“Love looks not with the eyes”: supranormal processing of emotional speech in individuals with late-blindness versus preserved processing in individuals with congenital-blindness
Bibliographic record
Abstract
Processing of emotional speech in the absence of visual information relies on two auditory channels: semantics and prosody. No study to date has investigated how blindness impacts this process. Two theories, Perceptual Deficit, and Sensory Compensation, yiled different expectations about the role of visual experience (or its lack thereof) in processing emotional speech. To test the effect of vision and early visual experience on processing of emotional speech, we compared individuals with congenital blindness (CB, n = 17), individuals with late blindness (LB, n = 15), and sighted controls (SC, n = 21) on identification and selective-attention of semantic and prosodic spoken-emotions. Results showed that individuals with blindness performed at least as well as SC, supporting Sensory Compensation and the role of cortical reorganisation. Individuals with LB outperformed individuals with CB, in accordance with Perceptual Deficit, supporting the role of early visual experience. The LB advantage was moderated by executive functions (working-memory). Namely, the advantage was erased for individuals with CB who showed higher levels of executive functions. Results suggest that vision is not necessary for processing of emotional speech, but early visual experience could improve it. The findings support a combination of the two aforementioned theories and reject a dichotomous view of deficiencies/enhancements of blindness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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, 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".