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

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Dayan,  P       
Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Kastner, D., Williams, G., Holobetz, C., Romano, J., & Dayan, P. (submitted). The choice-wide behavioral association study: data-driven identification of interpretable behavioral components.


Cite as: https://hdl.handle.net/21.11116/0000-000E-7DBF-0
Abstract
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 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, rats, and humans) and find, in all instances, that it provides interpretable information about each behavioral task.