A Technique for Emulating Human Recall Timing in Artificial Intelligence

Human recall of items in a specified category (for example, breeds of dogs) follows a characteristic pattern in which early responses tend to occur quickly, whereas later responses occur more slowly. In contrast, artificial systems typically produce items with relatively uniform latency. This paper proposes a computational model for reproducing human recall timing patterns in artificial systems. In this model, as more items are recalled, the probability that a randomly sampled item is a duplicate increases. As a result, a growing proportion of samples are rejected, requiring additional attempts before a new item is produced. This increasing rejection rate naturally produces progressively longer interresponse times as recall progresses. When averaged across many runs, the model’s per-item attempts curve closely resembles the interresponse time curve observed in human recall and shows near-perfect convergence with the classic coupon collector per-item expectation. Although formal human-subject testing is still needed, preliminary observations suggest that human recall timing is consistent with mathematical expectations predicted by the coupon collector model, indicating potential applications in cognitive diagnostics, cognitive modeling, and artificial intelligence. V32: This version includes a comprehensive rewrite to improve clarity and readability, with simplified explanations of core concepts that do not alter the underlying model or its claims. The overall structure and flow across sections have been improved, and terminology has been refined for consistency. No substantive changes have been made to the theoretical framework or results.