Selective attention to emotional pictures as a function of gambling motives in problem and nonproblem gamblers.
Bibliographic record
Abstract
Problem gambling may reflect a maladaptive means of fulfilling specific affect-regulation motives, such as enhancing positive affect or coping with negative affect. Research with clinical populations indicates that disorders with prominent affective symptoms are characterized by attentional biases for symptom-congruent information. Thus, we assessed whether problem gamblers with enhancement motives for gambling would demonstrate attentional biases for positive emotional information, relative to other types of emotional information, and problem gamblers with coping motives for gambling would demonstrate attentional biases for negative emotional information, compared with other types of emotional information. In addition, we expected motive-congruent biases to be stronger in problem gamblers than nonproblem gamblers. To test these hypotheses, problem and nonproblem gamblers received an emotional orienting task in which neutral, negative, and positive pictorial cues appeared to one side of the computer screen, followed by target words in cued or uncued locations. In a look-away condition, participants had to shift attention away from pictures to respond to predominantly uncued targets, whereas in a look-toward condition, they had to orient to pictures to categorize predominantly cued targets. The results revealed motive-congruent orienting biases and disengagement lags for emotional pictures in problem gamblers. The link between motives and affective biases was less apparent in nonproblem gamblers. Results suggest that attentional measures may provide a useful complement to the subjective methodologies that are typically employed in studying problem gamblers.
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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".