Department of Psychology, Center for Brain Sciences, Harvard University, Cambridge, MA, USA.
Department of Psychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
Nat Hum Behav. 2024 May;8(5):917-931. doi: 10.1038/s41562-024-01814-x. Epub 2024 Feb 8.
Computational phenotyping has emerged as a powerful tool for characterizing individual variability across a variety of cognitive domains. An individual's computational phenotype is defined as a set of mechanistically interpretable parameters obtained from fitting computational models to behavioural data. However, the interpretation of these parameters hinges critically on their psychometric properties, which are rarely studied. To identify the sources governing the temporal variability of the computational phenotype, we carried out a 12-week longitudinal study using a battery of seven tasks that measure aspects of human learning, memory, perception and decision making. To examine the influence of state effects, each week, participants provided reports tracking their mood, habits and daily activities. We developed a dynamic computational phenotyping framework, which allowed us to tease apart the time-varying effects of practice and internal states such as affective valence and arousal. Our results show that many phenotype dimensions covary with practice and affective factors, indicating that what appears to be unreliability may reflect previously unmeasured structure. These results support a fundamentally dynamic understanding of cognitive variability within an individual.
计算表型已成为一种强大的工具，可用于描述各种认知领域的个体变异性。个体的计算表型定义为从对行为数据进行拟合的计算模型中获得的一组具有机械解释力的参数。然而，这些参数的解释关键取决于它们的心理计量特性，而这些特性很少被研究。为了确定控制计算表型时间变异性的来源，我们使用一系列七项任务进行了为期 12 周的纵向研究，这些任务测量了人类学习、记忆、感知和决策的各个方面。为了检查状态效应的影响，每周参与者都要提供跟踪他们的情绪、习惯和日常活动的报告。我们开发了一个动态计算表型框架，该框架使我们能够区分练习和内部状态（如情感效价和唤醒度）的时变效应。我们的研究结果表明，许多表型维度与练习和情感因素相关，这表明看似不可靠的因素可能反映了以前未测量到的结构。这些结果支持了个体认知变异性的基本动态理解。
