Social-cue perception and mentalizing ability following traumatic brain injury: A human-robot interaction study
Bibliographic record
Abstract
PRIMARY OBJECTIVE: Research studies and clinical observations of individuals with traumatic brain injury (TBI) indicate marked deficits in mentalizing-perceiving social information and integrating it into judgements about the affective and mental states of others. The current study investigates social-cognitive mechanisms that underlie mentalizing ability to advance our understanding of social consequences of TBI and inform the development of more effective clinical interventions. RESEARCH DESIGN: The study followed a mixed-design experiment, manipulating the presence of a mentalizing gaze cue across trials and participant population (TBI vs. healthy comparisons). METHODS AND PROCEDURES: Participants, 153 adults, 74 with moderate-severe TBI and 79 demographically matched healthy comparison peers, were asked to judge a humanoid robot's mental state based on precisely controlled gaze cues presented by the robot and apply those judgements to respond accurately on the experimental task. MAIN OUTCOMES AND RESULTS: Results showed that, contrary to our hypothesis, the social cues improved task performance in the TBI group but not the healthy comparison group. CONCLUSIONS: Results provide evidence that, in specific contexts, individuals with TBI can perceive, correctly recognize, and integrate dynamic gaze cues and motivate further research to understand why this ability may not translate to day-to-day social interactions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".