Using Theory of Mind to Arbitrate between Social and Non-social Learning

2026-07-30

AI Summary

How do humans balance social and asocial learning to maximize utility? This work proposes a rational mentalizing model that formalizes theory of mind as a utility-driven arbitration mechanism for social learning. By employing Bayesian inference to estimate others’ goals and the informativeness of their actions, the model quantifies the expected benefit of social learning and compares it against the value of independent exploration to select the optimal strategy. Integrating behavioral experiments with computational modeling, the framework successfully reproduces human selective learning behavior in a novel game-theoretic task, thereby revealing the central role of theory of mind in learning-related decision-making.

Abstract

Social learning is a powerful mechanism through which agents learn about the world from others. However, humans sometimes choose direct experience over social learning, which can carry time and cognitive resource costs. How do people balance social and non-social learning? We propose a Rational Mentalizing model of the decision to engage in social learning. This model estimates the utility of social learning by reasoning about another agent's goal and the informativeness of their future actions. It then weighs the utility of social learning against the utility of non-social learning. Using a novel game where players choose between observing other agents or exploring the environment, we show that the Rational Mentalizing model can quantitatively capture human trade-offs between these strategies. These findings suggest that selective social learning is guided by 'Theory of Mind' in the service of utility maximization.

Problem

• social learning
• non-social learning
• Theory of Mind
• learning trade-off
• utility maximization

Innovation

• Theory of Mind
• social learning
• Rational Mentalizing
• utility maximization
• cognitive modeling