A Case of Impaired Auditory and Visual Speech Prosody Perception after Right Hemisphere Damage
Bibliographic record
Abstract
It is well established that vision plays a role in segmental speech perception, but the role of vision in prosodic speech perception is less clear. We report on the difficulties in prosodic speech perception encountered by KB after a right hemisphere stroke. In addition to musical deficits, KB was suspected of having impaired auditory prosody perception. As expected, KB was impaired on two prosody perception tasks in an auditory-only condition. We also examined whether the addition of visual prosody cues would facilitate his performance on these tasks. Unexpectedly, KB was also impaired on both tasks under visual-only and audio-visual conditions. Thus, there was no evidence that KB could integrate auditory and visual prosody information or that he could use visual cues to compensate for his deficit in the auditory domain. In contrast, KB was able to identify segmental speech information using visual cues and to use these visual cues to improve his performance when auditory segmental cues were impoverished. KB was also able to integrate audio-visual segmental information in the McGurk effect. Thus, KB's visual deficit was specific to prosodic speech perception and, to our knowledge, this is the first reported case of such a deficit.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".