Regulation of interpersonal distance in virtual reality: Implications for socio-emotional functioning in late adulthood
Bibliographic record
Abstract
OBJECTIVE: Accurately interpreting emotional states from facial expressions is crucial for effective social interactions. This study investigates age-related differences in interpersonal distance (IPD) regulation and emotion recognition using a virtual reality (VR) environment. We examined how younger and older adults adjust their IPD in response to emotional expressions from virtual agents. METHODS: Eighty participants, divided into older adults (OA) and younger adults (YA), took part in the study. Participants were immersed in a VR setup where they engaged in social interactions with happy or angry looking virtual agents. This behavioral task was complemented by a standardized emotion recognition task (ERT). RESULTS: Results showed that both YA and OA preferred larger distances from angry-looking virtual agents compared to happy ones. No significant differences in IPD were found between the age groups. However, older adults were less accurate in recognizing facial expressions. CONCLUSION: These findings suggest that older adults can effectively regulate their social distance despite potential challenges in emotion recognition. The study underscores the importance of considering cognitive, perceptual, and motivational factors when examining the dynamics of emotional recognition and interpersonal distance in social contexts.
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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 | 0.000 | 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".