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Formalizing planning and information search in naturalistic decision-making
L T Hunt1, N D Daw2, P Kaanders3
1Department of Psychiatry, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK. laurence.hunt@psych.ox.ac.uk.
Nature Neuroscience
|June 22, 2021
Summary
Mammals and birds use planning and information sampling for complex decisions. Advances in algorithms help explain the neural basis of these behaviors and their evolutionary origins.
Area of Science:
- Neuroscience
- Cognitive Science
- Animal Behavior
Background:
- Complex decision-making in mammals and birds is temporally extended, requiring planning and information sampling.
- Current understanding of these behaviors is limited compared to simpler decision tasks.
- Recent algorithmic advancements offer new perspectives on analyzing neural and behavioral data.
Purpose of the Study:
- To review advances in algorithms supporting planning and information search.
- To explain the neural and behavioral data related to complex decision-making.
- To understand the evolutionary origins of planning and curiosity in different species.
Main Methods:
- Review of recent algorithmic advances in planning and information search.
- Analysis of neural data from medial temporal lobe, prefrontal, and cingulate cortices.
- Examination of behavioral data from decision-making tasks in mammals and birds.
Main Results:
- Algorithmic frameworks can explain neural and behavioral data in complex decision-making.
- Planning and information search are crucial for improving future action selection.
- Understanding the evolutionary basis for planning and curiosity across species.
Conclusions:
- Planning and information search are complementary mechanisms for enhancing future choices.
- Neural activity in specific brain regions supports these complex cognitive functions.
- Further research integrating algorithmic, neural, and behavioral approaches is needed.
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