Spatio-temporal EEG dynamics during decision-making in online poker players with problem gambling
Bibliographic record
Abstract
Background and aims: Gambling activity evolves along a continuum from recreational to Gambling Disorder (GD) and a particular challenge is to identify whether there are some neurophysiological particularities already present in gamblers at an early stage. Our main goal was to determine whether, in the gamblers' population, neural responses generated during uncertain decisions were different depending on problematic gambling risk defined by the Canadian Problem Gambling Index (CPGI). We tested the following hypothesis, that the Problem Gambling group would show a different brain activity related to outcomes processing than people with low risk. Methods: For this purpose, we established a relatively homogeneous population of Online Poker Players divided into two groups according to the CPGI (Low Risk and Problem Gambling). By means of high-density EEG, we compared the spatio-temporal dynamics generated during the completion of the Iowa Gambling Task. Results: One specific topographic map was observed between 150-175 ms after a negative outcome for both groups, whereas it was displayed in the win condition only for the Problem Gambling group. We found that the Global Field Power of this map was negatively correlated with participants' adherence to a strategy. Source localization identified Anterior Cingulate Cortex and Temporal regions as generators of this map. Discussion and conclusions: Reward hypersensitivity EEG responses identified in the early outcome process could constitute a potential biomarker of problematic gambling.
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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) | 0.002 | 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".