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The amount of generalization that organisms show from learned associations to new stimuli can often be explained by perceptual dissimilarity, i.e. distance in psychological space. However, this doesn’t seem to be true using emotionally relevant stimuli like fearful faces. We propose that this can be understood in a Bayesian framework in which the organism infers a mapping of psychological space onto outcome probabilities by integrating prior assumptions with new information. This approach allows for the incorporation of domain specific prior knowledge. We employed face stimuli that differ on one fear relevant (emotional expression) and one fear irrelevant (identity) dimension in combination with Pavlovian conditioning to investigate generalization at several time points. We can show that generalization is skewed towards the fear relevant pole in the beginning, but gravitates towards the actually reinforced stimulus over time. Our Bayesian model that comprises a prior belief state about the structure of the predictive relationship between the psychological space and an aversive outcome can reproduce the experimental data.

Fear Generalization of Emotional Stimuli Can Be Explained By a Bayesian Inference Model