The resilience of verbal sequence learning: Evidence from the Hebb repetition effect.
Bibliographic record
Abstract
In a single large-scale study, we demonstrate that verbal sequence learning as studied using the classic Hebb repetition effect (Hebb, 1961)-the improvement in the serial recall of a repeating sequence compared to nonrepeated sequences-is resilient to both wide and irregular spacing between sequence repetitions. Learning of a repeated sequence of letters was evident to a comparable degree with three, five, and eight intervening nonrepeated sequences and regardless of whether the spacing between repetitions was regular or irregular. Importantly, this resilience of verbal sequence learning was observed despite complete item-set overlap between repeated and nonrepeated sequences. The findings are consistent with the conceptualization of the Hebb repetition effect as a laboratory analogue of natural phonological word-form learning. The results also have implications for the two leading models of Hebb sequence learning: Whereas the results are incompatible with the model of Page and Norris (2009), they can be handled readily by the model of Burgess and Hitch (2006) through the abandonment of its assumption of long-term (across-trial level) decay. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".