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Open Access Publications from the University of California

A computational model for simulating the future using a memory timeline


The ability to learn temporal relationships and use that knowledge to simulate future events is among the most remarkable aspects of cognition. Recently introduced behavioral task called Judgment of Imminence (JOI) combined with a well-known Judgment of Recency (JOR) task pointed to a remarkable symmetry between the temporal organization of memory and prediction. The data were consistent with the hypothesis that both memory and prediction can be organized as a compressed mental timeline. This means that the past and future can be remembered or simulated sequentially relative to the present. The compression implies that events closer to the present, regardless of whether they are in the past or in the future, were represented more accurately than those further from the present. Here we used the existing JOR model based on a compressed memory timeline to build an associative representation that can learn the temporal relationships and create a timeline of the future, which mirrors the timeline of the past. We show that this approach can simultaneously account for response times and accuracy in both JOR and JOI. This work provides a time-local neural-level mechanistic account for how the temporal organization of the memory can be used to learn the temporal structure of the world and simulate the future in an efficient manner as a compressed mental timeline.

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