Toward comprehensive models of naturalistic cooperation and competition in primates

Award Summary

Yale University received a $1.13 million Cooperative Agreement from the National Institute on Drug Abuse (NIDA) under the Drug Use and Addiction Research Programs (CFDA 93.279), effective September 1, 2025 through June 30, 2030. This award supports comprehensive research into naturalistic cooperation and competition in primates, specifically examining how complex social interactions function in species whose social structures depend heavily on both cooperative and competitive behaviors. The research employs marmosets as the primary animal model to simultaneously collect multidimensional biobehavioral measurements—including both action-based and internal state-based variables—during naturalistic social interactions among freely moving subjects.

The project delivers two primary research products: (1) empirical data and analysis of the functional and directionally causal dependencies between biobehavioral variables in cooperative and competitive interactions, and (2) comprehensive, empirically testable generative models of primate social interaction validated through iterative comparison between computational models and experimental observations. The research utilizes multiple advanced modeling approaches, including multi-agent reinforcement learning with recurrent neural networks, to predict complex social behavioral patterns and develop mechanistic understanding of social cognition. This work contributes to NIDA's broader mission of supporting basic and translational research addressing substance use and addiction by advancing fundamental knowledge of social decision-making and behavioral mechanisms relevant to understanding drug use in social contexts.

Abstract

The flexible ability to work together for mutual benefits while competing against others for limited resources is a hallmark of advanced social cognition. Cooperative and competitive interactions are highly dynamic and complex. However, studying the precise behavioral mechanisms of these interactions has been challenging. This is partly due to the fact that the standard animal models in lab studies do not reliably cooperate. Moreover, typical studies do not include multidimensional behavioral measurements that are essential to understand such complex interactions. Therefore, there is a need to investigate complex social interactions in a species whose social structure strongly depends on both cooperation and competition, while tracking multiple action-based and internal state-related variables to obtain a comprehensive understanding of social behavior. The marmoset is an excellent species for studying complex social interactions grounded in context-dependent cooperative and competitive tendencies within their natural ethology. The first major goal of this proposal is to simultaneously and continuously collect multidimensional biobehavioral measurements, both action-based and internal state-based, during naturalistic cooperative and competitive interactions between freely moving marmosets. We aim to understand the functional and directionally causal dependencies of these biobehavioral variables in cooperative and competitive behaviors. The second major goal is to build comprehensive and empirically testable generative models of primate social interaction and to validate our understanding iteratively between the models and the experiments. We will use multiple modeling approaches to exploit their strengths: multi-agent reinforcement learning with recurrent neural networks will be used to learn complex patterns for prediction, and the structure and inputs to these models will be informed by dynamic bayesian networks to increase the interpretability of the models. We will use an embodied agent-based framework, with the recurrent neural networks driving musculoskeletal models of marmosets, to better model cooperative and competitive interactions of nonhuman primates. With the comprehensive biobehavioral data and the musculoskeletal model, we will build generalizable models of primate social interaction via a multi-level constraints-based framework. Finally, we will validate the generative models of primate social interaction by inducing multiple types of in silico environmental and task manipulations that are designed to predictably alter social strategies and carrying out those experiments in vivo that significantly alter the resulting social strategy. Overall, we aim to provide the most comprehensive understanding of primate social interactions to date, along with novel generative models of such behavior.