Decomposing Predictive Information in Social Dynamics

Social behaviors include some of the most interesting interactions in living systems yet their characterization is often qualitative or specialized to particular organisms and assays. Here we suggest that at the core of social interactions is the notion of mutual prediction, which we analyze in the context of two male zebrafish engaged in a dominance contest. Using 3D velocity trajectories, we construct the mutual information between a two-animal past and one-animal future, and we quantify the redundant, unique, and synergistic components using partial information decomposition (PID). As social behaviors can change rapidly in time, we compute PID using a sliding time window, and we choose the window size to maximize total information. We find that our predictive information decomposition naturally aligns with important social concepts, such as mirroring and dominance. At the end of the contest, we find asymmetries in self-unique and redundant information which align with the emergent dominance relationship. During the contest, we find that redundant information increases, showing that predictive information is increasingly shared between individuals. In contrast synergistic and unique information, which capture information exchange, are approximately constant. Applied to mecp2 zebrafish mutants, an autism model, we find that predictive information is reduced overall, but especially for synergistic flows, which is indicative of difficulties in more complex social dynamics.