cpm: A python library for theory-driven modelling in computational psychiatry

AI Summary

cpm is a Python library for theory-driven modelling in computational psychiatry and cognitive neuroscience.
Integrates diverse models and methods, including associative, reinforcement learning, signal detection theories, and hierarchical estimation via empirical and variational Bayesian techniques.
Flexible, modular architecture designed for experts and novices, lowering barriers to advanced modelling across tasks like risky decision-making and metacognition.

AI Summary

It is designed to be accessible to both expert and non-expert modellers in order to conduct cutting-edge computational modelling, while adhering to best practices.

It covers a wide range of problems (such as risky decision-making, reward/punishment learning, perceptual metacognition), models (including those based on associative, reinforcement learning, and signal detection theories), and methods (such as hierarchical parameter estimation using empirical and variational Bayesian techniques).

Basic summary

PLoS Comput Biol. 2026 Jul 13;22(7):e1014481. doi: 10.1371/journal.pcbi.1014481. Online ahead of print.

ABSTRACT

The Computational Psychiatry Modelling (cpm) toolbox is a Python library for theory-driven modelling in computational psychiatry and cognitive (neuro-)science. cpm integrates a wide range of established approaches into a single framework. It is designed to be accessible to both expert and non-expert modellers in order to conduct cutting-edge computational modelling, while adhering to best practices. The toolbox provides a flexible, modular architecture that adjusts to different needs. It covers a wide range of problems (such as risky decision-making, reward/punishment learning, perceptual metacognition), models (including those based on associative, reinforcement learning, and signal detection theories), and methods (such as hierarchical parameter estimation using empirical and variational Bayesian techniques). Such a customisable toolbox aims to lower the barrier for beginners and to facilitate access to advanced modelling approaches in psychiatry and beyond.

PMID:42441721 | DOI:10.1371/journal.pcbi.1014481

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