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Activity Number: 332 - SPEED: Section on Bayesian Statistical Science
Type: Contributed
Date/Time: Tuesday, August 1, 2017 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #323257 View Presentation
Title: A Bayesian Race Model for Response Times Under Cyclic Stimulus Discriminability
Author(s): Deborah Kunkel* and Kevin Potter and Trisha Van Zandt and Peter F Craigmile and Mario Peruggia
Companies: The Ohio State University and University of Massachusetts and The Ohio State University and The Ohio State University and The Ohio State University
Keywords: Cognitive modeling ; Inverse Gaussian distribution ; Harmonic regression ; Predictive diagnostics ; Gaussian diffusion
Abstract:

Response time data (RTs) from designed experiments are often used to validate theories of how the brain processes information. When an RT results from a task involving two or more possible responses, the cognitive process which determines the RT may be modeled as the first hitting time of underlying competing processes, with each process describing accumulation of information in favor of one of the responses. In one popular model, the racers are assumed to be Gaussian diffusions. Their hitting times are inverse Gaussian random variables and the resulting RT has a min-inverse Gaussian distribution. The RT data analyzed in this paper were collected in an experiment requiring subjects to perform a two-choice task in response to a regularly repeating sequence of stimuli. Starting from a min-inverse Gaussian likelihood for the RTs we build a Bayesian hierarchy for the rates and thresholds of the racing diffusions. The analysis allows to characterize patterns in a subject's response on the basis of features of the subject's diffusion rates (the subject's ``footprint'') and a subject's gradual changes in speed as trends in the diffusion thresholds.


Authors who are presenting talks have a * after their name.

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