A Deep Learning Approach to Analyzing Continuous-Time Cognitive Processes.

ABSTRACT: The dynamics of the mind are complex. Mental processes unfold continuously in time and may be sensitive to a myriad of interacting variables, especially in naturalistic settings. But statistical models used to analyze data from cognitive experiments often assume simplistic dynamics. Recent advances in deep learning have yielded startling improvements to simulations of dynamical cognitive processes, including speech comprehension, visual perception, and goal-directed behavior. But due to poor interpretability, deep learning is generally not used for scientific analysis. Here, we bridge this gap by showing that deep learning can be used, not just to imitate, but to analyze complex processes, providing flexible function approximation while preserving interpretability. To do so, we defi

SUBMITTER: Shain C 

PROVIDER: S-EPMC10962694 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature