Orienting attention to sound object representations attenuates change deafness.
Bibliographic record
Abstract
According to the object-based account of attention, multiple objects coexist in short-term memory (STM), and we can selectively attend to a particular object of interest. Although there is evidence that attention can be directed to visual object representations, the assumption that attention can be oriented to sound object representations has yet to be validated. Here, we used a delayed match-to-sample task to examine whether orienting attention to sound object representations influences change detection within auditory scenes consisting of 3 concurrent sounds, each occurring at a different location. On some trials, the 2 scenes were identical; in the remaining trials, the locations of 2 sounds were switched. In a control experiment, we first identified auditory scenes, in which the 3 sounds were unambiguously segregated, for the subsequent experiments. In 2 experiments, we showed that orienting attention to a sound object representation during memory retention (via a retro-cue) enhanced performance relative to uncued trials, up to 4 s of memory retention. Our study shows that complex auditory scenes composed of cooccurring sound sources are quickly parsed into sound object representations--which are then available for top-down selective attention. Here, we demonstrate that attention can be guided toward 1 of those representations, thereby attenuating change deafness. Furthermore, the effects of retro-cues in audition extend analogous findings in the visual domain, thereby suggesting that orienting attention to an object within visual or auditory STM may follow similar processing principles.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".