Learning multiple lists at the same time in the Hebb repetition effect.
Bibliographic record
Abstract
In short-term ordered recall, when 1 list of items is repeated over the course of the experiment, recall performance typically improves. This is known as the Hebb repetition effect (Hebb, 1961). In the present study, we contrasted the typical condition involving a single repeated sequence with concurrent learning of 2 repeated sequences. Participants performed a spatial recall task, in which sequences of 7 dots were presented in each trial. For a given participant, the location of the dots were the same for all trials. Presentation order of dots varied randomly, except for 1 or 2 particular series that were repeated every 4 trials. Results revealed a significant learning slope for both the single and dual list conditions and learning was as efficient in both conditions. The findings provide further evidence in support of models linking the Hebb repetition effect to word-form learning.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".