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Record W3030522065 · doi:10.1186/s12868-020-00572-6

Alexithymia and automatic processing of facial emotions: behavioral and neural findings

2020· article· en· W3030522065 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueBMC Neuroscience · 2020
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsnot available
FundersFAZIT StiftungDeutsche Forschungsgemeinschaft
KeywordsAlexithymiaPsychologyFacial expressionCognitive psychologyNeuroscienceComputer scienceClinical psychologyCommunication

Abstract

fetched live from OpenAlex

BACKGROUND: Alexithymia is a personality trait characterized by difficulties identifying and describing feelings, an externally oriented style of thinking, and a reduced inclination to imagination. Previous research has shown deficits in the recognition of emotional facial expressions in alexithymia and reductions of brain responsivity to emotional stimuli. Using an affective priming paradigm, we investigated automatic perception of facial emotions as a function of alexithymia at the behavioral and neural level. In addition to self-report scales, we applied an interview to assess alexithymic tendencies. RESULTS: During 3 T fMRI scanning, 49 healthy individuals judged valence of neutral faces preceded by briefly shown happy, angry, fearful, and neutral facial expressions. Alexithymia was assessed using the 20-Item Toronto Alexithymia Scale (TAS-20), the Bermond-Vorst Alexithymia Questionnaire (BVAQ) and the Toronto Structured Interview for Alexithymia (TSIA). As expected, only negative correlations were found between alexithymic features and affective priming. The global level of self-reported alexithymia (as assessed by the TAS-20 and the BVAQ) was found to be related to less affective priming owing to angry faces. At the facet level, difficulties identifying feelings, difficulties analyzing feelings, and impoverished fantasy (as measured by the BVAQ) were correlated with reduced affective priming due to angry faces. Difficulties identifying feelings (BVAQ) correlated also with reduced affective priming due to fearful faces and reduced imagination (TSIA) was related to decreased affective priming due to happy faces. There was only one significant correlation between alexithymia dimensions and automatic brain response to masked facial emotions: TAS-20 alexithymia correlated with heightened brain response to masked happy faces in superior and medial frontal areas. CONCLUSIONS: Our behavioral results provide evidence that alexithymic features are related in particular to less sensitivity for covert facial expressions of anger. The perceptual alterations could reflect impaired automatic recognition or integration of social anger signals into judgemental processes and might contribute to the problems in interpersonal relationships associated with alexithymia. Our findings suggest that self-report measures of alexithymia may have an advantage over interview-based tests as research tools in the field of emotion perception at least in samples of healthy individuals characterized by rather low levels of alexithymia.

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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.257

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.063
GPT teacher head0.323
Teacher spread0.260 · 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