title: Optimal behavioral hierarchy.
abstract: Human behavior has long been recognized to display hierarchical structure: actions fit together into subtasks, which cohere into extended goal-directed activities. Arranging actions hierarchically has well established benefits, allowing behaviors to be represented efficiently by the brain, and allowing solutions to new tasks to be discovered easily. However, these payoffs depend on the particular way in which actions are organized into a hierarchy, the specific way in which tasks are carved up into subtasks. We provide a mathematical account for what makes some hierarchies better than others, an account that allows an optimal hierarchy to be identified for any set of tasks. We then present results from four behavioral experiments, suggesting that human learners spontaneously discover optimal action hierarchies.
authors.affiliation: Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America.
authors.forename: Alec
authors.initials: A
authors.lastname: Solway
authors.affiliation: Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America.
authors.forename: Carlos
authors.initials: C
authors.lastname: Diuk
authors.affiliation: Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America.
authors.forename: Natalia
authors.initials: N
authors.lastname: Córdova
authors.affiliation: Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America.
authors.forename: Debbie
authors.initials: D
authors.lastname: Yee
authors.affiliation: School of Computer Science, University of Massachusetts Amherst, Amherst, Massachusetts, United States of America.
authors.forename: Andrew G
authors.initials: AG
authors.lastname: Barto
authors.affiliation: Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America; Department of Psychology, Princeton University, Princeton, New Jersey, United States of America.
authors.forename: Yael
authors.initials: Y
authors.lastname: Niv
authors.affiliation: Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America; Department of Psychology, Princeton University, Princeton, New Jersey, United States of America.
authors.forename: Matthew M
authors.initials: MM
authors.lastname: Botvinick
mesh_terms: D000293:Adolescent; D000328:Adult; D001519:Behavior / Q000502:physiology*; D019295:Computational Biology; D005260:Female; D006040:Goals; D006801:Humans; D007858:Learning / Q000502:physiology*; D008297:Male; D008875:Middle Aged; D008959:Models, Neurological; D055815:Young Adult