vision and Language ● Part 4: Conclusion and Discussion “Bias Laundering” Cognitive Biases, Data Biases, and ML Vinodkumar Prabhakaran Margaret Mitchell Google Brain Google Brain Andrew Emily Simone Parker Lucy Ben Elena Deb Timnit Gebru Zaldivar Denton Wu Barnes Vasserman Hutchinson Spitzer Raji Adrian Brian Dirk Josh Alex Blake Hee Jung Hartwig Blaise Benton Zhang Hovy Lovejoy Beutel Lemoine Ryu Adam Agüera y Arcas What’s in this tutorial ● Motivation for Fairness research in NLP ● How and why NLP models may be unfair ● Various types of NLP fairness issues and mitigation approaches ● What can/should we do? What’s NOT in this tutorial ● Definitive answers to fairness/ethical questions ● Prescriptive solutions to fix ML/NLP (un)fairness What do you see? What do you see? ● Bananas What do you see? ● Bananas ● Stickers What do you see? ● Bananas ● Stickers ● Dole Bananas What do you see? ● Bananas ● Stickers ● Dole Bananas ● Bananas at a store What do you see? ● Bananas ● Stickers ● Dole Bananas ● Bananas at a store ● Bananas on shelves What do you see? ● Bananas ● Stickers ● Dole Bananas ● Bananas at a store ● Bananas on shelves ● Bunches of bananas What do you see? ● Bananas ● Stickers ● Dole Bananas ● Bananas
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • Can Self-Persuasion Reduce Hostile Attribution Bias in Young Children?
    Journal of Abnormal Child Psychology https://doi.org/10.1007/s10802-018-0499-2 Can Self-Persuasion Reduce Hostile Attribution Bias in Young Children? Anouk van Dijk1 & Sander Thomaes1 & Astrid M. G. Poorthuis1 & Bram Orobio de Castro1 # The Author(s) 2018 Abstract Two experiments tested an intervention approach to reduce young children’s hostile attribution bias and aggression: self-persua- sion. Children with high levels of hostile attribution bias recorded a video-message advocating to peers why story characters who caused a negative outcome may have had nonhostile intentions (self-persuasion condition), or they simply described the stories (control condition). Before and after the manipulation, hostile attribution bias was assessed using vignettes of ambiguous provocations. Study 1 (n =83,age4–8) showed that self-persuasion reduced children’s hostile attribution bias. Study 2 (n = 121, age 6–9) replicated this finding, and further showed that self-persuasion was equally effective at reducing hostile attribution bias as was persuasion by others (i.e., listening to an experimenter advocating for nonhostile intentions). Effects on aggressive behavior, however, were small and only significant for one out of four effects tested. This research provides the first evidence that self-persuasion may be an effective approach to reduce hostile attribution bias in young children. Keywords Hostile attribution bias . Self-persuasion . Aggression . Intervention . Experiments Children’s daily social interactions abound with provocations by Dodge 1994). The present research tests an intervention approach peers, such as when they are physically hurt, laughed at, or ex- to reduce hostile attribution bias in young children. cluded from play. The exact reasons behind these provocations, Most interventions that effectively reduce children’s hostile and especially the issue of whether hostile intent was involved, attribution bias rely on attribution retraining techniques (e.g., are often unclear.
    [Show full text]
  • Empathy Through Data Loneliness Through the Lens of Data Visualization
    Empathy Through Data Loneliness through the lens of data visualization By Sananda Dutta A thesis presented to OCAD University in partial fulfilment of the requirements for the degree of Master of Design in Digital Futures. Toronto, Ontario, Canada, 2021. Empathy Through Data Sananda Dutta Creative Commons Copyright Notice Copyright Notice This document is licensed under the Creative Commons CC BY 4.0 2.5 Canada License. https://creativecommons.org/licenses/by/4.0/legalcode You are free to: Share - copy and redistribute the material in any medium or format Under the following conditions: Attribution - You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. No additional restrictions - You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits. 1 Empathy Through Data Sananda Dutta ABSTRACT This research examines strategies aimed at fostering empathy through data visualizations. Loneliness experienced during the COVID-19 pandemic (during May, July and September 2020) is used as a case study to explore alternate ways of representing data. Along with ways to humanize data representation and curb Statistical numbing, this research uses metaphors to encode sensitive data to help visually represent people suffering loneliness in Ontario during the first wave of the COVID-19 pandemic. The research amalgamates ‘affect theory’ with concepts of ‘arithmetic of emotions’ and ‘compassion fade’ to try and create a solution for issues related to the ways in which we respond to sensitive issues.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]