Social learning in a structured world

Social learning is essential for human success, enabling individuals to acquire skills and knowledge - traits - from others.

While theories of "cultural evolution" suggest that success-based imitation is crucial for the spread of adaptive traits, recent studies show that it is much rarer than expected.

Our project addresses this mismatch by recognizing that traits are hierarchically structured: learning advanced skills requires mastering more basic ones, like learning guitar chords before solos.

Through three studies using computational models and experiments, we will investigate how such structure influences social learning.

Study 1 will use simulations to quantify the performance of social learning strategies in structured trait environments.

Study 2 employs experiments to test these predictions, and explore how individuals adapt their strategies based on trait structure.

Study 3 will use findings from Studies 1 & 2 to test predictions about how traits spread in groups with large-scale experiments.This research addresses key puzzles regarding the underuse of success-based social learning, variability in learning strategies, and the mechanisms allowing their adjustment with experience.

By integrating formal models with empirical testing, we aim to understand how trait structure impacts social learning and its role in cultural evolution.

This project will not only illuminate the nature of social learning but also inform interventions for efficient knowledge dissemination