Also like vision, theory of mind is a complex cognitive process that depends on many different brain regions with likely distinct computational roles (DiCarlo et al., 2012). We suggest that a predictive Stem Cell Compound Library datasheet coding framework can be used both to shed light on existing data about these brain regions, and to suggest productive
s of research. First, we

briefly review predictive coding, and sketch a model we believe can serve as an integrative framework for the neuroscience of theory of mind. Second, we provide a selective review of existing neuroimaging studies of theory of mind. Across different stimuli and designs, with correspondingly different social information and predictive contexts, we find a classic signature of a predictive error code: reduced neural response to more predictable inputs. Third, we discuss how to distinguish predictive coding from alternative explanations of this response profile, including differences in attention or processing time. Based on recent neuroimaging experiments in visual neuroscience, we suggest strategies

learn more for future experiments to test specific predictions of predictive coding. Finally, we discuss the implications of predictive coding for our understanding of the neural basis of theory of mind. The central idea of “predictive coding” is that (some) neural responses contain information not about the value of a currently perceived stimulus, but about the difference between the stimulus value and the expected value (Fiorillo et al., 2003, Schultz et al., 1997 and Schultz, 2010). This general idea is most familiar from studies of “reward prediction error” in dopaminergic neurons in the striatum. Famously, these neurons initially fire when

the animal receives a valued reward, like a drop of juice, and do not respond above baseline to neutral stimuli, such as aural tones. After the animal has learned that a particular tone predicts the arrival of a drop of Liothyronine Sodium juice two seconds later, the same neurons fire at the time of the tone. Tellingly, the firing rate of these neurons no longer rises above baseline at the time the juice drop actually arrives. Nevertheless, the neurons still respond to juice. If the tone that typically predicts a single drop of juice is unexpectedly followed by two drops of juice, the neurons will increase their firing; and if the tone is unexpectedly followed by no drops of juice, the neurons decrease their firing rate below baseline ( Fiorillo et al., 2003 and Schultz et al., 1997). These dopaminergic neurons exhibit the simplest and best known example of a neural “error” code: the rate of firing corresponds to any currently “new” (i.e.