Predictive coding posits that neural systems make forward-looking predictions about incoming information. Neural signals contain information not about the currently perceived stimulus, but about the difference between the observed and the predicted stimulus. We propose to extend the predictive coding framework from high-level sensory processing to the more abstract domain of theory of mind; that is, to inferences about others’ goals, thoughts, and personalities. We review evidence that, across brain regions, neural responses to depictions of human behavior, from biological motion to trait descriptions, exhibit a key signature of predictive coding: reduced activity to predictable stimuli. We discuss how future experiments could distinguish predictive coding from alternative explanations of this response profile. This framework may provide an important new window on the neural computations underlying theory of mind.

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Functional localizer tasks allow researchers to identify brain regions in each individual's brain, using a combination of anatomical and functional constraints. In this study we compare three social cognitive localizer tasks, designed to efficiently identify regions in the “Pain Matrix”, recruited in response to a person's physical pain, and the “Theory of Mind network”, recruited in response to a person's mental states (i.e. beliefs and emotions). Participants performed three tasks: first, the verbal false-belief stories task; second, a verbal task including stories describing physical pain versus emotional suffering; and third, passively viewing a nonverbal animated movie, which included segments depicting physical pain, and beliefs and emotions. All three localizers were efficient in identifying replicable, stable networks in individual subjects. The consistency across tasks makes all three tasks viable localizers. Nevertheless, there were small reliable differences in the location of the regions and the pattern of activity within regions, hinting at more specific representations. The new localizers go beyond those currently available: first, they simultaneously identify two functional networks with no additional scan time, and second, the non-verbal task extends the populations in whom functional localizers can be applied. These localizers will be made publicly available.

Intentional harms are typically judged to be morally worse than accidental harms. Distinguishing between intentional harms and accidents depends on the capacity for mental state reasoning (i.e., reasoning about beliefs and intentions), which is supported by a group of brain regions including the right temporo-parietal junction (RTPJ). Prior research has found that interfering with activity in RTPJ can impair mental state reasoning for moral judgment and that high-functioning individuals with autism spectrum disorders make moral judgments based less on intent information than neurotypical participants. Three experiments, using multivoxel pattern analysis, find that ( i ) in neurotypical adults, the RTPJ shows reliable and distinct spatial patterns of responses across voxels for intentional vs. accidental harms, and ( ii ) individual differences in this neural pattern predict differences in participants’ moral judgments. These effects are specific to RTPJ. By contrast, ( iii ) this distinction was absent in adults with autism spectrum disorders. We conclude that multivoxel pattern analysis can detect features of mental state representations (e.g., intent), and that the corresponding neural patterns are behaviorally and clinically relevant.