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Multi-level Bayesian Models for the Study of Confidence in Decision Making

Wirthlin, Marco (2019) Multi-level Bayesian Models for the Study of Confidence in Decision Making. Master's Thesis / Essay, Human-Machine Communication.

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Abstract

The faculty to judge the accuracy of our own performance during perceptual decision-making is called metacognition. Schizophrenia patients, because of error-monitoring deficits, are hypothesized to judge their own actions less accurately. Here, behavioral, neural and kinematic trial-level measurements have been studied in healthy and schizophrenia participants on the trial-level in relationship to metacognition via by-timepoint mixed-effect linear regressions. Furthermore, a custom R-package, "StanDDM", implementing a Bayesian, multi-level fitting framework for Drift Diffusion Models was developed to characterize cognitive processes in both groups. Behavioral and modeling results suggest that schizophrenia patients, while performing equally well as healthy subjects in terms of metacognition and accuracy, use a different cognitive strategy which is not reliant on metacognition, but focused on the own motor execution and stimular properties. Mouse tracking and EEG results show that healthy participants rely on their ability to observe their own actions, while patients do not. Metacognition was associated with frontal regions during decision making only in healthy subjects, usually also involved in error processing. This suggests that patients may be unable to rely on metacognition because of disconnectivity between brain areas, which translates in slower task execution and lower motor preparation times.

Item Type: Thesis (Master's Thesis / Essay)
Supervisor:
Supervisor nameSupervisor E mail
Vugt, M.K. vanM.K.van.Vugt@rug.nl
Supervisor (outside RUG):
Supervisor outside RUG nameSupervisor outside RUG E mail
Faivre, NathanUNSPECIFIED
Degree programme: Human-Machine Communication
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 01 Jul 2019
Last Modified: 09 Jul 2019 09:20
URI: http://fse.studenttheses.ub.rug.nl/id/eprint/19786

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