The Emotional Lexicon and Its Correlates Following Traumatic Brain Injury
Bibliographic record
Abstract
Traumatic brain injury (TBI) can often influence the way subjects process and cope with their emotional life. In spite of the huge amount of studies investigating facial emotion recognition in subjects with traumatic brain injury, none of them has examined if their emotional lexicon, i.e. the ability to express emotions through words, may be affected. In this case-control study, we investigated the emotional lexicon of a group of 16 severe TBI subjects, comparing their performances with an healthy control group. A set of 25 visual stimuli (10 single picture images, 5 cartoon story pictures and 10 video clips) were selected. All the stimuli were chosen for their high emotional content by ten blind judges. The participants were asked to describe the stimuli, focusing on their emotional content. To get a better understanding of the correlates of emotional lexicon, all the participants were administered with the backward version of the Digit Span test, the Ekman and Friesen 60 Faces, the 20-Item Toronto Alexithymia Scale and the Empathy Quotient. Results pointed out a significant difference between TBI subjects and healthy controls only for cartoon story and video clip description. Conversely, TBI subjects performed similarly to controls when asked to describe the single picture images. A significant correlation was found in TBI subjects between the results of the Digit Span and number of emotional words, while no correlation was detected between emotional terms and the three scales used to assess TBI subjects’ emotional profile. These outcomes highlight that, for more complex stimuli, difficulties in emotional lexicon may depend on factors other than empathy, alexythimia or emotion recognition. These difficulties seem to be related to reduced working memory capacity, which prevent the subjects from correctly processing the emotional content of stimuli.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".