Focused attention during eating enhanced memory for meal satiety but did not reduce later snack intake in men: A randomised within-subjects laboratory experiment
Bibliographic record
Abstract
Attending to food being eaten (‘attentive eating’) may reduce later overeating. However, evidence in support of this comes primarily from studies in women. The aims of the current study were to investigate the effect that attentive eating has on later food intake in men and examine potential underlying mechanisms. Using a within-subjects design, 34 men (BMI M = 23.73 kg/m 2 , SD = 2.93; age M = 29.15, SD = 11.99) consumed a fixed lunchtime meal on two study days. On one study day participants were instructed to pay attention to the sensory properties of the meal as they ate (focused attention condition), and on the other study day participants ate lunch normally. Three hours after each lunchtime session, ad libitum consumption of snack food was measured, and measures of memory for the earlier lunchtime meal were completed. Participants remembered the lunch to be significantly more satiating in the focused attention condition compared to the control condition. However, focused attention did not significantly affect later ad libitum snack intake or other measures of meal memory. Further research is needed to understand when focused attention influences subsequent food intake before this approach can be used effectively to reduce food intake.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".