The evolution of high-fidelity social learning

TL;DR

This paper models the evolution of high-fidelity social learning, showing it depends on specific conditions like trait complexity and learning costs, which explains its rarity despite common social learning.

Contribution

It introduces a Bayesian model contrasting high- and low-fidelity social learning, identifying conditions favoring high-fidelity transmission and its role in cultural evolution.

Findings

High-fidelity transmission evolves with inexpensive social and individual learning.

Low-fidelity transmission is more robust when traits are simple and problems are easy.

Conditions for high-fidelity evolution are stricter, explaining its rarity.

Abstract

A defining feature of human culture is that knowledge and technology continually improve over time. Such cumulative cultural evolution (CCE) probably depends far more heavily on how reliably information is preserved than on how efficiently it is refined. Therefore, one possible reason that CCE appears diminished or absent in other species is that it requires accurate but specialized forms of social learning at which humans are uniquely adept. Here, we develop a Bayesian model to contrast the evolution of high-fidelity social learning, which supports CCE, against low-fidelity social learning, which does not. We find that high-fidelity transmission evolves when (1) social and (2) individual learning are inexpensive, (3) traits are complex, (4) individual learning is abundant, (5) adaptive problems are difficult and (6) behaviour is flexible. Low-fidelity transmission differs in many respects. It not only evolves when (2) individual learning is costly and (4) infrequent but also proves more robust when (3) traits are simple and (5) adaptive problems are easy. If conditions favouring the evolution of high-fidelity transmission are stricter (3 and 5) or harder to meet (2 and 4), this could explain why social learning is common, but CCE is rare.