How Does Emotional Context Modulate Response Inhibition in Alexithymia: Electrophysiological Evidence from an ERP Study
Bibliographic record
Abstract
BACKGROUND: Alexithymia, characterized by difficulties in identifying and describing feelings, is highly indicative of a broad range of psychiatric disorders. Several studies have also discovered the response inhibition ability impairment in alexithymia. However, few studies on alexithymic individuals have specifically examined how emotional context modulates response inhibition procedure. In order to investigate emotion cognition interaction in alexithymia, we analyzed the spatiao-temporal features of such emotional response inhibition by the approaches of event-related potentials and neural source-localization. METHOD: The study participants included 15 subjects with high alexithymia scores on the 20-item Toronto Alexithymia Scale (alexithymic group) and 15 matched subjects with low alexithymia scores (control group). Subjects were instructed to perform a modified emotional Go/Nogo task while their continuous electroencephalography activities were synchronously recorded. The task includes 3 categories of emotional contexts (positive, negative and neutral) and 2 letters ("M" and "W") centered in the screen. Participants were told to complete go and nogo actions based on the letters. We tested the influence of alexithymia in this emotional Go/Nogo task both in behavioral level and related neural activities of N2 and P3 ERP components. RESULTS: We found that negatively valenced context elicited larger central P3 amplitudes of the Nogo-Go difference wave in the alexithymic group than in the control group. Furthermore, source-localization analyses implicated the anterior cingulate cortex (ACC) as the neural generator of the Nogo-P3. CONCLUSION: These findings suggest that difficulties in identifying feelings, particularly in negative emotions, is a major feature of alexithymia, and the ACC plays a critical role in emotion-modulated response inhibition related to alexithymia.
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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 | 0.001 |
| 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".