Can ‘Hebb’ Be Distracted? Testing the Susceptibility of Sequence Learning to Auditory Distraction
Bibliographic record
Abstract
Sequence learning plays a key role in many daily activities such as language and skills acquisition. The present study sought to assess the nature of the Hebb repetition effect-the enhanced serial recall for a repeated sequence of items compared to random sequences-by examining the vulnerability of this classical sequence-learning phenomenon to auditory distraction. Sound can cause unwanted distraction by either interfering specifically with the processes involved in the focal task (interference-by-process), or by diverting attention away from a focal task (attentional capture). Participants were asked to perform visual serial recall, in which one to-be-remembered sequence was repeated every four trials, while ignoring irrelevant sound. Whereas both changing-state (Experiment 1) and deviant sounds (Experiment 2) disrupted recall performance compared to steady-state sounds, performance for the repeated sequence increased across repetitions at the same rate regardless of the sound condition. Such findings suggest that Hebbian sequence learning is impervious to environmental interference, which provides further evidence that the Hebb repetition effect is an analogue of word-form learning.
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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 | 0.005 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".