Perception of speaker sincerity in complex social interactions by cochlear implant users
Bibliographic record
Abstract
Understanding insincere language (sarcasm and teasing) is a fundamental part of communication and crucial for maintaining social relationships. This can be a challenging task for cochlear implant (CIs) users who receive degraded suprasegmental information important for perceiving a speaker's attitude. We measured the perception of speaker sincerity (literal positive, literal negative, sarcasm, and teasing) in 16 adults with CIs using an established video inventory. Participants were presented with audio-only and audio-visual social interactions between two people with and without supporting verbal context. They were instructed to describe the content of the conversation and answer whether the speakers meant what they said. Results showed that subjects could not always identify speaker sincerity, even when the content of the conversation was perfectly understood. This deficit was greater for perceiving insincere relative to sincere utterances. Performance improved when additional visual cues or verbal context cues were provided. Subjects who were better at perceiving the content of the interactions in the audio-only condition benefited more from having additional visual cues for judging the speaker's sincerity, suggesting that the two modalities compete for cognitive recourses. Perception of content also did not correlate with perception of speaker sincerity, suggesting that what was said vs. how it was said were perceived using unrelated segmental versus suprasegmental cues. Our results further showed that subjects who had access to lower-order resolved harmonic information provided by hearing aids in the contralateral ear identified speaker sincerity better than those who used implants alone. These results suggest that measuring speech recognition alone in CI users does not fully describe the outcome. Our findings stress the importance of measuring social communication functions in people with CIs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".