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Record W2001723642 · doi:10.1038/oby.2005.2

Lessons from the Bottomless Bowl

2005· letter· en· W2001723642 on OpenAlexaff
C. Peter Herman

Bibliographic record

VenueObesity Research · 2005
Typeletter
Languageen
FieldNeuroscience
TopicRegulation of Appetite and Obesity
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFeelingCalorieTask (project management)Control (management)PsychologyAestheticsCognitive psychologySocial psychologyComputer scienceMedicineArtificial intelligenceArtEngineeringEndocrinology

Abstract

fetched live from OpenAlex

In their clever experiment, Wansink et al. (1) provide a dramatic illustration of the importance of visual cues in the control of food intake. As they put it, aphoristically, “people use their eyes to count calories and not their stomachs.” Thanks to the Rube Goldbergesque imperceptibly self-refilling bowl, the experimental participants—through the clear evidence of their own senses—were misled into believing that they had eaten less than they really had. As a result, they consumed a full 73% more soup than did participants who ate from a normal soup bowl, without realizing it and without feeling any fuller. The ancient notion of the “wisdom of the body,” in which calories are regulated automatically through hormonal/neuronal mechanisms and negative-feedback loops, simply cannot accommodate the fact that trick soup bowls can so easily fool the eater. Wansink et al. (1) argue that, rather than keeping track of how much we eat—monitoring mouthfuls or spoonfuls or even calories, we turn this difficult and annoying task over to proxies. For instance, we, or at least 61% of us, according to the supplementary data of Wansink et al., are habitual plate-cleaners; in effect, we accept the portion on our plate as an appropriate amount to eat, and, therefore, we eat the full portion, monitoring our intake only to the extent of stopping when the plate is empty. Because our portions have grown larger (2), we eat more, without particularly realizing it, thereby doing our part to promote the current obesity epidemic. The bottomless bowl study raises some perplexing questions. Importantly, what exactly was the visual cue that the experimental participants used to terminate eating? Not the empty bowl; none of them got that far; even the normal bowl was replenished by the experimenter when only 25% of the soup remained. The eaters ended up, on average, eating (or leaving) one-half, which Wansink et al. suggest may have been their target when eating from a large (18-oz) bowl. However, clearly several people in the normal bowl condition ate at least 75% of the soup—triggering a refill—so the one-half mark was not their original target. When do people target the entire portion and when do they target only one-half? Will they change their minds partway through the meal if they realize that consuming a full portion is not possible? Finally, it may be argued that, in this study, there was inadequate time for satiety cues to accumulate and feedback to stop intake. If the meal had been interrupted and later resumed, perhaps participants would have received more reliable information from their brain–gut axis about how sated they really were. Possibly—but in the real world, we eat our meals without the sort of interlude that might encourage useful internal feedback. Accordingly, the size of the original portion dictates our intake, and when that portion is excessive, our intake will be excessive too. If only our bowls and plates were programmed to imperceptibly empty themselves!

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0020.007
Scholarly communication0.0040.007
Open science0.0040.003
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0220.006

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.

Opus teacher head0.181
GPT teacher head0.400
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations6
Published2005
Admission routes1
Has abstractyes

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