Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 112, Issue 10, Pages 3098-3103Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1414219112
Keywords
planning; hierarchical reinforcement learning; memoization; pruning
Categories
Funding
- British Academy
- German Research Foundation
- Wellcome Trust-National Institutes of Health (NIH)
- Sackler Fellowship in Psychobiology
- NIH [T32GM007753, F30MH100729]
- Swiss National Science Foundation
- University of Zurich
- Neuroscience Centre Zurich
- Gatsby Charitable Foundation
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Humans routinely formulate plans in domains so complex that even the most powerful computers are taxed. To do so, they seem to avail themselves of many strategies and heuristics that efficiently simplify, approximate, and hierarchically decompose hard tasks into simpler subtasks. Theoretical and cognitive research has revealed several such strategies; however, little is known about their establishment, interaction, and efficiency. Here, we use model-based behavioral analysis to provide a detailed examination of the performance of human subjects in a moderately deep planning task. We find that subjects exploit the structure of the domain to establish subgoals in a way that achieves a nearly maximal reduction in the cost of computing values of choices, but then combine partial searches with greedy local steps to solve subtasks, and maladaptively prune the decision trees of subtasks in a reflexive manner upon encountering salient losses. Subjects come idiosyncratically to favor particular sequences of actions to achieve subgoals, creating novel complex actions or options.
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