Sounds Activate Visual Cortex and Improve Visual Discrimination
Bibliographic record
Abstract
A recent study in humans (McDonald et al., 2013) found that peripheral, task-irrelevant sounds activated contralateral visual cortex automatically as revealed by an auditory-evoked contralateral occipital positivity (ACOP) recorded from the scalp. The present study investigated the functional significance of this cross-modal activation of visual cortex, in particular whether the sound-evoked ACOP is predictive of improved perceptual processing of a subsequent visual target. A trial-by-trial analysis showed that the ACOP amplitude was markedly larger preceding correct than incorrect pattern discriminations of visual targets that were colocalized with the preceding sound. Dipole modeling of the scalp topography of the ACOP localized its neural generators to the ventrolateral extrastriate visual cortex. These results provide direct evidence that the cross-modal activation of contralateral visual cortex by a spatially nonpredictive but salient sound facilitates the discriminative processing of a subsequent visual target event at the location of the sound. Recordings of event-related potentials to the targets support the hypothesis that the ACOP is a neural consequence of the automatic orienting of visual attention to the location of the sound.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".