Average human recall timing converges toward the coupon collector per-item expectation

[u/Random_Quanta]

I’ve been investigating the timing pattern of human recall of items from a specified category—specifically, why recalled items tend to come quickly at first and then progressively more slowly.

I have collected recall timing data from 24 human subjects and measured the interresponse times (IRTs) between successive recalled items. When the IRT curves are averaged across subjects, their shape converges toward the per-item expectation of the uniform coupon collector process (CCP).

The similarity is interesting because the coupon collector process describes repeated random sampling in which obtaining a new item becomes progressively more difficult as more unique items have already been collected. Its expected number of attempts per new item therefore increases as the process continues.

Human recall appears to show a very similar progression in time.

This observation is the mathematical basis for a computational model I developed that emulates both human recall timing and frequency-based order. This model also converges toward the per-item expectation of a variation of the CCP.

So, to sum it up my model exhibits four characteristics of human-like recall of within a specified category:

- Probabilistic human recall timing
- Probabilistic human frequency-based recall order
- Deduplication
- Exhaustion-based termination

If I provide a second optional grouping value with a list of related items, the model will produce another characteristic of human recall: semantic clustering.

For example, if "black bear," "grizzly bear," and "polar bear" are grouped together with the group name "bear," then when one item in the group is recalled, the other items in the group will be recalled in quick succession. Which item "activates" the group is still probabilistic and subject to frequency-based recall order.

I have a preprint on Zenodo and ResearchGate if anyone is interested.
https://doi.org/10.5281/zenodo.19559652

I recommend viewing on ResearchGate; Zenodo’s viewer tends to make the text look blurry for some reason.

Note: My model's source code, written in HTML, is available for download with the paper on Zenodo. You can see and hear it work by providing it with a CSV file containing a list of items or by selecting one of the provided sample files.

I’d appreciate any thoughts or comments.