Recallable but not Recognizable: The Influence of Semantic Priming in Recall Paradigms
Bibliographic record
Abstract
Abstract When people can successfully recall a studied word, they should be able to recognize it as having been studied. In cued recall paradigms, however, participants sometimes correctly recall words in the presence of strong semantic cues but then fail to recognize those words as actually having been studied. Although the conditions necessary to produce this unusual effect are known, the underlying neural correlates have not been investigated. Across two experiments, involving both behavioral and electrophysiological methods (EEG), we investigated the cognitive and neural processes that underlie recognition failures. Experiments 1A and 1B showed that, in cued recall paradigms, presuming that recalled items can be recognized is a flawed assumption: Recognition failures occur in the presence of cues, regardless of whether those failures are measured. Experiment 2 showed that successfully recalled words that are recognized are driven by recollection at recall and by a combination of recollection and familiarity at recognition; in contrast, recognition failures are driven by semantic priming at recall and followed by negative-going ERP effects consistent with implicit processes such as repetition fluency and context familiarity at recognition. These results demonstrate that recall—long-characterized as predominantly reflecting recollection-based processing in episodic memory—can at times also be served by a confluence of implicit cognitive processes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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) | 0.001 | 0.000 |
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".