How much can we infer about the past and future, given our knowledge of the present? Unlike temporally symmetric inferences about simple sequences, inferences about our own lives are asymmetric: we are better able to infer the past than the future, since we remember our past but not our future (i.e., the psychological arrow of time). What happens when both the past and future are unobserved, as when we make inferences about other people’s lives? We had participants view segments of a character-driven television drama. They wrote out what would happen just before or after each just-watched segment. Participants were better at inferring past (versus future) events. This asymmetry was driven by participants’ reliance on characters’ conversational references in the narrative, which tended to favor the past. Our work reveals a temporal asymmetry in how observations of other people’s behaviors can inform us about the past and future.

Major depressive disorder is a common and disabling disorder with high rates of treatment resistance. Evidence suggests it is characterized by distributed network dysfunction that may be variable across patients, challenging the identification of quantitative biological substrates. We carried out this study to determine whether application of a novel computational approach to a large sample of high spatiotemporal resolution direct neural recordings in humans could unlock the functional organization and coordinated activity patterns of depression networks. This group level analysis of depression networks from heterogenous intracranial recordings was possible due to application of a correlational model-based method for inferring whole-brain neural activity. We then applied a network framework to discover brain dynamics across this model that could classify depression. We found a highly distributed pattern of neural activity and connectivity across cortical and subcortical structures that was present in the majority of depressed subjects. Furthermore, we found that this depression signature consisted of two subnetworks across individuals. The first was characterized by left temporal lobe hypoconnectivity and pathological beta activity. The second was characterized by a hypoactive, but hyperconnected left frontal cortex. These findings have applications toward personalization of therapy.