Selective impairment of decision making under ambiguity in alexithymia
Bibliographic record
Abstract
BACKGROUND: Alexithymia is characterised by difficulties identifying and describing emotions. Few studies have investigated how alexithymia influences decision-making under different conditions (ambiguity and risk). This study aimed to examine whether alexithymia contributes to impairment in decision-making. METHOD: This study included 42 participants with high scores in the Chinese version of Toronto Alexithymia Scale (alexithymia group), and 44 matched subjects with low scores (control group). Decision-making was measured using the Iowa Gambling Task (IGT) and the Game of Dice Task (GDT). RESULTS: The main findings of this study revealed selective deficits in IGT performance for the alexithymia group, while GDT performance was unimpaired when compared with the control group. In IGT, total netscores were lower for the alexithymia group compared to the control group, particularly with regard to block 5. Moreover, the alexithymia individuals selected significantly more adverse cards than the controls, indicating significant decision-making impairments. CONCLUSION: Alexithymia selectively influences decision-making under ambiguity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".