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Modelling human behaviour in cognitive tasks with latent dynamical systems

Author

Listed:
  • Paul I. Jaffe

    (Stanford University
    Lumos Labs)

  • Russell A. Poldrack

    (Stanford University)

  • Robert J. Schafer

    (Lumos Labs)

  • Patrick G. Bissett

    (Stanford University)

Abstract

Response time data collected from cognitive tasks are a cornerstone of psychology and neuroscience research, yet existing models of these data either make strong assumptions about the data-generating process or are limited to modelling single trials. We introduce task-DyVA, a deep learning framework in which expressive dynamical systems are trained to reproduce sequences of response times observed in data from individual human subjects. Models fitted to a large task-switching dataset captured subject-specific behavioural differences with high temporal precision, including task-switching costs. Through perturbation experiments and analyses of the models’ latent dynamics, we find support for a rational account of switch costs in terms of a stability–flexibility trade-off. Thus, our framework can be used to discover interpretable cognitive theories that explain how the brain dynamically gives rise to behaviour.

Suggested Citation

  • Paul I. Jaffe & Russell A. Poldrack & Robert J. Schafer & Patrick G. Bissett, 2023. "Modelling human behaviour in cognitive tasks with latent dynamical systems," Nature Human Behaviour, Nature, vol. 7(6), pages 986-1000, June.
    • Handle: RePEc:nat:nathum:v:7:y:2023:i:6:d:10.1038_s41562-022-01510-8
    DOI: 10.1038/s41562-022-01510-8
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    Cited by:

    1. Tianlin Luo & Mengya Xu & Zhihao Zheng & Gouki Okazawa, 2025. "Limitation of switching sensory information flow in flexible perceptual decision making," Nature Communications, Nature, vol. 16(1), pages 1-16, December.

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