Alpha and theta oscillations contribute to attribute regulation in dietary decision making under self-control
Bibliographic record
Abstract
Abstract How do different cognitive self-regulation strategies alter attribute value construction (AVC) and evidence accumulation (EA)? We recorded EEG during food choices while participants responded naturally or regulated their choices by focusing on healthy eating or decreasing their desire for all food. Using a drift diffusion model (DDM), we predicted the time course of neural signals associated with AVC and EA. Results suggested that suppression of frontal and occipital alpha power matched model-predicted EA signals: it tracked the goal-relevance of tastiness and healthiness attributes, predicted individual differences in successful down-regulation of tastiness, and conformed to the DDM-predicted time course of EA. We also found an earlier rise in frontal and occipital theta power that represented food tastiness more strongly during regulation, and predicted a weaker influence of food tastiness on behaviour. Our findings suggest that different regulatory strategies may commonly recruit theta-mediated control processes to modulate the attribute influence on EA.
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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 | 0.000 | 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) | 0.001 | 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".