Unpacking the sandwich: Which mechanisms underlie the increase in sandwich priming during word recognition?
Bibliographic record
Abstract
Lupker and Davis (2009) introduced a modification of Forster and Davis's (1984) masked priming technique that increased the size of priming effects. The modification involved briefly presenting the target as a preprime during the priming sequence (e.g., #####-JUDGE-judge-JUDGE; the "sandwich" method). At present, the precise mechanisms underlying this increase are not well understood, at least partially because most previous experiments comparing the two procedures involved between-subject comparisons. To examine these mechanisms more fully, we conducted three lexical decision experiments with sandwich and conventional priming methods using a within-subject design. We examined two types of form-related priming: letter transpositions (Experiment 1) and letter replacements (Experiments 2 and 3). Results showed an increase in masked priming effects with the sandwich method in all three experiments. Cross-method comparisons revealed the source of this increase: The sandwich technique sped up the responses to transposed-letter pairs and one-letter replacement letter pairs, produced no latency differences for double replacement-letter pairs, and slowed down responses to unrelated pairs. Experiment 3, using a control preprime (xxxxx), showed that the change in the nature of the priming effects was not simply due to the longer lag between the pattern mask and the target stimulus in the sandwich priming method. These findings pose problems for computational activation-based models that provide accounts of masked priming effects. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".