Developmental Perceptual Impairments: Cases When Tone-Deafness and Prosopagnosia Co-occur
Bibliographic record
Abstract
Studies have shown subtle gray and white matter abnormalities in subjects with several developmental disorders including prosopagnosia, tone-deafness, and dyslexia. Correlational evidence suggests that tone-deafness and dyslexia tend to co-occur, suggesting a link between these two developmental disorders. However, it is not known whether tone-deafness can also be associated with other developmental disorders such as impaired face recognition or prosopagnosia. We addressed this question by assessing face perception abilities in a group of tone-deaf individuals and matched non-tone-deaf subjects. The Cambridge (CFMT) and the Warrington (WRMT) face memory tests were used to assess face processing in the combined group of 12, out of which six tested in the tone-deaf range. Only tone-deaf participants (two out of six) scored in the impaired range on the CFMT, one of whom was also impaired on the WRMT face memory test. Furthermore, the melodic composite score of all participants on the Montreal Battery of Evaluation of Amusia significantly correlated with their face recognition score on the CFMT. Our results suggest that in some cases tone-deafness might co-occur with face recognition impairments. It is implausible that both deficits are linked to a single cognitive dysfunction that spans different perceptual systems in different modalities. They are likely associated with a common pathogenetic mechanism of early development that leads to anomalies affecting the function of different brain systems or the connection between regions.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".