Journal article
Optimal behavioral hierarchy
PLoS computational biology, Vol.10(8), e1003779
08/01/2014
DOI: 10.1371/journal.pcbi.1003779
PMID: 25122479
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.
Details
- Title: Subtitle
- Optimal behavioral hierarchy
- Creators
- Alec Solway - Princeton UniversityCarlos Diuk - Princeton UniversityNatalia Córdova - Princeton UniversityDebbie Yee - Princeton UniversityAndrew G Barto - University of Massachusetts AmherstYael NivMatthew M Botvinick - Princeton University
- Resource Type
- Journal article
- Publication Details
- PLoS computational biology, Vol.10(8), e1003779
- DOI
- 10.1371/journal.pcbi.1003779
- PMID
- 25122479
- NLM abbreviation
- PLoS Comput Biol
- ISSN
- 1553-7358
- eISSN
- 1553-7358
- Publisher
- PLoS
- Language
- English
- Date published
- 08/01/2014
- Academic Unit
- Psychological and Brain Sciences
- Record Identifier
- 9985180925802771
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