Emergent social transmission of model-based representations without inference

TL;DR

This paper demonstrates how simple social cues can enable the transmission of complex, model-based knowledge without requiring mentalizing, through reinforcement learning simulations.

Contribution

It introduces a minimal social learning mechanism that supports the emergence of sophisticated representations without inference of mental states.

Findings

Social cues bias learning towards expert-like representations.

Model-based learners benefit most from social exposure, with faster learning.

Cultural transmission can occur via non-mentalizing, simple social processes.

Abstract

How do people acquire rich, flexible knowledge about their environment from others despite limited cognitive capacity? Humans are often thought to rely on computationally costly mentalizing, such as inferring others' beliefs. In contrast, cultural evolution emphasizes that behavioral transmission can be supported by simple social cues. Using reinforcement learning simulations, we show how minimal social learning can indirectly transmit higher-level representations. We simulate a na\"ive agent searching for rewards in a reconfigurable environment, learning either alone or by observing an expert - crucially, without inferring mental states. Instead, the learner heuristically selects actions or boosts value representations based on observed actions. Our results demonstrate that these cues bias the learner's experience, causing its representation to converge toward the expert's. Model-based learners benefit most from social exposure, showing faster learning and more expert-like representations. These findings show how cultural transmission can arise from simple, non-mentalizing processes exploiting asocial learning mechanisms.