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Record W3124928277 · doi:10.1038/srep34979

Swiss identity smells like chocolate: Social identity shapes olfactory judgments

2016· article· en· W3124928277 on OpenAlexfundno aff
Géraldine Coppin, Eva Pool, Sylvain Delplanque, Bastiaan Oud, Christian Margot, David Sander, Jay J. Van Bavel

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaUniversité de GenèveSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsIdentity (music)Olfactory perceptionComputer sciencePsychologyData scienceOlfactionArtAestheticsNeuroscience

Abstract

fetched live from OpenAlex

There is extensive evidence that social identities can shape people's attitudes and behavior, but what about sensory judgments? We examined the possibility that social identity concerns may also shape the judgment of non-social properties-namely, olfactory judgment. In two experiments, we presented Swiss and non-Swiss participants with the odor of chocolate, for which Switzerland is world-famous, and a control odor (popcorn). Swiss participants primed with Swiss identity reported the odor of chocolate (but not popcorn) as more intense than non-Swiss participants (Experiments 1 and 2) and than Swiss participants primed with individual identity or not primed (Experiment 2). The self-reported intensity of chocolate smell tended to increase as identity accessibility increased-but only among Swiss participants (Experiment 1). These results suggest that identity priming can counter-act classic sensory habituation effects, allowing identity-relevant smells to maintain their intensity after repeated presentations. This suggests that social identity dynamically influences sensory judgment. We discuss the potential implications for models of social identity and chemosensory perception.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.603
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.002

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 head0.105
GPT teacher head0.369
Teacher spread0.264 · 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; both teacher heads agree on what is shown here.

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

Citations19
Published2016
Admission routes1
Has abstractyes

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