How do Individuals With and Without Traumatic Brain Injury Interpret Emoji? Similarities and Differences in Perceived Valence, Arousal, and Emotion Representation
Bibliographic record
Abstract
Abstract Impaired facial affect recognition is common after traumatic brain injury (TBI) and linked to poor social outcomes. We explored whether perception of emotions depicted by emoji is also impaired after TBI. Fifty participants with TBI and 50 non-injured peers generated free-text labels to describe emotions depicted by emoji and rated their levels of valence and arousal on nine-point rating scales. We compared how the two groups’ valence and arousal ratings were clustered and examined agreement in the words participants used to describe emoji. Hierarchical clustering of affect ratings produced four emoji clusters in the non-injured group and three emoji clusters in the TBI group. Whereas the non-injured group had a strongly positive and a moderately positive cluster, the TBI group had a single positive valence cluster, undifferentiated by arousal. Despite differences in cluster numbers, hierarchical structures of the two groups’ emoji ratings were significantly correlated. Most emoji had high agreement in the words participants with and without TBI used to describe them. Participants with TBI perceived emoji similarly to non-injured peers, used similar words to describe emoji, and rated emoji similarly on the valence dimension. Individuals with TBI showed small differences in perceived arousal for a minority of emoji. Overall, results suggest that basic recognition processes do not explain challenges in computer-mediated communication reported by adults with TBI. Examining perception of emoji in context by people with TBI is an essential next step for advancing our understanding of functional communication in computer-mediated contexts after brain injury.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".