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Record W2331772130 · doi:10.1097/htr.0b013e31829dded6

Affect Recognition in Traumatic Brain Injury

2013· article· en· W2331772130 on OpenAlexaff
Barbra Zupan, Dawn Neumann

Bibliographic record

VenueJournal of Head Trauma Rehabilitation · 2013
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsBrock University
Fundersnot available
KeywordsAffect (linguistics)Traumatic brain injuryContext (archaeology)PsychologyEmotion recognitionFacial expressionAudiologyMedicinePsychiatryNeuroscienceCommunication

Abstract

fetched live from OpenAlex

OBJECTIVES: To compare affect recognition by people with and without traumatic brain injury (TBI) for (1) unimodal and context-enriched multimodal media; (2) positive (happy) and negative emotions; and (3) neutral multimodal stimuli. PARTICIPANTS: A total of 60 people with moderate to severe TBI and 60 matched controls. MEASURES: (1) facial affect, (2) vocal affect, and (3) multimodal affect. RESULTS: Compared with controls, people with TBI scored significantly lower on both unimodal measures but not on the multimodal measure. Within- group comparisons for people with TBI revealed that they were better at recognizing affect from multimodal than unimodal stimuli. As a group, participants with TBI who were categorized as having impaired facial/vocal affect recognition were less accurate at recognizing all emotions, including happy, than unimpaired participants. Neutral stimuli were more poorly identified by participants with TBI than by those with controls. CONCLUSION: Context-enriched multimodal stimuli may enhance affect recognition for people with TBI. People with TBI who have impaired affect recognition may have problems identifying both positive (happy) and negative expressions. Furthermore, people with TBI may perceive affect when there is none.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.934
Threshold uncertainty score0.812

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.076
GPT teacher head0.388
Teacher spread0.312 · 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 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

Citations31
Published2013
Admission routes1
Has abstractyes

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