Bibliographic record
Abstract
Since the discovery that amnesiacs retained certain forms of unconscious learning and memory, implicit memory research has grown immensely over the past several decades. This review discusses two of the most intriguing questions in implicit memory research: how we think it works and why it is important to human behaviour. Using priming as an example, this paper surveys how historic behavioural studies have revealed how implicit memory differs from explicit memory. More recent neuroimaging studies are also discussed. These studies explore the neural correlate of priming and have led to the formation of the sharpening, fatigue, and facilitation neural models of priming. The latter part of this review discusses why humans have evolved with highly flexible explicit memory systems while still retaining inflexible implicit memory systems. Traditionally, researchers have regarded the competitive interaction between implicit and explicit memory systems to be fundamental for essential behaviours like habit learning. However, recently documented collaborative interactions suggest that both implicit and explicit processes are potentially necessary for optimal memory and learning performance. The literature reviewed in this paper reveals how implicit memory research has and continues to challenge our understanding of learning, memory, and the brain systems that underlie them.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.015 |
| Scholarly communication | 0.006 | 0.016 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".