Literature DB >> 20658855

Assessing the belief bias effect with ROCs: it's a response bias effect.

Chad Dube1, Caren M Rotello, Evan Heit.   

Abstract

A belief bias effect in syllogistic reasoning (Evans, Barston, & Pollard, 1983) is observed when subjects accept more valid than invalid arguments and more believable than unbelievable conclusions and show greater overall accuracy in judging arguments with unbelievable conclusions. The effect is measured with a contrast of contrasts, comparing the acceptance rates for valid and invalid arguments with believable and unbelievable conclusions. We show that use of this measure entails the assumption of a threshold model, which predicts linear receiver operating characteristics (ROCs). In 3 experiments, subjects made "valid"/"invalid" responses to syllogisms, followed by confidence ratings that allowed the construction of empirical ROCs; ROCs were also constructed from a base-rate manipulation in one experiment. In all cases, the form of the empirical ROCs was curved and therefore inconsistent with the assumptions of Klauer, Musch, and Naumer's (2000) multinomial model of belief bias. We propose a more appropriate, signal detection-based model of belief bias. We then use that model to develop theoretically sound and empirically justified measures of decision accuracy and response bias; those measures demonstrate that the belief bias effect is simply a response bias effect. Thus, our data and analyses challenge existing theories of belief bias because those theories predict an accuracy effect that our data suggest is a Type I error. Our results also provide support for processing theories of deduction that assume responses are driven by a graded argument-strength variable, such as the probability heuristic model proposed by Chater and Oaksford (1999). (c) 2010 APA, all rights reserved.

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Year:  2010        PMID: 20658855     DOI: 10.1037/a0019634

Source DB:  PubMed          Journal:  Psychol Rev        ISSN: 0033-295X            Impact factor:   8.934


  21 in total

1.  Negative valence can evoke a liberal response bias in syllogistic reasoning.

Authors:  Oshin Vartanian; Ann Nakashima; Fethi Bouak; Ingrid Smith; Joseph V Baranski; Bob Cheung
Journal:  Cogn Process       Date:  2012-09-26

Review 2.  When more data steer us wrong: replications with the wrong dependent measure perpetuate erroneous conclusions.

Authors:  Caren M Rotello; Evan Heit; Chad Dubé
Journal:  Psychon Bull Rev       Date:  2015-08

3.  Beliefs and Bayesian reasoning.

Authors:  Andrew L Cohen; Sara Sidlowski; Adrian Staub
Journal:  Psychon Bull Rev       Date:  2017-06

4.  Why Do People Believe What They Do? A Functionalist Perspective.

Authors:  Matthew Tyler Boden; Howard Berenbaum; James J Gross
Journal:  Rev Gen Psychol       Date:  2016-12-01

5.  Beyond ROC curvature: Strength effects and response time data support continuous-evidence models of recognition memory.

Authors:  Chad Dube; Jeffrey J Starns; Caren M Rotello; Roger Ratcliff
Journal:  J Mem Lang       Date:  2012-10       Impact factor: 3.059

6.  Individual Differences in Base Rate Neglect: A Fuzzy Processing Preference Index.

Authors:  Christopher R Wolfe; Christopher R Fisher
Journal:  Learn Individ Differ       Date:  2013-06-01

7.  Internal reinstatement hides cuing effects in source memory tasks.

Authors:  Jeffrey J Starns; Jason L Hicks
Journal:  Mem Cognit       Date:  2013-10

8.  Pre-screening workers to overcome bias amplification in online labour markets.

Authors:  Ans Vercammen; Alexandru Marcoci; Mark Burgman
Journal:  PLoS One       Date:  2021-03-23       Impact factor: 3.240

9.  Alleviating the concerns with the SDT approach to reasoning: reply to Singmann and Kellen (2014).

Authors:  Dries Trippas; Michael F Verde; Simon J Handley
Journal:  Front Psychol       Date:  2015-02-19

Review 10.  Imaging deductive reasoning and the new paradigm.

Authors:  Mike Oaksford
Journal:  Front Hum Neurosci       Date:  2015-02-27       Impact factor: 3.169

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