An associative-learning account of collective learning

Associative learning is an important adaptive mechanism that is well conserved among a broad range of species. Although it is typically studied in isolated animals, associative learning can occur in the presence of conspecifics in nature. While many social aspects of individual learning have received much attention, the study of collective learning—the acquisition of knowledge in groups of animals through shared experience—has a much shorter history. Consequently, the conditions under which collective learning emerges and the mechanisms that underlie such emergence are still largely unexplored. Here, we develop a parsimonious model of collective learning based on the complementary integration of associative learning and collective intelligence. It assumes (1) a simple associative learning rule, based on the Rescorla-Wagner model, in which the actions of conspecifics serve as cues, and (2) a horse-race action selection rule. Simulations of this model show no benefit of group training over individual training in a simple discrimination task (A+/B-). However, a group-training advantage emerges after the discrimination task is reversed (A-/B+). Model predictions suggest that, in a dynamic environment, simply tracking the actions of conspecifics that are solving the same problem can yield superior learning to individual animals and enhanced performance to the group.