Shen Chen, Calvin Olivia L, Rawls Eric, Redish A David, Sponheim Scott R
University of Minnesota, Minneapolis MN 55455 USA.
Veterans Affairs Medical Center, One Veterans Drive, Minneapolis MN 55417 USA.
medRxiv. 2023 Aug 16:2023.08.14.23293891. doi: 10.1101/2023.08.14.23293891.
Cognitive control deficits are consistently identified in individuals with schizophrenia and other psychotic psychopathologies. In this analysis, we delineated proactive and reactive control deficits in psychotic psychopathology via hierarchical Drift Diffusion Modeling (hDDM). People with psychosis (PwP; N=123), their first-degree relatives (N=79), and controls (N=51) completed the Dot Pattern Expectancy task, which allows differentiation between proactive and reactive control. PwP demonstrated slower drift rates on proactive control trials suggesting less efficient use of cue information for proactive control. They also showed longer non-decision times than controls on infrequent stimuli sequences suggesting slower perceptual processing. An explainable machine learning analysis indicated that the hDDM parameters were able to differentiate between the groups better than conventional measures. Through DDM, we found that cognitive control deficits in psychosis are characterized by slower motor/perceptual time and slower evidence-integration primarily in proactive control.
在患有精神分裂症和其他精神病性精神障碍的个体中，认知控制缺陷一直被发现。在本分析中，我们通过分层漂移扩散模型（hDDM）描绘了精神病性精神障碍中的主动控制和反应控制缺陷。患有精神病的人（PwP；N = 123）、他们的一级亲属（N = 79）和对照组（N = 51）完成了点模式预期任务，该任务允许区分主动控制和反应控制。PwP在主动控制试验中表现出较慢的漂移率，表明在主动控制中对线索信息的利用效率较低。在不频繁的刺激序列中，他们还表现出比对照组更长的非决策时间，表明知觉处理较慢。一项可解释的机器学习分析表明，hDDM参数比传统测量方法能更好地区分不同组。通过DDM，我们发现精神病中的认知控制缺陷主要表现为主动控制中运动/知觉时间较慢和证据整合较慢。
