Psychophysiological Markers of Auditory Distraction: A Scoping Review
Bibliographic record
Abstract
Short-term memory can be disrupted by task-irrelevant sound.Auditory distraction has been globally studied under the lens of two main phenomena: the deviation effect and the changing-state effect.Yet, it remains unclear whether they rely on common cerebral mechanisms and, concomitantly, what psychophysiological responses they can trigger.This scoping review provides a state of knowledge regarding psychophysiological indices of auditory distraction.Records published between 2001 and 2021 on the deviation effect and the changing-state effect with psychophysiological measures were extracted from PubMed, ERIC, PsycNet, Web of Science, and ScienceDirect.Records investigating task-relevant sounds, as well as those that failed to observe performance disruption, or to include a control condition or a concurrent cognitive task, were excluded from the review.The Revised Cochrane risk-ofbias tool for randomized trials was used for bias evaluation.Fifteen records were reviewed, mainly characterized by randomization, measurement and selection of results biases.Some markers were specific to the distraction type, but nonspecific responses were also found.Overall, we outline the main markers used to index auditory distraction, present their meaning for understanding underpinning mechanisms, and discuss implications and knowledge gaps that need to be filled to fully exploit psychophysiology for auditory distraction research.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".