Social cognition assessment using virtual reality: A systematic review.
Bibliographic record
Abstract
Social cognition refers to the abilities that allow us to perceive, process, and respond to others’ behaviors and emotions. Technological advancements in virtual reality (VR) support its potential for evaluating social cognition and addressing limitations of traditional psychometric measures. A systematic review was conducted to document VR tasks that have been used to study social cognition. An inventory of these tasks is provided along with detailed information on study, participant, assessment tool, and technological characteristics. Challenges and limitations associated with these tools are discussed to inform future directions for using VR for social cognition. Embase, APA PsycInfo, PubMed, Web of Science, and Cochrane Library were queried using relevant keywords. Articles were screened for eligibility according to the Population, Intervention, Comparison, Outcomes, and Study design criteria. The data extraction table was developed from relevant literature. Two reviewers performed screening and extraction steps independently, and disagreements were resolved by a third independent reviewer. Sixty-six studies were included, covering 59 unique VR tools assessing social cognition in the following areas: emotion processing, social perception, moral reasoning, theory of mind, empathy, and attribution style. Most paradigms targeted emotion processing. Autism and schizophrenia were the most studied clinical conditions. Important issues identified through the review included challenges associated with cultural adaptations and limited psychometric data. Numerous options are available to study social cognition using VR, but most focus on lower order sociocognitive abilities (e.g., emotion processing), and future work should seek to develop stimuli or environments that can target more complex skills and can accommodate greater diversity for cross-cultural use.
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 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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".