Online cognitive tasks are gaining traction as scalable and cost-effective alternatives to traditional supervised assessments. However, variability in peoples’ home devices, visual and motor abilities, and speed-accuracy biases confound the specificity with which online tasks can measure cognitive abilities. To address these limitations, we developed IDoCT (Iterative Decomposition of Cognitive Tasks), a method for estimating domain-specific cognitive abilities and trial-difficulty scales from task performance timecourses in a data-driven manner while accounting for device and visuomotor latencies, unspecific cognitive processes and speed-accuracy trade-offs. IDoCT can operate with any computerised task where cognitive difficulty varies across trials. Using data from 388,757 adults, we show that IDoCT successfully dissociates cognitive abilities from these confounding factors. The resultant cognitive scores exhibit stronger dissociation of psychometric factors, improved cross-participants distributions, and meaningful demographic’s associations. We propose that IDoCT can enhance the precision of online cognitive assessments, especially in large scale clinical and research applications.

在线认知任务作为传统监督评估的可扩展且具有成本效益的替代方案，越来越受到关注。然而，人们家用设备、视觉和运动能力的可变性以及速度准确性偏差混淆了在线任务可以衡量认知能力的特异性。为了解决这些限制，我们开发了 IDoCT（认知任务的迭代分解），这是一种以数据驱动的方式从任务执行时间过程中估计特定领域的认知能力和试验难度量表的方法，同时考虑设备和视觉运动延迟、非特异性认知过程和速度-准确性权衡。IDoCT 可以处理任何计算机化任务，其中认知难度因试验而异。使用来自 388,757 名成年人的数据，我们表明 IDoCT 成功地将认知能力与这些混杂因素分开。由此产生的认知分数表现出更强的心理测量因素分离、改进的交叉参与者分布和有意义的人口统计学关联。我们建议 IDoCT 可以提高在线认知评估的准确性，尤其是在大规模临床和研究应用中。