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Identified versus Statistical LivesAn Interdisciplinary Perspective$
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I. Glenn Cohen, Norman Daniels, and Nir Eyal

Print publication date: 2015

Print ISBN-13: 9780190217471

Published to Oxford Scholarship Online: April 2015

DOI: 10.1093/acprof:oso/9780190217471.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2022. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use.date: 27 January 2022

“Dual-Process” Models of the Mind and the “Indentifiable Victim Effect”

“Dual-Process” Models of the Mind and the “Indentifiable Victim Effect”

Chapter:
(p.24) 2 “Dual-Process” Models of the Mind and the “Indentifiable Victim Effect”
Source:
Identified versus Statistical Lives
Author(s):

Peter Railton

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780190217471.003.0003

Probabilities are pervasive in our lives, and thus our ability to learn and use probabilistic information effectively has a central place in humankind’s adaptive capacities. Curiously, research on the “statistical victim effect” and related phenomena suggests that we are not good at according probabilistic information appropriate weight in judgment and decision-making. Recently, explanations of these anomalous judgments and decisions have been given in terms of “dual-process” models of the mind, according to which the mind has two distinctive ways of processing information, one intuitive and affective (thus knowing little of probability and logic) and the other more reflective and cognitive (thus better at probability and logic, but less readily brought to bear). Using evidence from recent work in cognitive and affective neuroscience, this chapter raises some questions about dual-process models of this kind and suggests an alternative explanation of the “statistical” or “identifiable” victim effect, based upon implicit probabilistic learning.

Keywords:   statistical victim effect, identifiable victim effect, dual-process models, probabilistic learning, intuition, affect

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