The co-occurrence of multisensory facilitation and cross-modal conflict in the human brain
Bibliographic record
Abstract
Perceptual objects often comprise a visual and auditory signature that arrives simultaneously through distinct sensory channels, and cross-modal features are linked by virtue of being attributed to a specific object. Continued exposure to cross-modal events sets up expectations about what a given object most likely "sounds" like, and vice versa, thereby facilitating object detection and recognition. The binding of familiar auditory and visual signatures is referred to as semantic, multisensory integration. Whereas integration of semantically related cross-modal features is behaviorally advantageous, situations of sensory dominance of one modality at the expense of another impair performance. In the present study, magnetoencephalography recordings of semantically related cross-modal and unimodal stimuli captured the spatiotemporal patterns underlying multisensory processing at multiple stages. At early stages, 100 ms after stimulus onset, posterior parietal brain regions responded preferentially to cross-modal stimuli irrespective of task instructions or the degree of semantic relatedness between the auditory and visual components. As participants were required to classify cross-modal stimuli into semantic categories, activity in superior temporal and posterior cingulate cortices increased between 200 and 400 ms. As task instructions changed to incorporate cross-modal conflict, a process whereby auditory and visual components of cross-modal stimuli were compared to estimate their degree of congruence, multisensory processes were captured in parahippocampal, dorsomedial, and orbitofrontal cortices 100 and 400 ms after stimulus onset. Our results suggest that multisensory facilitation is associated with posterior parietal activity as early as 100 ms after stimulus onset. However, as participants are required to evaluate cross-modal stimuli based on their semantic category or their degree of congruence, multisensory processes extend in cingulate, temporal, and prefrontal cortices.
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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) | 0.001 | 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".