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Record W4402495015 · doi:10.1016/j.nlm.2024.107984

Cue-potentiated feeding in rodents: Implications for weight regulation in obesogenic environments

2024· review· en· W4402495015 on OpenAlexafffund
Michael D. Kendig, Laura H. Corbit

Bibliographic record

VenueNeurobiology of Learning and Memory · 2024
Typereview
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversity of Toronto
FundersAustralian Research CouncilCanadian Institutes of Health Research
KeywordsOvereatingPsychologyNeuroscienceFood intakeCognitive psychologyDevelopmental psychologyBiologyObesity

Abstract

fetched live from OpenAlex

Cue-potentiated feeding (CPF) describes instances where food intake is increased by exposure to conditioned cues associated with food, often in the absence of hunger. CPF effects have been reported in a range of experimental protocols developed by researchers working across diverse fields spanning behavioural neuroscience, social psychology and ecology. Here we review the evolution of research on cue-potentiated feeding in animal models to identify important behavioural parameters and key neural circuits and pharmacological systems underlying the effect. Overall, evidence indicates that social, discrete and contextual stimuli can be used to elicit CPF effects across multiple species, though effects are often subtle and sensitive to procedural variables. While regular exposure to food cues is thought to be a key risk factor for overeating in so-called 'obesogenic' environments, further work is needed to identify whether CPF promotes positive energy balance and weight gain over the longer term. We suggest several methodological and conceptual areas for inquiry to elucidate the contribution of CPF to the regulation of food choice and energy 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.969
Threshold uncertainty score0.841

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.363
Teacher spread0.323 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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

Citations3
Published2024
Admission routes2
Has abstractyes

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