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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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Caplan et al. (2024) studied this question.
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