Gambling disorder is associated with reduced sensitivity to expected value during risky choice
Bibliographic record
Abstract
BACKGROUND AND AIMS: Individuals with gambling disorder display increased levels of risk-taking, but it is not known if it is associated with an altered subjective valuation of gains and/or losses, perception of their probabilities, or integration of these sources of information into expected value. METHODS: Participants with gambling disorder (n = 48) were compared with a healthy comparison group (n = 35) on a two-choice lottery task that involved either gains-only or losses-only gambles. On each trial, two lotteries were displayed, showing the associated probability and magnitude of the possible outcome for each. On each trial, participants chose one of the two lotteries, and the outcome was revealed. RESULTS: Choice behaviour was highly sensitive to the expected value of the two gambles in both the gain and loss domains. This sensitivity to expected value was attenuated in the group with gambling disorder. The group with gambling disorder used both probability and magnitude information less, and this impairment was greater for probability information. By contrast, they used prior feedback (win vs loss) to inform their next choice, despite the independence of each trial. Within the gambling disorder group, problem gambling severity and trait gambling-related cognitions independently predicted reduced sensitivity to expected value. The majority of observed effects were consistent across both gain and loss domains. DISCUSSION AND CONCLUSIONS: Our results provide a thorough characterization of decision processes in gain and loss domains in gambling disorder, and place these problems in the context of theoretical constructs from behavioural economics.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".