Stimulus duration and recognition memory: An attentional subsetting account
Bibliographic record
Abstract
Attentional subsetting theory (Caplan, 2023) posits that only a small subset of item features are attended in episodic recognition tasks. This explained a pivotal finding for the development of recognition models: the near-null list-strength effect, where encoding strength influences recognition similarly in mixed-strength lists and pure-strength lists. Most research uses spaced repetition to manipulate encoding strength. However, the origin of the null list-strength effect was a more unusual manipulation of stimulus duration (1 s versus 2 s) — and reported an inverted list-strength effect. We present an attentional subsetting theory of duration that produces inversions — and explains why they are uncommon: Earlier-attended features dwell within a lower-dimensional feature subspace, which participants can sometimes disregard during test trials of pure-strong lists, giving strong-pure items an extra advantage. The model previously only solved for d ′ . We extend it to generate realistic hit and false-alarm rates by deriving the criterion from attention to each probe. Supporting the theory, two pre-registered experimental manipulations of stimulus-duration reproduced robust inverted list-strength effects, suggesting this type of finding is unlikely due to sampling error. This account of stimulus-duration, explaining inverted, as well as upright and null, list-strength effects, could be incorporated in most models with vector representations • The theory: participants attend a small, idiosyncratic subset of an items’ features. • Early versus later attended features may differ in dimensionality. • This explains how long study times can have more advantage in different lists. • Two experiments support this prediction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".