Difficulty Identifying Feelings and Automatic Activation in the Fusiform Gyrus in Response to Facial Emotion
Bibliographic record
Abstract
Difficulties in identifying and differentiating one's emotions are a central characteristic of alexithymia. In the present study, automatic activation of the fusiform gyrus to facial emotion was investigated as a function of alexithymia as assessed by the 20-item Toronto Alexithymia Scale. During 3 Tesla fMRI scanning, pictures of faces bearing sad, happy, and neutral expressions masked by neutral faces were presented to 22 healthy adults who also responded to the Toronto Alexithymia Scale. The fusiform gyrus was selected as the region of interest, and voxel values of this region were extracted, summarized as means, and tested among the different conditions (sad, happy, and neutral faces). Masked sad facial emotions were associated with greater bilateral activation of the fusiform gyrus than masked neutral faces. The subscale, Difficulty Identifying Feelings, was negatively correlated with the neural response of the fusiform gyrus to masked sad faces. The correlation results suggest that automatic hyporesponsiveness of the fusiform gyrus to negative emotion stimuli may reflect problems in recognizing one's emotions in everyday life.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".