Different Neural Networks Are Involved in Audiovisual Speech Perception Depending on the Context
Bibliographic record
Abstract
How are we able to easily and accurately recognize speech sounds despite the lack of acoustic invariance? One proposed solution is the existence of a neural representation of speech syllable perception that transcends its sensory properties. In the present fMRI study, we used two different audiovisual speech contexts both intended to identify brain areas whose levels of activation would be conditioned by the speech percept independent from its sensory source information. We exploited McGurk audiovisual fusion to obtain short oddball sequences of syllables that were either (a) acoustically different but perceived as similar or (b) acoustically identical but perceived as different. We reasoned that, if there is a single network of brain areas representing abstract speech perception, this network would show a reduction of activity when presented with syllables that are acoustically different but perceived as similar and an increase in activity when presented with syllables that are acoustically similar but perceived as distinct. Consistent with the long-standing idea that speech production areas may be involved in speech perception, we found that frontal areas were part of the neural network that showed reduced activity for sequences of perceptually similar syllables. Another network was revealed, however, when focusing on areas that exhibited increased activity for perceptually different but acoustically identical syllables. This alternative network included auditory areas but no left frontal activations. In addition, our findings point to the importance of subcortical structures much less often considered when addressing issues pertaining to perceptual representations.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".