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Record W1963317479 · doi:10.1080/1357650x.2015.1075546

Emotional correlates of unirhinal odour identification

2015· article· en· W1963317479 on OpenAlexaboutno aff
David G. Wilkinson, Sergio Moreno, Chee Siang Ang, Farzin Deravi, Dinkar Sharma, Mohamed Sakel

Bibliographic record

VenueLaterality Asymmetries of Body Brain and Cognition · 2015
Typearticle
Languageen
FieldNeuroscience
TopicOlfactory and Sensory Function Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyInterpersonal Reactivity IndexFacial expressionEmpathyOlfactionEmotional expressionCognitive psychologyFeelingNeurotypicalAudiologyDevelopmental psychologyCommunicationNeuroscienceSocial psychologyPerspective-taking

Abstract

fetched live from OpenAlex

It seems self-evident that smell profoundly shapes emotion, but less clear is the nature of this interaction. Here, we sought to determine whether the ability to identify odours co-varies with self-reported feelings of empathy and emotional expression recognition, as predicted if the two capacities draw on common resource. Thirty-six neurotypical volunteers were administered the Alberta Smell Test, the Interpersonal Reactivity Index and an emotional expression recognition task. Statistical analyses indicated that feelings of emotional empathy positively correlated with odour discrimination in right nostril, while the recognition of happy and fearful facial expressions positively correlated with odour discrimination in left nostril. These results uncover new links between olfactory discrimination and emotion which, given the ipsilateral configuration of the olfactory projections, point towards intra- rather than inter-hemispheric interaction. The results also provide novel support for the proposed lateralization of emotional empathy and the recognition of facial expression, and give reason to further explore the diagnostic sensitivity of smell tests because reduced sensitivity to others' emotions can mark the onset of certain neurological diseases.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.225
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.000

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 head0.154
GPT teacher head0.285
Teacher spread0.131 · 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 designBench or experimental
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

Citations4
Published2015
Admission routes1
Has abstractyes

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