The ubiquitous nature of the Hebb repetition effect: Error learning mistaken for the absence of sequence learning.
Bibliographic record
Abstract
Sequence learning is essential in cognition and underpins activities such as language and skill acquisition. One classical demonstration of sequence learning is that of the Hebb repetition effect, whereby serial recall improves over repetitions on a repeated list relative to random lists. When addressing the question of which mechanism underlies the effect, the traditional approach is to prevent the action of processes thought to be responsible for sequence learning: If the typical Hebb repetition effect is reduced, these processes are key to the effect, researchers claim. By reanalyzing the data of F. B. R. Parmentier, M. T. Maybery, M. Huitson, and D. M. Jones (2008)-who reported no Hebb effect for sequences of auditory-spatial stimuli-we revealed that error learning can be mistaken for the absence of sequence learning. Indeed, incorrect responses are reproduced increasingly over repetitions. Our findings suggest that the Hebb repetition effect can be associated with response learning as well as stimulus processing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".