Using virtual reality to improve verbal episodic memory in schizophrenia: A proof-of-concept trial
Bibliographic record
Abstract
Schizophrenia is associated with impairments in verbal episodic memory. Strategy for Semantic Association Memory (SESAME) training represents a promising cognitive remediation program to improve verbal episodic memory. Virtual reality (VR) may be a novel tool to increase the ecological validity and transfer of learned skills of traditional cognitive remediation programs. The present proof-of-concept study aimed to assess the feasibility, acceptability, and preliminary efficacy of a VR-based cognitive remediation module inspired by SESAME principles to improve the use of verbal episodic memory strategies in schizophrenia. Thirty individuals with schizophrenia/schizoaffective disorder completed this study. Participants were randomized to either a VR-based verbal episodic memory training condition inspired by SESAME principles (intervention group) or an active control condition (control group). In the training condition, a coach taught semantic encoding strategies (active rehearsal and semantic clustering) to help participants remember restaurant orders in VR. In the active control condition, participants completed visuospatial puzzles in VR. Attrition rate, participant experience ratings, and cybersickness questionnaires were used to assess feasibility and acceptability. Trial 1 of the Hopkins Verbal Learning Test – Revised was administered pre- and post-intervention to assess preliminary efficacy. Feasibility was demonstrated by a low attrition rate (5.88 %), and acceptability was demonstrated by limited cybersickness and high levels of enjoyment. Although the increase in the number of semantic clusters used following the module did not reach conventional levels of statistical significance in the intervention group, it demonstrated a notable trend with a medium effect size (t = 1.48, p = 0.15, d = 0.54), in contrast to the control group where it remained stable (t = 0.36, p = 0.72, d = 0.13). These findings were similar for the semantic clustering ratio in the intervention (t = 1.61, p = 0.12, d = 0.59) and control (t = 0.36, p = 0.72, d = 0.13) groups. There was no significant change in the number of recalled words in either group following VR immersion. This VR intervention was feasible, acceptable, and may be useful for improving the use of semantic encoding strategies. These findings support the use of more ecological approaches for the treatment of cognitive impairments in schizophrenia, such as VR-based cognitive remediation.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".