Fig. 7: Emergence of social learning strategies in agent-based simulations.
From: Feature-based reward learning shapes human social learning strategies
Learned social feature weights under temporal change (a–d), under spatial change (e–h), in dangerous environments (i–l) and with competition (m–p). Higher values correspond to a stronger influence. For all simulations, N = 100. The figures show the median (black dot) average value from 800 simulation runs, the ‘box’ covers the first to third quartiles of the distribution, and the whiskers the range of simulation data.
