Literature DB >> 21244191

The belief bias effect is aptly named: a reply to Klauer and Kellen (2011).

Chad Dube1, Caren M Rotello, Evan Heit.   

Abstract

In "Assessing the Belief Bias Effect With ROCs: It's a Response Bias Effect," Dube, Rotello, and Heit (2010) examined the form of receiver operating characteristic (ROC) curves for reasoning and the effects of belief bias on measurement indices that differ in whether they imply a curved or linear ROC function. We concluded that the ROC data are in fact curved and that analyses using statistics that assume a linear ROC are likely to produce Type I errors. Importantly, we showed that the interaction between logic and belief that has inspired much of the theoretical work on belief bias is in fact an error stemming from inappropriate reliance on a contrast (hit rate-false alarm rate) that implies linear ROCs. Dube et al. advanced a new model of belief bias, which, in light of their data, is currently the only plausible account of the effect. Klauer and Kellen (2011) disputed these conclusions, largely on the basis of speculation about the data collection method used by Dube et al. to construct the ROCs. New data and model-based analyses are presented that refute the speculations made by Klauer and Kellen. We also show that new modeling results presented by Klauer and Kellen actually support the conclusions advanced by Dube et al. Together, these data show that the methods used by Dube et al. are valid and that the belief bias effect is simply a response bias effect.

Entities:  

Mesh:

Year:  2011        PMID: 21244191     DOI: 10.1037/a0021774

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


  8 in total

1.  Recognition memory models and binary-response ROCs: a comparison by minimum description length.

Authors:  David Kellen; Karl Christoph Klauer; Arndt Bröder
Journal:  Psychon Bull Rev       Date:  2013-08

2.  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

3.  Modeling causal conditional reasoning data using SDT: caveats and new insights.

Authors:  Dries Trippas; Michael F Verde; Simon J Handley; Matthew E Roser; Nicolas A McNair; Jonathan St B T Evans
Journal:  Front Psychol       Date:  2014-03-12

4.  Memory, reasoning, and categorization: parallels and common mechanisms.

Authors:  Brett K Hayes; Evan Heit; Caren M Rotello
Journal:  Front Psychol       Date:  2014-06-17

Review 5.  The neural correlates of belief bias: activation in inferior frontal cortex reflects response rate differences.

Authors:  Caren M Rotello; Evan Heit
Journal:  Front Hum Neurosci       Date:  2014-10-21       Impact factor: 3.169

6.  When fast logic meets slow belief: Evidence for a parallel-processing model of belief bias.

Authors:  Dries Trippas; Valerie A Thompson; Simon J Handley
Journal:  Mem Cognit       Date:  2017-05

7.  Characterizing belief bias in syllogistic reasoning: A hierarchical Bayesian meta-analysis of ROC data.

Authors:  Dries Trippas; David Kellen; Henrik Singmann; Gordon Pennycook; Derek J Koehler; Jonathan A Fugelsang; Chad Dubé
Journal:  Psychon Bull Rev       Date:  2018-12

8.  Fluency and belief bias in deductive reasoning: new indices for old effects.

Authors:  Dries Trippas; Simon J Handley; Michael F Verde
Journal:  Front Psychol       Date:  2014-06-24
  8 in total

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