Is obesity related to enhanced neural reactivity to visual food cues? A review and meta-analysis
Bibliographic record
Abstract
Theoretical work suggests that obesity is related to enhanced incentive salience of food cues. However, evidence from both behavioral and neuroimaging studies on the topic is mixed. In this work we review the literature on cue reactivity in obesity and perform a preregistered meta-analysis of studies investigating effects of obesity on brain responses to passive food pictures viewing. Further, we examine whether age influences brain responses to food cues in obesity. In the meta-analysis we included 13 studies of children and adults that investigated group differences (obese vs. lean) in responses to food vs. non-food pictures viewing. While we found no significant differences in the overall meta-analysis, we show that age significantly influences brain response differences to food cues in the left insula and the left fusiform gyrus. In the left insula, obese vs. lean brain differences in response to food cues decreased with age, while in the left fusiform gyrus the pattern was opposite. Our results suggest that there is little evidence for obesity-related differences in responses to food cues and that such differences might be mediated by additional factors that are often not considered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".