Generalization, the transfer of knowledge to novel situations, has been studied in distinct disciplines that focus on different aspects. Here we propose a Bayesian model that assumes an exponential mapping from psychological space to outcome probabilities. This model is applicable to probabilistic reinforcement and integrates representation learning by tracking the relevance of stimulus dimensions. Since the belief state about this mapping is dependent on prior knowledge, we designed three experiments that emphasized this aspect. In all studies, we found behavior to be influenced by prior knowledge in a way that is consistent with the model. In line with the literature on representation learning, we found the representational geometry in the middle frontal gyrus to correspond to the behavioral preference for one over the other stimulus dimension and to be updated as predicted by the model. We interpret these findings as support for a common mechanism of generalization.

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