Difficulties identifying feelings, alexithymic features and brain responses in social phobia trait: an fMRI study
Bibliographic record
Abstract
Aims A growing body of research now documents a specific pattern of brain activation during emotional tasks in patients with social phobia. Furthermore, recent studies indicate that non-clinical participants show a similar pattern of responses. Clinical and physiological data from literature highlight that social anxiety is associated with difficulties in emotional managing. However, much less is known about the part of alexithymia in social phobia, as far as clinical and infra-clinical (high shyness) approaches are concerned. Method Four hundred undergraduate university students were screened with an anxiety and social phobia questionnaire. Forty participants, with low and high levels of social phobia, were then included according to a dimensional approach. Each participant underwent a comprehensive psychiatric evaluation that included a structured clinical interview for current and past psychiatric disorders and psychometric scales, including the Liebowitz Social Anxiety Scale (LSAS) and the Toronto Alexithymia Scale (TAS-20). Participants were asked to make gender discrimination choices when viewing faces that showed happiness, fear, anger, sadness, neutral expressions or distractors while in a 3 Tesla fMRI scanner. Results As expected, social phobia trait was correlated with TAS-20 scores, and specifically in “difficulties identifying feelings”. Brain activations showed an evolutionary pattern response in correlation with social phobia and alexithymia concerning limbic regions (amygdala and insula). Social phobia trait seems to be particularly receptive to anger faces. Conclusion Our findings support the hypothesis that alexithymia play a major role in social anxiety disorder. Identifying feelings could explain alexithymic functioning in social phobia, clinically and physiologically.
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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 | 0.000 |
| 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".