The representational structure of mental states generalizes across target people and stimulus modalities.

Each individual experiences mental states in their own idiosyncratic way, yet perceivers can accurately understand a huge variety of states across unique individuals. How do they accomplish this feat? Do people think about their own anger in the same ways as another person's anger? Is reading about someone's anxiety the same as seeing it? Here, we test the hypothesis that a common conceptual core unites mental state representations across contexts. Across three studies, participants judged the mental states of multiple targets, including a generic other, the self, a socially close other, and a socially distant other. Participants viewed mental state stimuli in multiple modalities, including written scenarios and images. Using representational similarity analysis, we found that brain regions associated with social cognition expressed stable neural representations of mental states across both targets and modalities. Together, these results suggest that people use stable models of mental states across different people and contexts.

Tools of the Trade Multivoxel pattern analysis in fMRI: a practical introduction for social and affective neuroscientists.

The family of neuroimaging analytical techniques known as multivoxel pattern analysis (MVPA) has dramatically increased in popularity over the past decade, particularly in social and affective neuroscience research using functional magnetic resonance imaging (fMRI). MVPA examines patterns of neural responses, rather than analyzing single voxel- or region-based values, as is customary in conventional univariate analyses. Here, we provide a practical introduction to MVPA and its most popular variants (namely, representational similarity analysis (RSA) and decoding analyses, such as classification using machine learning) for social and affective neuroscientists of all levels, particularly those new to such methods. We discuss how MVPA differs from traditional mass-univariate analyses, the benefits MVPA offers to social neuroscientists, experimental design and analysis considerations, step-by-step instructions for how to implement specific analyses in one's own dataset and issues that are currently facing research using MVPA methods.

People represent their own mental states more distinctly than those of others.

One can never know the internal workings of another person-one can only infer others' mental states based on external cues. In contrast, each person has direct access to the contents of their own mind. Here, we test the hypothesis that this privileged access shapes the way people represent internal mental experiences, such that they represent their own mental states more distinctly than the states of others. Across four studies, participants considered their own and others' mental states; analyses measured the distinctiveness of mental state representations. Two fMRI studies used representational similarity analyses to demonstrate that the social brain manifests more distinct activity patterns when thinking about one's own states vs. others'. Two behavioral studies complement these findings, and demonstrate that people differentiate between states less as social distance increases. Together, these results suggest that we represent our own mind with greater granularity than the minds of others.

The brain represents people as the mental states they habitually experience.

Social life requires us to treat each person according to their unique disposition. To tailor our behavior to specific individuals, we must represent their idiosyncrasies. Here, we advance the hypothesis that our representations of other people reflect the mental states we perceive those people to habitually experience. We tested this hypothesis by measuring whether neural representations of people could be accurately reconstructed by summing state representations. Separate participants underwent functional MRI while considering famous individuals and individual mental states. Online participants rated how often each famous person experiences each state. Results supported the summed state hypothesis: frequency-weighted sums of state-specific brain activity patterns accurately reconstructed person-specific patterns. Moreover, the summed state account outperformed the established alternative-that people represent others using trait dimensions-in explaining interpersonal similarity. These findings demonstrate that the brain represents people as the sums of the mental states they experience.

The Social Brain Automatically Predicts Others' Future Mental States.

Social life requires people to predict the future: people must anticipate others' thoughts, feelings, and actions to interact with them successfully. The theory of predictive coding suggests that the social brain may meet this need by automatically predicting others' social futures. If so, when representing others' current mental state, the brain should already start representing their future states. To test this hypothesis, we used fMRI to measure female and male human participants' neural representations of mental states. Representational similarity analysis revealed that neural patterns associated with mental states currently under consideration resembled patterns of likely future states more so than patterns of unlikely future states. This effect manifested in activity across the social brain network and in medial prefrontal cortex in particular. Repetition suppression analysis also supported the social predictive coding hypothesis: considering mental states presented in predictable sequences reduced activity in the precuneus relative to unpredictable sequences. In addition to demonstrating that the brain makes automatic predictions of others' social futures, the results also demonstrate that the brain leverages a 3D representational space to make these predictions. Proximity between mental states on the psychological dimensions of rationality, social impact, and valence explained much of the association between state-specific neural pattern similarity and state transition likelihood. Together, these findings suggest that the way the brain represents the social present gives people an automatic glimpse of the social future.SIGNIFICANCE STATEMENT When you see a ball in flight, your brain calculates, not just its static visual features such as size and shape, but also predicts its future trajectory. Here, we investigated whether the same might hold true in the social world: when we see someone flying into a rage, does our brain automatically predict their social trajectory? In this study, we scanned participants' brain activity while they judged others' mental states. We found that neural activity associated with a given state resembled activity associated with likely future states. Additionally, unpredictable sequences of states evoked more brain activity than predictable sequences, consistent with monitoring for, and updating from, prediction errors. These results suggest that the social brain automatically predicts others' future mental states.

The neural representation of social networks.

The computational demands associated with navigating large, complexly bonded social groups are thought to have significantly shaped human brain evolution. Yet, research on social network representation and cognitive neuroscience have progressed largely independently. Thus, little is known about how the human brain encodes the structure of the social networks in which it is embedded. This review highlights recent work seeking to bridge this gap in understanding. While the majority of research linking social network analysis and neuroimaging has focused on relating neuroanatomy to social network size, researchers have begun to define the neural architecture that encodes social network structure, cognitive and behavioral consequences of encoding this information, and individual differences in how people represent the structure of their social world.