Department of Psychology, University of Minnesota, Minneapolis, MN, USA.
Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA.
Schizophr Bull. 2024 Nov 8;50(6):1357-1370. doi: 10.1093/schbul/sbae014.
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.
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.
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.
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.
认知控制缺陷在精神病理学患者中较为突出。提供认知控制前摄性控制缺陷证据的研究通常会检查个体的平均表现，而不会检查个体试验之间的变化，从而可能掩盖对认知控制的关键贡献因素的检测。在这里，我们通过漂移扩散模型（DDM）利用试验间变异性，旨在确定精神分裂症认知控制缺陷的关键因素。
精神分裂症患者（PwP；N=122）、他们的一级生物亲属（N=78）和对照组（N=50）各自完成了 120 次点模式预期（DPX）认知控制任务。我们对个体试验的反应和反应时间（RT）数据拟合了完整的分层 DDM，然后使用分类模型将 DDM 参数与传统的前摄性和反应性控制措施进行比较。
PwP 在主动控制试验中表现出较慢的漂移率，表明对线索信息的使用效率较低。PwP 和亲属都表现出对不频繁试验序列的延长非决策时间，表明感知处理速度减慢。分类分析表明，DDM 参数比传统措施更好地区分了组间差异，并确定了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最为重要。DDM 参数与现实世界的功能和精神分裂症特质相关。
对试验级数据的建模表明，证据积累缓慢和预备期延长是精神病理学中认知控制缺陷的最强贡献因素。DPX 中的这种异常反应模式与吸引子动力学模型中的浅盆地一致，反映了维持状态表示的困难，可能由过度神经兴奋或连接不良介导。
