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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. Here we explore whether there are asymmetries in inferences about the unobserved pasts and futures of other people’s lives. In two experiments (analyses of the replication experiment were pre-registered), our participants view segments of two character-driven television dramas and write out what they think happens just before or after each just-watched segment. Participants are better at inferring unseen past (versus future) events. This asymmetry is driven by participants’ reliance on characters’ conversational references in the narrative, which tend to favor the past. This tendency is also replicated in a large-scale analysis of conversational references in natural conversations. Our work reveals a temporal asymmetry in how observations of other people’s behaviors can inform inferences about the past and future.
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. Here we explore whether there are asymmetries in inferences about the unobserved pasts and futures of other people’s lives. In two experiments (analyses of the replication experiment were pre-registered), our participants view segments of two character-driven television dramas and write out what they think happens just before or after each just-watched segment. Participants are better at inferring unseen past (versus future) events. This asymmetry is driven by participants’ reliance on characters’ conversational references in the narrative, which tend to favor the past. This tendency is also replicated in a large-scale analysis of conversational references in natural conversations. Our work reveals a temporal asymmetry in how observations of other people’s behaviors can inform inferences about the past and future.
While recounting an experience, one could employ multiple strategies to transition from one part to the next. For instance, if the event was learned out of linear order, one could recall events according to the time they were learned (temporal), similar events (semantic), events occurring nearby in time (chronological), or events produced by the current event (causal). To disentangle the importance of these factors, we had participants watch the nonlinear narrative, “Memento,” under different task instructions and presentation orders. For each scene of the film, we also separately computed semantic and causal networks. From these derivations, we contrasted the evidence for temporal, semantic, chronological, or causal strategies during recall. Critically, there was stronger evidence for the causal and chronological strategies than semantic or temporal strategies. Moreover, the causal and chronological strategies outperformed the temporal one even after asking participants to recall the film in the presented order, underscoring the fundamental nature of causal structure in scaffolding understanding and organizing recall. Nevertheless, time still marginally predicted recall transitions, suggesting it still operates as a weak signal in the presence of more salient forms of structure. In addition, semantic and causal network properties predicted scene memorability, including showing a stronger role for causes of an event than its outgoing effects. In summary, these effects highlight the importance of accounting for complex, causal networks in knowledge building and memory.
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