Social Learning via Bayesian Inverse Reinforcement Learning: Learning from and about a Learner
Abstract
What does a social learner learn? Research has explored imitation-based social learning strategies as well as inverse reinforcement algorithms that estimate others' true reward function. In the current study, we propose that social learning may be more elaborate and develop a model of social learning using Bayesian inference that seeks to understand both the task an observed demonstrator is performing and the demonstrator itself. Using simulations, we show that the model is able to learn about the demonstrator when provided with full and partial information. We strengthen this point by asking the model to make inferences about missing choice and reward information. Last, we show that the model is able to represent one set of beliefs about the environment while attributing a distinct set of beliefs to the demonstrator. Thus, we move away from simple models of social learning, investigating inference-making as a core mechanism of social learning.