Decision‐Making Deficits and Overeating: A Risk Model for Obesity
Bibliographic record
Abstract
OBJECTIVE: To demonstrate that human overeating is not just a passive response to salient environmental triggers and powerful physiological drives; it is also about making choices. The ventromedial prefrontal cortex has been strongly implicated in the neural circuitry necessary for making advantageous decisions when various options for action are available. Decision-making deficits have been found in patients with ventromedial prefrontal cortex lesions and in those with substance dependence--impairments that reflect an inability to advantageously assess future consequences. That is, they choose immediate rewards in the face of future long-term negative consequences. RESEARCH METHODS AND PROCEDURES: We extended this research to the study of overeating and overweight, testing a regression model that predicted that poor decision making (as assessed by a validated computerized gambling task) and a tendency to overeat under stress would correlate with higher BMI in a group of healthy adult women (N = 41) representing a broad range of body weights. RESULTS: We found statistically significant main effects for both independent variables in the predicted direction (p < 0.05; R2 = 0.35). Indeed, the decision-making impairments across the 100 trials of the computer task were greater in those with high BMI than in previous studies with drug addicts. DISCUSSION: Findings suggested that cortical and subcortical processes, which regulate one's ability to inhibit short-term rewards when the long-term consequences are deleterious, may also influence eating behaviors in a culture dominated by so many, and such varied, sources of palatable and calorically dense sources of energy.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".