Bibliographic record
Abstract
If some list items are studied strongly and others weakly, many memory models predict the effect of strength on memory will be larger when strengths are mixed within a list than between pure lists of a single strength: a list-strength effect. In explaining why list-strength effects were elusive in old/new recognition, Shiffrin et al. (1990) introduced differentiation. This gave the model a way to produce an inverted list-strength effect, which they thought was usually offset by the coexisting expected "upright" list-strength effect. Alternatively, attentional subsetting theory (Caplan, 2023; Caplan & Guitard, 2024b) predicted inverted list-strength effects in some circumstances by considering how the dimensionalities of attended feature spaces might differ for strong and weak items. Inversions were indeed found in manipulations of stimulus duration (e.g., 500 ms vs. 2,000 ms study time/word). Here we replicated the pattern when display time was equated (Experiment 1) and with massed repetition (Experiment 2), ruling out the relevance of vision-locked features and the number of stimulus onsets. Both theoretical accounts of inverted list-strength effects, however, miss the fine structure of the data, namely, reduced hit rates for weak items in pure than mixed lists and the reverse effect (albeit less robust) for strong items. Model fits suggested the critical factor is that list composition parametrically influences the number of deep features processed at test combined with participants' response bias adapting to list composition. In sum, inverted list-strength effects are robustly found in manipulations of item study time and point to differential processing of probe features depending on list composition, compatible with most models. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".