Bibliographic record
Abstract
Multimodal perception has been previously investigated using simple stimuli such as pure tones and geometric forms or light flashes. It is generally found that presentation of bimodal stimuli improves behavioural performance, whether in detection, localisation or identification tasks. However, little is known about the multimodal integration of biologically relevant stimuli such as faces and voices. The purpose of this study was to determine the time-course of face and voice perception, depending on the attended modality. Nineteen subjects performed a gender categorisation on congruent or incongruent bimodal stimuli with attention directed to one or the other modality, i.e. to the faces or to the voices. ERPs were recorded concurrently. Behavioural data showed that gender categorisation was faster for faces than voices. Incongruent information in the unattended modality decreased accuracy and prolonged RTs compared to the congruent condition. ERPs were dominated by the response to the faces. Brain topography analyses showed a larger activity when attention was directed to faces around 100 ms after stimulus onset, equivalent to the P1 latency. However, the face-specific ERP component, N170, was not sensitive to the direction of attention. These data showed that directing attention to a particular sensory modality modulates early processing of visuo-auditory information, but not the N170. Further analyses will clarify the way in which attention affects the processing of bimodal stimuli. It is currently debated whether the N170 is sensitive to top-down effects or not. Based on these results, we suggest that neural mechanisms underlying the N170 are automatically recruited by faces.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | medium |
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".