Attentional capture by deviant sounds: A noncontingent form of auditory distraction?
Bibliographic record
Abstract
The occurrence of an unexpected, infrequent sound in an otherwise homogeneous auditory background tends to disrupt the ongoing cognitive task. This "deviation effect" is typically explained in terms of attentional capture whereby the deviant sound draws attention away from the focal activity, regardless of the nature of this activity. Yet, there is theoretical and empirical evidence suggesting that the attention-capture mechanism underlying this form of distraction could rather be triggered in a task-contingent fashion. The present study aimed at determining whether the auditory deviation effect reflects the action of either a stimulus-driven or a task-contingent orienting mechanism. To do so, we conducted a systematic investigation whereby the impact of verbal deviants-a letter embedded in the repetition of another letter-and spatial deviants-a sound presented contralaterally to the other sounds-on verbal and spatial short-term memory (STM) was assessed. This study established that both verbal and spatial deviants can hinder both verbal and spatial order-reconstruction (Experiment 1) and missing-item tasks (Experiment 2). Such results demonstrate that the deviation effect reflects a general form of auditory distraction as interference took place both within and across domains and regardless of the processes engaged in the focal task. (PsycINFO Database Record
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".