Holmlund Terje B, Cohen Alex S, Cheng Jian, Foltz Peter W, Bernstein Jared, Rosenfeld Elizabeth, Laeng Bruno, Elvevåg Brita
Department of Clinical Medicine, University of Tromsø-The Arctic University of Norway, 9037 Tromsø, Norway.
Department of Psychology, Louisiana State University, Baton Rouge, LA 70803, USA.
Brain Sci. 2023 Mar 4;13(3):442. doi: 10.3390/brainsci13030442.
The Stroop interference task is indispensable to current neuropsychological practice. Despite this, it is limited in its potential for repeated administration, its sensitivity and its demands on professionals and their clients. We evaluated a digital Stroop deployed using a smart device. Spoken responses were timed using automated speech recognition. Participants included adult nonpatients (N = 113; k = 5 sessions over 5 days) and patients with psychiatric diagnoses (N = 85; k = 3-4 sessions per week over 4 weeks). Traditional interference (difference in response time between color incongruent words vs. color neutral words; M = 0.121 s) and facilitation (neutral vs. color congruent words; M = 0.085 s) effects were robust and temporally stable over testing sessions (ICCs 0.50-0.86). The performance showed little relation to clinical symptoms for a two-week window for either nonpatients or patients but was related to self-reported concentration at the time of testing for both groups. Performance was also related to treatment outcomes in patients. The duration of response word utterances was longer in patients than in nonpatients. Measures of intra-individual variability showed promise for understanding clinical state and treatment outcome but were less temporally stable than measures based solely on average response time latency. This framework of remote assessment using speech processing technology enables the fine-grained longitudinal charting of cognition and verbal behavior. However, at present, there is a problematic lower limit to the absolute size of the effects that can be examined when using voice in such a brief 'out-of-the-laboratory condition' given the temporal resolution of the speech-to-text detection system (in this case, 10 ms). This resolution will limit the parsing of meaningful effect sizes.
Stroop干扰任务在当前神经心理学实践中不可或缺。尽管如此，它在重复施测的潜力、敏感性以及对专业人员及其客户的要求方面存在局限性。我们评估了一种使用智能设备部署的数字Stroop任务。使用自动语音识别对语音反应进行计时。参与者包括成年非患者（N = 113；在5天内进行5次测试）和患有精神疾病诊断的患者（N = 85；在4周内每周进行3 - 4次测试）。传统的干扰效应（颜色不一致单词与颜色中性单词之间的反应时间差异；M = 0.121秒）和促进效应（中性单词与颜色一致单词之间；M = 0.085秒）在测试过程中是稳健且随时间稳定的（组内相关系数ICC为0.50 - 0.86）。在两周的时间窗口内，无论是非患者还是患者，其表现与临床症状几乎没有关系，但与两组在测试时自我报告的注意力集中程度有关。患者的表现也与治疗结果相关。患者说出反应词的持续时间比非患者长。个体内变异性的测量方法在理解临床状态和治疗结果方面显示出前景，但在时间上不如仅基于平均反应时间潜伏期的测量方法稳定。这种使用语音处理技术的远程评估框架能够对认知和言语行为进行细粒度的纵向记录。然而，目前在这种简短的“实验室外条件”下使用语音时，鉴于语音到文本检测系统的时间分辨率（在这种情况下为10毫秒），可检测到的效应的绝对大小存在一个有问题的下限。这种分辨率将限制对有意义效应大小的解析。
