The Impact of Alexithymia on Pathological Gamblers’ Decision Making
Bibliographic record
Abstract
OBJECTIVE: We investigated whether alexithymia is at the root of the decision-making deficit classically reported in pathological gamblers. BACKGROUND: Alexithymia has been shown to be a recurrent personality trait of pathological gamblers and to impair the decision-making abilities of nonpathological gamblers, but no previous studies have investigated whether alexithymia significantly affects pathological gamblers' decision making. Although investigations of pathological gamblers typically have studied those seeking treatment, most pathological gamblers do not seek treatment. Thus, to study people representative of the general population of pathological gamblers, we conducted our study in "sportsbook" casinos with a small sample of gamblers who were not seeking treatment. METHODS: We recruited gamblers in sportsbooks and classified them based on their scores on the South Oaks Gambling Screen and the Toronto Alexithymia Scale: 3 groups of pathological gamblers (6 alexithymic, 8 possibly alexithymic, and 6 nonalexithymic) and 8 healthy controls. All of the participants completed an adaptation of the Iowa Gambling Task. RESULTS: The alexithymic group chose less advantageously on the task than the other groups. The severity of the deficit in decision-making abilities was related to the severity of alexithymia, even when we controlled for the effects of anxiety and depression. CONCLUSIONS: Our findings provide preliminary evidence that alexithymia might be a critical personality trait underlying pathological gamblers' decision-making deficits.
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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 | 0.001 | 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".