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Open Access Publications from the University of California

Integrating Experience into Bayesian Theory of Mind


Other people's mental states---what they want, what they know, and how they combine the two to act---are structured by the experiences that they've had. In line with this, we propose that inferences about other people's experiences are a central, but often neglected, aspect of human Theory of Mind. We explore this idea by presenting and testing a computational model that jointly infers others' desires, knowledge, and experience. We find that, by focusing inferences on others' experience, our model can make richer inferences about other's knowledge than would be otherwise possible. Our model quantitatively fits participant judgments on two experiments above an and beyond an alternative model. Overall, our work extends the richness of human Theory of Mind judgements that can be formalized as Bayesian inference over a generative model.

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