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

Behavior contains rich structure across many timescales, but 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 uses powerful, resampling-based, methods of multiple comparisons correction 1-3 to identify sequences of actions or choices that either differ significantly between groups or significantly correlate with a covariate of interest. We apply CBAS to different tasks and species (flies 4 , rats 5 , and humans 6 ) and find, in all instances, that it provides interpretable information about each behavioral task.