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B0428
Title: Hierarchical Hidden Markov models for response time data Authors:  Deborah Kunkel - Clemson University (United States) [presenting]
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.