Meta-analyses have found that social cognition training (SCT) has large effects on the emotion recognition ability of people with a psychotic disorder. Virtual reality (VR) could be a promising tool for delivering SCT. Presently, it is unknown how improvements in emotion recognition develop during (VR-)SCT, which factors impact improvement, and how improvements in VR relate to improvement outside VR. Data were extracted from task logs from a pilot study and randomized controlled trials on VR-SCT (n = 55). Using mixed-effects generalized linear models, we examined the: (a) effect of treatment session (1-5) on VR accuracy and VR response time for correct answers; (b) main effects and moderation of participant and treatment characteristics on VR accuracy; and (c) the association between baseline performance on the Ekman 60 Faces task and accuracy in VR, and the interaction of Ekman 60 Faces change scores (i.e., post-treatment - baseline) with treatment session. Accounting for the task difficulty level and the type of presented emotion, participants became more accurate at the VR task (b = 0.20, p < 0.001) and faster (b = -0.10, p < 0.001) at providing correct answers as treatment sessions progressed. Overall emotion recognition accuracy in VR decreased with age (b = -0.34, p = 0.009); however, no significant interactions between any of the moderator variables and treatment session were found. An association between baseline Ekman 60 Faces and VR accuracy was found (b = 0.04, p = 0.006), but no significant interaction between difference scores and treatment session. Emotion recognition accuracy improved during VR-SCT, but improvements in VR may not generalize to non-VR tasks and daily life.

中文翻译：

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虚拟现实中情绪识别训练的轨迹和精神病患者改善的预测因素。

荟萃分析发现，社会认知训练 (SCT) 对精神病患者的情绪识别能力有很大影响。虚拟现实 (VR) 可能是提供 SCT 的有前途的工具。目前，尚不清楚在 (VR-)SCT 期间情绪识别的改进是如何发展的，哪些因素会影响改进，以及 VR 中的改进如何与 VR 之外的改进相关联。数据是从一项试点研究和 VR-SCT 随机对照试验 (n = 55) 的任务日志中提取的。使用混合效应广义线性模型，我们检查了：(a) 治疗阶段 (1-5) 对 VR 准确性和正确答案的 VR 响应时间的影响；(b) 参与者和治疗特征对 VR 准确性的主要影响和调节；(c) Ekman 60 Faces 任务的基线性能与 VR 准确性之间的关联，以及 Ekman 60 Faces 变化分数（即治疗后 - 基线）与治疗会话的相互作用。考虑到任务难度级别和呈现的情绪类型，参与者在 VR 任务中变得更加准确（b = 0.20，p < 0.001），并且随着治疗过程的进行，提供正确答案的速度更快（b = -0.10，p < 0.001） . VR 中的整体情绪识别准确性随着年龄的增长而下降 (b = -0.34, p = 0.009)；然而，没有发现任何调节变量和治疗过程之间存在显着的相互作用。发现基线 Ekman 60 Faces 与 VR 准确性之间存在关联（b = 0.04，p = 0.006），但差异分数与治疗会话之间没有显着交互作用。