Modelling human behaviour in cognitive tasks with latent dynamical systems

• Published in: Nature human behaviour Vol. 7; no. 6; pp. 986 - 1000
• Main Authors: Jaffe, Paul I., Poldrack, Russell A., Schafer, Robert J., Bissett, Patrick G.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.06.2023; Nature Publishing Group
• Subjects: 631/378/2649/2150; 631/477/2811; Behavior; Behavioral Sciences; Biomedical and Life Sciences; Cognitive tasks; Cognitive theories; Cognitive-behavioral factors; Deep learning; Dynamical systems; Experimental Psychology; Flexibility; Life Sciences; Microeconomics; Neurosciences; Personality and Social Psychology; Psychology; Reaction time; Research subjects; Sequences
• ISSN: 2397-3374; 2397-3374
• DOI: 10.1038/s41562-022-01510-8

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. The authors introduce a deep learning framework to reproduce sequences of response times and use it to provide evidence for a stability–flexibility trade-off underlying task-switching costs.