AI Article Synopsis
- Response time data from cognitive tasks are crucial for psychology and neuroscience, but current models often rely on strong assumptions or only analyze single trials.
- We introduce task-DyVA, a deep learning framework that effectively captures individual differences in response times across tasks, particularly in a large task-switching dataset.
- Our findings suggest that the framework reveals insights into cognitive processes, specifically highlighting a trade-off between stability and flexibility in task-switching, which can lead to more interpretable theories of brain function and behavior.
Article 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.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1038/s41562-022-01510-8 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!