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Record W2766489399 · doi:10.1038/s41598-017-14404-x

Alexithymia and emotional reactions to odors

2017· article· en· W2766489399 on OpenAlexaboutno aff
Cinzia Cecchetto, Raffaella I. Rumiati, Marilena Aiello

Bibliographic record

VenueScientific Reports · 2017
Typearticle
Languageen
FieldNeuroscience
TopicOlfactory and Sensory Function Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaOdorPsychologyOlfactionAffect (linguistics)AmygdalaToronto Alexithymia ScaleConstruct (python library)CognitionAudiologyClinical psychologyNeuroscienceCommunicationMedicine

Abstract

fetched live from OpenAlex

Alexithymia is a psychological construct characterized by deficits in processing emotional stimuli. However, little is known about the processing of odours in alexithymia, even though there is extensive proof that emotion and olfaction are closely linked. The present study is aimed at investigating how alexithymic individuals process emotions conveyed by odors. Emotional responses to unpleasant, neutral odors and clean air were collected through self-report ratings and psychophysiological measures in a sample of 62 healthy participants with high (HA), medium (MA) and low (LA) levels of alexithymia. Moreover, participants performed tests on odors identification and threshold and completed questionnaires assessing olfactory imagery and awareness. Two main results have been found: first, HA and MA groups showed altered physiological responses to odors, compared to LA, while no differences among the groups were observed in odor ratings; and second, affective and cognitive alexithymia components were differently associated with the performance on olfactory tests, skin conductance response to odors, reaction times in the rating task, and scores on olfactory questionnaires. We conclude that alexithymia is characterized by altered physiological reactions to olfactory stimuli; moreover, we stress the importance of evaluating the different alexithymia components since they affect emotional stimuli processing in different ways.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.767
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0010.000
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 head0.156
GPT teacher head0.308
Teacher spread0.152 · 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.

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

Citations32
Published2017
Admission routes1
Has abstractyes

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