Fitting hidden Markov models to psychological data

Ingmar Visser, Maartie E.J. Raijmakers, Peter C.M. Molenaar

Research output: Contribution to journalArticle

47 Scopus citations

Abstract

Markov models have been used extensively in psychology of learning. Applications of hidden Markov models are rare however. This is partially due to the fact that comprehensive statistics for model selection and model assessment are lacking in the psychological literature. We present model selection and model assessment statistics that are particularly useful in applying hidden Markov models in psychology. These statistics are presented and evaluated by simulation studies for a toy example. We compare AIC, BIC and related criteria and introduce a prediction error measure for assessing goodness-of-fit. In a simulation study, two methods of fitting equality constraints are compared. In two illustrative examples with experimental data we apply selection criteria, fit models with constraints and assess goodness-of-fit. First, data from a concept identification task is analyzed. Hidden Markov models provide a flexible approach to analyzing such data when compared to other modeling methods. Second, a novel application of hidden Markov models in implicit learning is presented. Hidden Markov models are used in this context to quantify knowledge that subjects express in an implicit learning task. This method of analyzing implicit learning data provides a comprehensive approach for addressing important theoretical issues in the field.

Original languageEnglish (US)
Pages (from-to)185-199
Number of pages15
JournalScientific Programming
Volume10
Issue number3
DOIs
StatePublished - Jan 1 2002

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications

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