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The successor representation subserves hierarchical abstraction for goal-directed behavior
Sven Wientjes1, Clay B Holroyd1
1Department of Experimental Psychology, Ghent University, Ghent, Belgium.
Plos Computational Biology
|February 20, 2024
Summary
Humans use learned community structure to create hierarchical action plans. This successor representation learning aids goal-directed behavior and abstract planning, as shown in a virtual museum task.
Area of Science:
- Cognitive Science
- Neuroscience
- Psychology
Background:
- Humans excel at creating abstract, hierarchical plans by breaking down tasks into subgoals.
- Previous research identified sensitivity to 'community structure' in higher-order statistical learning.
- The successor representation model is proposed for hierarchical abstraction but lacks direct evidence in goal-directed behavior.
Purpose of the Study:
- To investigate if learned community structure is utilized for hierarchically informed action plans in goal-directed behavior.
- To determine if the successor representation underlies this hierarchical abstraction.
Main Methods:
- Participants searched for paintings in a virtual museum with community-structured 'wings'.
- Behavioral data, including choices and response times, were analyzed.
- Computational modeling using the successor representation was employed.
Main Results:
- Participant choices aligned with the museum's hierarchical community structure.
- Response times were best predicted by the successor representation.
- Correlation found between reflecting community structure and abstract planning ability.
Conclusions:
- Learned community structure supports hierarchical abstraction in goal-directed action planning.
- Successor representation learning is a key mechanism for this process.
- Findings bridge computational models of learning with behavioral evidence for hierarchical planning.
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