The choice-wide behavioral association study: data-driven identification of interpretable behavioral components

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. Data-driven methods to discover meaningful differences in complex behavior remain underdeveloped. Here the authors introduce the Choice-Wide Behavioral Association Study, which identifies interpretable behavioral components and reveals previously unrecognized features of behavior across flies, rats, and humans.