The adaptation of experimental cognitive tasks into measures that can be used to quantify neurocognitive outcomes in translational studies and clinical trials has become a key component of the strategy to address psychiatric and neurological disorders. Unfortunately, while most experimental cognitive tests have strong theoretical bases, they can have poor psychometric properties, leaving them vulnerable to measurement challenges that undermine their use in applied settings. Item response theory–based computerized adaptive testing has been proposed as a solution but has been limited in experimental and translational research due to its large sample requirements. We present a generalized latent variable model that, when combined with strong parametric assumptions based on mathematical cognitive models, permits the use of adaptive testing without large samples or the need to precalibrate item parameters. The approach is demonstrated using data from a common measure of working memory—the N-back task—collected across a diverse sample of participants. After evaluating dimensionality and model fit, we conducted a simulation study to compare adaptive versus nonadaptive testing. Computerized adaptive testing either made the task 36% more efficient or score estimates 23% more precise, when compared to nonadaptive testing. This proof-of-concept study demonstrates that latent variable modeling and adaptive testing can be used in experimental cognitive testing even with relatively small samples. Adaptive testing has the potential to improve the impact and replicability of findings from translational studies and clinical trials that use experimental cognitive tasks as outcome measures.

中文翻译：

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实验认知精神病理学研究的潜变量建模和自适应测试

将实验性认知任务调整为可用于量化转化研究和临床试验中的神经认知结果的措施已成为解决精神疾病和神经系统疾病策略的关键组成部分。不幸的是，虽然大多数实验性认知测试都有强大的理论基础，但它们可能具有较差的心理测量特性，使它们容易受到破坏其在应用环境中使用的测量挑战的影响。基于项目响应理论的计算机化自适应测试已被提出作为一种解决方案，但由于其大样本要求，在实验和转化研究中受到限制。我们提出了一个广义潜变量模型，当结合基于数学认知模型的强参数假设时，允许在没有大样本或需要预先校准项目参数的情况下使用自适应测试。该方法使用来自不同参与者样本的工作记忆的通用测量数据（N-back 任务）进行了演示。在评估维度和模型拟合后，我们进行了一项模拟研究来比较自适应与非自适应测试。与非自适应测试相比，计算机化自适应测试要么使任务效率提高 36%，要么使评分估计精确 23%。这项概念验证研究表明，即使样本相对较小，潜在变量建模和自适应测试也可用于实验性认知测试。