biostudies-literatureUrbaniak RSimons FoundationU.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and StrokeU.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)NINDS NIH HHS21770https://www.ebi.ac.uk/biostudies/studies/S-EPMC11411083biostudies-literatureUnknown14(1)Foraging for food is a rich and ubiquitous animal behavior that involves complex cognitive decisions, and interactions between different individuals and species. There has been exciting recent progress in understanding multi-agent foraging behavior from cognitive, neuroscience, and statistical perspectives, but integrating these perspectives can be elusive. This paper seeks to unify these perspectives, allowing statistical analysis of observational animal movement data to shed light on the viability of cognitive models of foraging strategies. We start with cognitive agents with internal preferences expressed as value functions, and implement this in a biologically plausible neural network, and an equivalent statistical model, where statistical predictors of agents' movements correspond to Scientific reportsLinking cognitive strategy, neural mechanism, and movement statistics in group foraging behaviors.PMC114110831K99NS131256-01Society of FellowsR00 NS131256K99 NS131256Urbaniak RMackevicius EXie MfalseLinking cognitive strategy, neural mechanism, and movement statistics in group foraging behaviors.Foraging for food is a rich and ubiquitous animal behavior that involves complex cognitive decisions, and interactions between different individuals and species. There has been exciting recent progress in understanding multi-agent foraging behavior from cognitive, neuroscience, and statistical perspectives, but integrating these perspectives can be elusive. This paper seeks to unify these perspectives, allowing statistical analysis of observational animal movement data to shed light on the viability of cognitive models of foraging strategies. We start with cognitive agents with internal preferences expressed as value functions, and implement this in a biologically plausible neural network, and an equivalent statistical model, where statistical predictors of agents' movements correspond to 2024-01-01T00:00:00Z2024 Sep2026-06-03T23:42:45.084Z2025-04-04T02:30:57.769ZS-EPMC114110833929426110.1038/s41598-024-71931-0