How the deployment of visual attention modulates auditory distraction
Bibliographic record
Abstract
Classically, attentional selectivity has been conceptualized as a passive by-product of capacity limits on stimulus processing. Here, we examine the role of more active cognitive control processes in attentional selectivity, focusing on how distraction from task-irrelevant sound is modulated by levels of task engagement in a visually presented short-term memory task. Task engagement was varied by manipulating the load involved in the encoding of the (visually presented) to-be-remembered items. Using a list of Navon letters (where a large letter is composed of smaller, different-identity letters), participants were oriented to attend and serially recall the list of large letters (low encoding load) or to attend and serially recall the list of small letters (high encoding load). Attentional capture by a single deviant noise burst within a task-irrelevant tone sequence (the deviation effect) was eliminated under high encoding load (Experiment 1). However, distraction from a continuously changing sequence of tones (the changing-state effect) was immune to the influence of load (Experiment 2). This dissociation in the amenability of the deviation effect and the changing-state effect to cognitive control supports a duplex-mechanism over a unitary-mechanism account of auditory distraction in which the deviation effect is due to attentional capture whereas the changing-state effect reflects direct interference between the processing of the sound and processes involved in the focal task. That the changing-state effect survives high encoding load also goes against an alternative explanation of the attenuation of the deviation effect under high load in terms of the depletion of a limited perceptual resource that would result in diminished auditory processing.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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".