# A neural network model for the evolution of reconstructive social learning

## TL;DR

This paper introduces a neural network model to study how social learning evolves and how it interacts with individual learning in different environments.

## Contribution

The novel contribution is a biologically realistic model of reconstructive social learning that incorporates neural network evolution and learning mechanisms.

## Key findings

- An effective neural network structure evolves rapidly, leading to adaptive inborn behavior in static environments.
- The type of social learning and the order of individual and social learning strongly influence evolutionary outcomes.
- Evolutionary dynamics of social learning can be complex, with simulations converging to alternative outcomes.

## Abstract

Learning from others is an important adaptation. However, the evolution of social learning and its role in the spread of socially transmitted information are not well understood. Few models of social learning account for the fact that socially transmitted information must be reconstructed by the learner, based on the learner’s previous knowledge and cognition. To represent the reconstructive nature of social learning, we present a modelling framework that incorporates the evolution of a neural network and a simple yet biologically realistic learning mechanism. The framework encompasses various forms of individual and social learning and allows the investigation of their interplay. Individual-based simulations reveal that an effective neural network structure rapidly evolves, leading to adaptive inborn behaviour in static environments, pure individual learning in highly variable environments, and a combination of individual and social learning in environments of intermediate stability. However, the evolutionary outcome depends strongly on the type of social learning (social guidance versus social instruction) and the order of individual and social learning. Moreover, the evolutionary dynamics of social learning can be surprisingly complex, with replicate simulations converging to alternative outcomes. We discuss the relevance of our modelling framework for cultural evolution and suggest future avenues of research.