Computational Phenotyping of Two-Person Interactions Reveals Differential Neural Response to Depth-of-Thought

Reciprocating exchange with other humans requires individuals to infer the intentions of their partners. Despite the importance of this ability in healthy cognition and its impact in disease, the dimensions employed and computations involved in such inferences are not clear. We used a computational theory-of-mind model to classify styles of interaction in 195 pairs of subjects playing a multi-round economic exchange game. This classification produces an estimate of a subject's depth-of-thought in the game (low, medium, high), a parameter that governs the richness of the models they build of their partner. Subjects in each category showed distinct neural correlates of learning signals associated with different depths-of-thought. The model also detected differences in depth-of-thought between two groups of healthy subjects: one playing patients with psychiatric disease and the other playing healthy controls. The neural response categories identified by this computational characterization of theory-of-mind may yield objective biomarkers useful in the identification and characterization of pathologies that perturb the capacity to model and interact with other humans. Author Summary: Human social interactions are extraordinarily rich and complex. The ability to infer the intentions of others is essential for successful social interactions. Although most of our inferences about others are silent and subtle, traces of their effects can be found in the behavior we exhibit in various tasks, notably repeated economic exchange games. In this study, we use a computational model that uses an explicit form of other-modeling to classify styles of play in a large cohort of subjects engaging in such a game. We classify players according to their depth of recursive reasoning (depth-of-thought), finding three groups whose performance throughout the task differed according to several measures. Neuroimaging results based on the model classification show a differential neural response to depth-of-thought. The model also detected differences in depth-of-thought between two groups of healthy subjects: one playing patients with psychiatric disease and the other playing healthy controls. These results demonstrate the power of a quantitative approach to examining behavioral heterogeneity during social exchange, and may provide useful biomarkers to characterize mental disorders when the capacity to make inferences about others is impaired.