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Activity Number: 346 - New Methods with Applications in Mental Health Statistics
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 10:30 AM to 12:20 PM
Sponsor: Mental Health Statistics Section
Abstract #306556 Presentation
Title: Hierarchical Hidden Markov Models for Response Time Data
Author(s): Deborah Kunkel* and Zhifei Yan and Peter F. Craigmile and Mario Peruggia and Trisha Van Zandt
Companies: Clemson University and Google and The Ohio State University and The Ohio State University and The Ohio State University
Keywords: hidden Markov model; cognitive modeling; human performance data; mixture modeling; Weibull distribution
Abstract:

Psychological data, particularly measurements obtained sequentially in experiments designed to test theories of human cognition, are often treated as independent and identically distributed samples from a single distribution that describes the cognitive process. This assumption is made for mathematical and analytic convenience; it is widely appreciated that such data are in fact mixtures from two or more processes, a subset of which are associated with the cognitive process of interest. Our modeling framework describes response times (RTs) as arising from a mixture of three distinct distributions. Transitions across the distributions are governed by a hidden Markov structure whose states produce either fast, average, or slow RTs. This process is nested within a second Hidden Markov structure, producing an “environment” process that allows the distribution of the response status to evolve due to factors such as fatigue and distractions. We performed a detection experiment designed to elicit responses under three environments that mimic the external conditions thought to influence latent statuses. We present our hierarchical model and demonstrate its fit on the experimental data.


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

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