Brain Network Involved in the Recognition of Facial Expressions of Emotion in the Early Blind
Bibliographic record
Abstract
Previous studies suggest that the brain network responsible for the recognition of facial expressions of emotion (FEEs) begins to emerge early in life. However, it has been unclear whether visual experience of faces is necessary for the development of this network. Here, we conducted both psychophysical and functional magnetic-resonance imaging (fMRI) experiments to test the hypothesis that the brain network underlying the recognition of FEEs is not dependent on visual experience of faces. Early-blind, late-blind and sighted subjects participated in the psychophysical experiment. Regardless of group, subjects haptically identified basic FEEs at above-chance levels, without any feedback training. In the subsequent fMRI experiment, the early-blind and sighted subjects haptically identified facemasks portraying three different FEEs and casts of three different shoe types. The sighted subjects also completed a visual task that compared the same stimuli. Within the brain regions activated by the visually-identified FEEs (relative to shoes), haptic identification of FEEs (relative to shoes) by the early-blind and sighted individuals activated the posterior middle temporal gyrus adjacent to the superior temporal sulcus, the inferior frontal gyrus, and the fusiform gyrus. Collectively, these results suggest that the brain network responsible for FEE recognition can develop without any visual experience of faces.
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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 | 0.002 |
| 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 | 0.001 |
| 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".