Bibliographic record
Abstract
The effects of attention on the neural processes underlying auditory scene analysis were investigated through the manipulation of auditory task load. Participants were asked to focus their attention on tuned and mistuned stimuli presented to one ear and to ignore similar stimuli presented to the other ear. For both tuned and mistuned sounds, long (standard) and shorter (deviant) duration stimuli were presented in both ears. Auditory task load was manipulated by varying task instructions. In the easier condition, participants were asked to press a button for deviant sounds (target) at the attended location, irrespective of tuning. In the harder condition, participants were further asked to identify whether the targets were tuned or mistuned. Participants were faster in detecting targets defined by duration only than by both duration and tuning. At the unattended location, deviant stimuli generated a mismatch negativity wave at frontocentral sites whose amplitude decreased with increasing task demand. In comparison, standard mistuned stimuli generated an object-related negativity at central sites whose amplitude was not affected by task difficulty. These results show that the processing of sound sequences is differentially affected by attentional load than is the processing of sounds that occur simultaneously (i.e., sequential vs. simultaneous grouping processes), and that they each recruit distinct neural networks.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".