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Updated: Dec 7, 2025

A Complex Diving-For-Food Task to Investigate Social Organization and Interactions in Rats
Published on: May 8, 2021
Synergistic Benefits of Group Search in Rats
Máté Nagy1, Attila Horicsányi2, Enikő Kubinyi3
1Department of Collective Behaviour, Max Planck Institute of Animal Behavior, Universitätsstraße 10, Konstanz 78457, Germany; Department of Biology, University of Konstanz, Universitätsstraße 10, Konstanz 78457, Germany; Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Universitätsstraße 10, Konstanz 78457, Germany; MTA-ELTE Statistical and Biological Physics Research Group, Hungarian Academy of Sciences, Pázmány P. stny. 1A, Budapest 1117, Hungary; MTA-ELTE "Lendület" Collective Behaviour Research Group, Hungarian Academy of Sciences, Pázmány P. stny. 1A, Budapest 1117, Hungary; Department of Biological Physics, Eötvös Loránd University, Pázmány P. stny. 1A, Budapest 1117, Hungary.
Abstract:
Locating unpredictable but essential resources is a task that all mobile animals have to perform in order to survive and reproduce. Research on search strategies has focused largely on independent individuals [1-3], but many organisms display collective behaviors, including during group search and foraging [4-6]. One classical experimental search task, informing studies of navigation, memory, and learning, is the location of a reward in a confined, complex maze setting [7, 8]. Rats (Rattus norvegicus) have been paradigmatic in psychological and biological studies [9, 10], but despite rats being highly social [11, 12], their group search behavior has not been investigated. Here, we explore the decision making of rats searching individually, or in groups, for a reward in a complex maze environment. Using automated video tracking, we find that rats exhibit-even when alone-a partially systematic search, leading to a continuous increase in their chance of finding the reward because of increased attraction to unexplored regions. When searching together, however, synergistic group advantages arise through integration of individual exploratory and social behavior. The superior search performances result from a strategy that represents a hierarchy of influential preferences in response to social and asocial cues. Furthermore, we present a computational model to compare the essential factors that influence how collective search operates and to validate that the collective search strategy increases the search efficiency of individuals in groups. This strategy can serve as direct inspiration for designing computational search algorithms and systems, such as autonomous robot groups, to explore areas inaccessible to humans. VIDEO ABSTRACT.

