Stimulus duration and recognition memory: An attentional subsetting account
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it