Title	Fitting a Stochastic Model to Eye Movement Time Series in a Categorization Task
Creator	Tupper, Paul
Publisher	Banff International Research Station for Mathematical Innovation and Discovery
Date Issued	2017-09-05T14:57
Description	Our goal is to develop an efficient framework for fitting stochastic continuous-time models to experimental data in cognitive psychology. As a simple test problem, we consider data from an eye-tracking study of attention in learning. For each subject, the data for each trial consists of the sequence of stimulus features that the subject fixates on, together with the duration of each fixation. We fit a stochastic differential equation model to this data, using the Approximate Bayesian Computation framework. For an individual subject we infer posterior distributions for the unknown parameters in the model.
Extent	30 minutes
Subject	Mathematics; Statistics; Partial differential equations
Type	Moving Image
File Format	video/mp4
Language	eng
Notes	Author affiliation: Simon Fraser University
Series	BIRS Workshop Lecture Videos (Oaxaca de Juárez (Mexico))
Date Available	2018-03-28
Provider	Vancouver : University of British Columbia Library
Rights	Attribution-NonCommercial-NoDerivatives 4.0 International
DOI	10.14288/1.0364517
URI	http://hdl.handle.net/2429/64975
Affiliation	Non UBC
Peer Review Status	Unreviewed
Scholarly Level	Faculty
Rights URI	http://creativecommons.org/licenses/by-nc-nd/4.0/
Aggregated Source Repository	DSpace

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