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 counterintuitive effect. In Experiment 1, we instructed 1 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 3 groups: 1 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 2 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. (PsycINFO Database Record
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".