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Record W2526897383 · doi:10.18725/oparu-4084

Mimische Emotionserkennung und Alexithymie

2016· dissertation· en· W2526897383 on OpenAlexaboutno aff
Mattias Kammerer

Bibliographic record

VenueOPen Access Repositorium der Universität Ulm (OPARU) (Ulm University) · 2016
Typedissertation
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

The aim of this dissertation was the assessment of possible correlations between alexithymia and the recognition of emotions in a sample of healthy subjects. Self-rated alexithymia (according to the altered factorial structure of the German version of the Toronto Alexithymia Scale – TAS-20) was correlated with objectively measured emotion recognition performance. Emotions were presented by facial expressions (Facially Expressed Emotion Labeling - FEEL) and text-based scenic descriptions of social interactions (Levels of Emotional Awareness Scale - LEAS). Objectively assessed emotion recognition (FEEL and LEAS) correlated positively with the importance of emotional introspection (TAS-20). External thinking (TAS-20) correlated negative with performance in the Levels of Emotional Awareness Scale. There was no correlation between the emotional performance tests (FEEL and LEAS) and the core of alexithymia, difficulties in identifying and describing emotions (TAS-20). Alexithymia (TAS-20 total score) is related with mental strain (GSI SCL-90-R). Another focus was on gender-specific differences in emotion recognition performance. Women performed significantly better in the Levels of Emotional Awareness Scale, in LEAS Score Self and especially in LEAS Score Other. Emotional introspection (TAS-20) was more important for the female participants and they showed less external thinking (TAS-20). In this study there was no significant correlation between self-rated alexithymia (TAS-20 total score) and deficits in the recognition of emotions shown by facial expression (FEEL).

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.524
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.005
Open science0.0030.001
Research integrity0.0010.001
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 head0.033
GPT teacher head0.352
Teacher spread0.318 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

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