Sequential sampling models in computational psychiatry: Bayesian parameter estimation, model selection and classification

TL;DR

This paper reviews the use of sequential sampling models in computational psychiatry, emphasizing Bayesian methods for parameter estimation, model selection, and classification to improve understanding and diagnosis of mental illnesses.

Contribution

It introduces a comprehensive toolbox of cognitive models, focusing on Bayesian estimation techniques, for advancing computational psychiatry research.

Findings

Hierarchical Bayesian estimation offers advantages over traditional methods.

Sequential sampling models can explain diverse cognitive phenomena.

Non-parametric Bayesian methods aid in classifying mental illnesses.

Abstract

Current psychiatric research is in crisis. In this review I will describe the causes of this crisis and highlight recent efforts to overcome current challenges. One particularly promising approach is the emerging field of computational psychiatry. By using methods and insights from computational cognitive neuroscience, computational psychiatry might enable us to move from a symptom-based description of mental illness to descriptors based on objective computational multidimensional functional variables. To exemplify this I will survey recent efforts towards this goal. I will then describe a set of methods that together form a toolbox of cognitive models to aid this research program. At the core of this toolbox are sequential sampling models which have been used to explain diverse cognitive neuroscience phenomena but have so far seen little adoption in psychiatric research. I will then describe how these models can be fitted to subject data and highlight how hierarchical Bayesian estimation provides a rich framework with many desirable properties and benefits compared to traditional optimization-based approaches. Finally, non-parametric Bayesian methods provide general solutions to the problem of classifying mental illness within this framework.