Beyond Scalar Choice: Foraging as a Policy Framework for Decision Neuroscience
Abstract
Foraging is a fundamental form of decision-making, requiring animals to search, evaluate resources, allocate effort, manage risk, and coordinate behavior. Much of foraging neuroscience reduces this behavior to scalar choice, using reward magnitude, delay, effort, risk, or opportunity cost to explain whether an animal stays or leaves, accepts or rejects, harvests or switches. We argue that this approach leaves a defining feature of foraging underdeveloped. Animals do not simply compare values at isolated decision points. They implement policies that organize state-dependent patterns of approach, inspection, consumption, transport, withdrawal, persistence, and disengagement over time. Here we propose a policy-centered perspective in which the primary explanatory unit is the mapping from internal and external states onto an organized repertoire of actions. This framework retains the normative strengths of optimal foraging theory while broadening the interpretation of departures from its predictions. Apparent suboptimality may reveal hidden costs, latent state variables, or changes in policy organization rather than a simple failure of valuation. This perspective also reframes the neural problem. Understanding foraging as policy points to a distributed neural basis that integrates valuation, spatial memory, action selection, risk regulation, and behavioral sequencing. By shifting the unit of analysis from scalar choice to policy organization, foraging provides a principled bridge across behavioral ecology, decision neuroscience, reinforcement learning, and research on artificial agents.
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BibTeX
@misc{rezaei2026beyond,
title = {{Beyond Scalar Choice: Foraging as a Policy Framework for Decision Neuroscience}},
author = {Zahra Rezaei and Juan F. Ramirez-Villegas and Ian Q Whishaw and rob sutherland and Majid H Mohajerani},
howpublished = {PsyArXiv},
year = {2026},
month = sep,
doi = {10.31234/osf.io/p38wq_v1},
url = {https://osf.io/preprints/psyarxiv/p38wq_v1/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Beyond Scalar Choice: Foraging as a Policy Framework for Decision Neuroscience
AU - Zahra Rezaei
AU - Juan F. Ramirez-Villegas
AU - Ian Q Whishaw
AU - rob sutherland
AU - Majid H Mohajerani
PY - 2026
DA - 2026/09/21
PB - PsyArXiv
DO - 10.31234/osf.io/p38wq_v1
UR - https://osf.io/preprints/psyarxiv/p38wq_v1/
AB - Foraging is a fundamental form of decision-making, requiring animals to search, evaluate resources, allocate effort, manage risk, and coordinate behavior. Much of foraging neuroscience reduces this behavior to scalar choice, using reward magnitude, delay, effort, risk, or opportunity cost to explain whether an animal stays or leaves, accepts or rejects, harvests or switches. We argue that this approach leaves a defining feature of foraging underdeveloped. Animals do not simply compare values at isolated decision points. They implement policies that organize state-dependent patterns of approach, inspection, consumption, transport, withdrawal, persistence, and disengagement over time. Here we propose a policy-centered perspective in which the primary explanatory unit is the mapping from internal and external states onto an organized repertoire of actions. This framework retains the normative strengths of optimal foraging theory while broadening the interpretation of departures from its predictions. Apparent suboptimality may reveal hidden costs, latent state variables, or changes in policy organization rather than a simple failure of valuation. This perspective also reframes the neural problem. Understanding foraging as policy points to a distributed neural basis that integrates valuation, spatial memory, action selection, risk regulation, and behavioral sequencing. By shifting the unit of analysis from scalar choice to policy organization, foraging provides a principled bridge across behavioral ecology, decision neuroscience, reinforcement learning, and research on artificial agents.
ER -