A linear mixed model analysis of masked repetition priming
Bibliographic record
Abstract
We examined individual differences in masked repetition priming by re-analysing item-level response-time (RT) data from three experiments. Using a linear mixed model (LMM) with subjects and items specified as crossed random factors, the originally reported priming and word-frequency effects were recovered. In the same LMM, we estimated parameters describing the distributions of these effects across subjects. Subjects' frequency and priming effects correlated positively with each other and negatively with mean RT. These correlation estimates, however, emerged only with a reciprocal transformation of RT (i.e., − 1/RT), justified on the basis of distributional analyses. Different correlations, some with opposite sign, were obtained (1) for untransformed or logarithmic RTs or (2) when correlations were computed using within-subject analyses. We discuss the relevance of the new results for accounts of masked priming, implications of applying RT transformations, and the use of LMMs as a tool for the joint analysis of experimental effects and associated individual differences.
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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 | 0.022 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".