Department of Psychology, University of Virginia, Charlottesville, VA, USA.
Department of Psychology, The Ohio State University, Columbus, OH, USA.
Behav Res Methods. 2021 Oct;53(5):1833-1856. doi: 10.3758/s13428-020-01534-w. Epub 2021 Feb 18.
Although there have been major strides toward uncovering the neurobehavioral mechanisms involved in cognitive functions like memory and decision making, methods for measuring behavior and accessing latent processes through computational means remain limited. To this end, we have created SUPREME (Sensing to Understanding and Prediction Realized via an Experiment and Modeling Ecosystem): a toolbox for comprehensive cognitive assessment, provided by a combination of construct-targeted tasks and corresponding computational models. SUPREME includes four tasks, each developed symbiotically with a mechanistic model, which together provide quantified assessments of perception, cognitive control, declarative memory, reward valuation, and frustrative nonreward. In this study, we provide validation analyses for each task using two sessions of data from a cohort of cognitively normal participants (N = 65). Measures of test-retest reliability (r: 0.58-0.75), stability of individual differences (ρ: 0.56-0.70), and internal consistency (α: 0.80-0.86) support the validity of our tasks. After fitting the models to data from individual subjects, we demonstrate each model's ability to capture observed patterns of behavioral results across task conditions. Our computational approaches allow us to decompose behavior into cognitively interpretable subprocesses, which we can compare both within and between participants. We discuss potential future applications of SUPREME, including clinical assessments, longitudinal tracking of cognitive functions, and insight into compensatory mechanisms.
尽管在揭示认知功能（如记忆和决策制定）所涉及的神经行为机制方面已经取得了重大进展，但通过计算手段测量行为和获取潜在过程的方法仍然有限。为此，我们创建了 SUPREME（通过实验和建模生态系统实现的感知、理解和预测）：一个全面的认知评估工具包，由针对特定结构的任务和相应的计算模型相结合提供。SUPREME 包括四个任务，每个任务都与一个机械模型协同开发，这些任务共同提供了感知、认知控制、陈述性记忆、奖励估值和挫折性非奖励的量化评估。在这项研究中，我们使用来自认知正常参与者队列的两个数据会话（N = 65）对每个任务进行验证分析。测试-重测信度（r：0.58-0.75）、个体差异稳定性（ρ：0.56-0.70）和内部一致性（α：0.80-0.86）的衡量标准支持了我们任务的有效性。在将模型拟合到个体受试者的数据后，我们展示了每个模型捕捉到行为结果在任务条件下的观察模式的能力。我们的计算方法使我们能够将行为分解为认知上可解释的子过程，我们可以在参与者内部和之间进行比较。我们讨论了 SUPREME 的潜在未来应用，包括临床评估、认知功能的纵向跟踪以及对补偿机制的深入了解。
