Re-evaluating Dissociations between Implicit and Explicit Category Learning: An Event-related fMRI Study
Bibliographic record
Abstract
Recent fMRI studies have found that distinct neural systems may mediate perceptual category learning under implicit and explicit learning conditions. In these previous studies, however, different stimulus-encoding processes may have been associated with implicit versus explicit learning. The present design was aimed at decoupling the influence of these factors on the recruitment of alternate neural systems. Consistent with previous reports, following incidental learning in a dot-pattern classification task, participants showed decreased neural activity in occipital visual cortex (extrastriate region V3, BA 19) in response to novel exemplars of a studied category compared to members of a foil category, but did not show this decreased neural activity following explicit learning. Crucially, however, our results show that this pattern was primarily modulated by aspects of the stimulus-encoding instructions provided at the time of study. In particular, when participants in an implicit learning condition were encouraged to evaluate the overall shape and configuration of the stimuli during study, we failed to find the pattern of brain activity that has been taken to be a signature of implicit learning, suggesting that activity in this area does not uniquely reflect implicit memory for perceptual categories but instead may reflect aspects of processing or perceptual encoding strategies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".