Remembering 'primed' words: The effect of prime encoding demands
Bibliographic record
Abstract
Rosner, Lopez-Benitez, D’Angelo, Thomson, and Milliken (2017) reported a novel recognition memory effect using an immediate repetition method during the study phase. During each trial of an incidental study phase, participants named a target word that followed a prime word that had the same identity (repeated trials) or a different identity (not-repeated trials). Recognition in the following test phase was better for the not-repeated trials. In the present study, we examined the influence of prime encoding demands on this counter-intuitive effect. In Experiment 1, we instructed one group to simply ignore the prime, as in the original study. A second group completed a divided attention task on prime presentation. Recognition memory was better for not-repeated than repeated words in both groups. In Experiment 2, encoding of the prime varied across three groups: one group named each prime, a second group counted the vowels in each prime, and a third group made a semantic discrimination for each prime. Recognition was better for repeated than for not-repeated words in the semantic group and did not differ across conditions for the other two groups. Finally, in Experiment 3, we assessed memory for not-repeated primes in addition to memory for targets (as in Experiments 1 and 2). The results confirmed that poor memory for the primes plays a significant role in producing the previously described effects. The results are discussed in relation to transient processing adaptations that affect memory encoding.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".