Background and Hypothesis Cognitive control deficits are prominent in individuals with psychotic psychopathology. Studies providing evidence for deficits in proactive control generally examine average performance and not variation across trials for individuals—potentially obscuring detection of essential contributors to cognitive control. Here, we leverage intertrial variability through drift-diffusion models (DDMs) aiming to identify key contributors to cognitive control deficits in psychosis. Study Design People with psychosis (PwP; N = 122), their first-degree biological relatives (N = 78), and controls (N = 50) each completed 120 trials of the dot pattern expectancy (DPX) cognitive control task. We fit full hierarchical DDMs to response and reaction time (RT) data for individual trials and then used classification models to compare the DDM parameters with conventional measures of proactive and reactive control. Study Results PwP demonstrated slower drift rates on proactive control trials suggesting less efficient use of cue information. Both PwP and relatives showed protracted nondecision times to infrequent trial sequences suggesting slowed perceptual processing. Classification analyses indicated that DDM parameters differentiated between the groups better than conventional measures and identified drift rates during proactive control, nondecision time during reactive control, and cue bias as most important. DDM parameters were associated with real-world functioning and schizotypal traits. Conclusions Modeling of trial-level data revealed that slow evidence accumulation and longer preparatory periods are the strongest contributors to cognitive control deficits in psychotic psychopathology. This pattern of atypical responding during the DPX is consistent with shallow basins in attractor dynamic models that reflect difficulties in maintaining state representations, possibly mediated by excess neural excitation or poor connectivity.

中文翻译：

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通过漂移扩散模型和吸引子动力学阐明精神病的认知控制缺陷

背景和假设 认知控制缺陷在患有精神病的个体中很突出。为主动控制缺陷提供证据的研究通常会检查个体的平均表现，而不是试验中的差异——这可能会掩盖对认知控制的重要贡献者的检测。在这里，我们通过漂移扩散模型（DDM）利用试验间变异性，旨在确定精神病认知控制缺陷的关键因素。研究设计 精神病患者 (PwP；N = 122)、其一级生物学亲属 (N = 78) 和对照组 (N = 50) 各完成 120 次点模式预期 (DPX) 认知控制任务试验。我们将完整的分层 DDM 与各个试验的响应和反应时间 (RT) 数据进行拟合，然后使用分类模型将 DDM 参数与主动和反应控制的传统措施进行比较。研究结果 PwP 在主动控制试验中表现出较慢的漂移率，表明提示信息的使用效率较低。PwP 和亲属都对不频繁的试验序列表现出长时间的不做决定的时间，这表明知觉处理速度减慢。分类分析表明，DDM 参数比传统测量方法更好地区分各组，并确定主动控制期间的漂移​​率、反应控制期间的非决策时间以及线索偏差是最重要的。DDM 参数与现实世界的功能和精神分裂特征相关。结论 试验级数据建模表明，缓慢的证据积累和较长的准备期是导致精神病性精神病理学认知控制缺陷的最强因素。DPX 期间的这种非典型响应模式与吸引子动力学模型中的浅盆地一致，反映了维持状态表征的困难，可能是由过度的神经兴奋或不良的连通性介导的。