Cognitive control of auditory distraction: Impact of task difficulty, foreknowledge, and working memory capacity supports duplex-mechanism account.
Bibliographic record
Abstract
The influence of top-down cognitive control on 2 putatively distinct forms of distraction was investigated. Attentional capture by a task-irrelevant auditory deviation (e.g., a female-spoken token following a sequence of male-spoken tokens)-as indexed by its disruption of a visually presented recall task-was abolished when focal-task engagement was promoted either by increasing the difficulty of encoding the visual to-be-remembered stimuli (by reducing their perceptual discriminability; Experiments 1 and 2) or by providing foreknowledge of an imminent deviation (Experiment 2). In contrast, distraction from continuously changing auditory stimuli ("changing-state effect") was not modulated by task-difficulty or foreknowledge (Experiment 3). We also confirmed that individual differences in working memory capacity--typically associated with maintaining task-engagement in the face of distraction--predict the magnitude of the deviation effect, but not the changing-state effect. This convergence of experimental and psychometric data strongly supports a duplex-mechanism account of auditory distraction: Auditory attentional capture (deviation effect) is open to top-down cognitive control, whereas auditory distraction caused by direct conflict between the sound and focal-task processing (changing-state effect) is relatively immune to such control.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".