The choice-wide behavioral association study: data-driven identification of interpretable behavioral components
TL;DR
This work presents a data-driven method—the choice-wide behavioral association study: CBAS—that systematically identifies such behavioral features that either significantly differ between groups or correlate with a covariate of interest.
Abstract
Behavior contains rich structure across many timescales. However, there is a dearth of methods to identify relevant components, especially over the longer periods required for learning and decision-making. Inspired by the goals and techniques of genome-wide association studies, we present a data-driven method—the choice-wide behavioral association study: CBAS—that systematically identifies such behavioral features. CBAS extracts sequences of actions or choices that either significantly differ between groups or correlate with a covariate of interest, using a powerful resampling-based method to correct for multiple comparisons. We illustrate CBAS through application to tasks performed by flies, rats and humans, showing that it provides unexpected and interpretable information in each case.