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Logistic Mixed Models to Investigate Implicit and Explicit Belief Tracking

Overview of attention for article published in Frontiers in Psychology, November 2016
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Title
Logistic Mixed Models to Investigate Implicit and Explicit Belief Tracking
Published in
Frontiers in Psychology, November 2016
DOI 10.3389/fpsyg.2016.01681
Pubmed ID
Authors

Martin Lages, Anne Scheel

Abstract

We investigated the proposition of a two-systems Theory of Mind in adults' belief tracking. A sample of N = 45 participants predicted the choice of one of two opponent players after observing several rounds in an animated card game. Three matches of this card game were played and initial gaze direction on target and subsequent choice predictions were recorded for each belief task and participant. We conducted logistic regressions with mixed effects on the binary data and developed Bayesian logistic mixed models to infer implicit and explicit mentalizing in true belief and false belief tasks. Although logistic regressions with mixed effects predicted the data well a Bayesian logistic mixed model with latent task- and subject-specific parameters gave a better account of the data. As expected explicit choice predictions suggested a clear understanding of true and false beliefs (TB/FB). Surprisingly, however, model parameters for initial gaze direction also indicated belief tracking. We discuss why task-specific parameters for initial gaze directions are different from choice predictions yet reflect second-order perspective taking.

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Mendeley readers

The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 40%
Student > Bachelor 4 16%
Lecturer 2 8%
Student > Doctoral Student 2 8%
Researcher 2 8%
Other 3 12%
Unknown 2 8%
Readers by discipline Count As %
Psychology 6 24%
Social Sciences 3 12%
Engineering 3 12%
Arts and Humanities 1 4%
Nursing and Health Professions 1 4%
Other 7 28%
Unknown 4 16%