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Record W2327842331 · doi:10.1037/hea0000134

Expression of executive control in situational context: Effects of facilitating versus restraining cues on snack food consumption.

2014· article· en· W2327842331 on OpenAlexaff
Peter A. Hall, Betty Tran, Cassandra J. Lowe, Corita Vincent, Marina Mourtzakis, Teresa Liu‐Ambrose, Harry Prapavessis, Yori Gidron

Bibliographic record

VenueHealth Psychology · 2014
Typearticle
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsWestern UniversityUniversity of British ColumbiaUniversity of Waterloo
Fundersnot available
KeywordsSensory cuePsychologyContext (archaeology)TasteDevelopmental psychologySituational ethicsCalorieSnack foodConsumption (sociology)Young adultFood choiceSocial psychologyFood scienceMedicineCognitive psychologyBiologyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: To examine the effects of executive function (EF) on objectively measured high-calorie snack food consumption in 2 age groups and to explore the moderating influence of environmental cues. METHODS: In Study 1, 43 older adults (M(age) = 74.81) and in Study 2, 79 younger adults (M(age) = 18.71) completed measures of EF and subsequently participated in a bogus taste-test paradigm wherein they were required to rate 3 highly appetitive (but high-calorie) snack foods on taste and texture. Grams of snack food consumed was measured covertly in the presence randomly assigned contextual cues (explicit semantic cues in Study 1; implicit visual cues in Study 2) that were facilitating or restraining in nature. RESULTS: Findings indicated that in both age groups, stronger EF predicted lower consumption of snack foods across conditions, and the effects of EF were most pronounced in the presence of facilitating cues. CONCLUSIONS: Older and younger adults with weaker EF tend to consume more high-calorie snack food compared with their stronger EF counterparts. These tendencies appear to be especially amplified in the presence of facilitating cues.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.855
Threshold uncertainty score0.628

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.113
GPT teacher head0.443
Teacher spread0.330 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations22
Published2014
Admission routes1
Has abstractyes

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