The Impact of Deafness on the Use of Information During Facial Emotion Discrimination
Bibliographic record
Abstract
The integration of visual and auditory cues facilitates the detection of emotions in our interactions with others. Sensory deprivation such as deafness can be compensated through sign language, lip reading and oral communication. The diagnostic features for the recognition of emotions expressed facially in relation to the preferred means of communication and other factors of deafness (e.g. CI, cochlear implant) still remain unknown. In the present study, we used the Bubbles technique (Gosselin & Schyns, 2001) to examine whether severe-to-profound bilateral deafness leads to visual differences in decoding specific facial information. Twenty-two deaf individuals (including oral deaf, signers, and CI users) matched in age and sex with 22 hearing controls completed 3,072 trials of a fear versus happy discrimination task. On each trial, a face image was randomly selected from a set of four (two happy), mirror-reversed with a probability of 0.5 and sampled using randomly located Gaussian apertures at five scales (see Adolphs et al., 2005). The number of Gaussian apertures was adjusted on each trial to maintain a 75% correct rate. Multiple linear regressions were performed on the location of the Gaussian apertures and accuracy score for each participant. The individual regression coefficient planes were combined within subject groups (oral, signers, CI users, controls). Finally, to reveal group differences in information use for the task at hand, we computed all pairwise contrasts between these group regression coefficient planes and we applied a statistical threshold (Chauvin et al., 2005). The deaf participants using sign language differed from the other groups by using mostly the eye region to discriminate emotions. Similar patterns with the mouth as the salient area were found for hearing controls, CI users and the oral deaf. Facial information processing is therefore influenced by auditory experience and the visual strategies used in communication.
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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 | 0.004 |
| 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) | 0.002 | 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".