The Journal of Mathematical Psychology publishes research on mathematical and computational models of psychological processes. Articles focus on developing and evaluating novel algorithms and theoretical frameworks for analyzing psychological data, including methods for latent variable modeling, Bayesian hypothesis testing, nonlinear dynamical systems, associative learning, information theory applications, network approaches to psychopathology, memory encoding and retrieval, and measurement validation. The journal emphasizes the quantitative and formal analysis of psychological phenomena.
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