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Face recognition is a key component of successful social behaviour. However, the computational processes that underpin perceptual learning and recognition as faces transition from unfamiliar to familiar, are poorly understood. In predictive coding, learning occurs through prediction errors that update stimulus familiarity, but recognition is a function of both stimulus and contextual familiarity. Here we show that behavioural responses on a two-option face recognition task can be predicted by the level of contextual and facial familiarity in a computational model derived from predictive coding principles. Using fMRI, we show that activity in the superior temporal sulcus varies with the contextual familiarity in the model, whereas activity in the fusiform face area covaries with the prediction error parameter that updated the facial familiarity. Our results characterize the key computations underpinning the perceptual learning of faces, highlighting that the functional properties of face processing areas conform to the principles of predictive coding.
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