We develop a novel measurement framework based on a Gaussian process coregionalization model to address a long-lasting debate in psychometrics: whether psychological features like personality share a common structure across the population, vary uniquely for individuals, or some combination. We propose the idiographic personality Gaussian process (IPGP) framework, an intermediate model that accommodates both shared trait structure across a population and "idiographic" deviations for individuals. IPGP leverages the Gaussian process coregionalization model to handle the grouped nature of battery responses, but adjusted to non-Gaussian ordinal data. We further exploit stochastic variational inference for efficient latent factor estimation required for idiographic modeling at scale. Using synthetic and real data, we show that IPGP improves both prediction of actual responses and estimation of individualized factor structures relative to existing benchmarks. In a third study, we show that IPGP also identifies unique clusters of personality taxonomies in real-world data, displaying great potential in advancing individualized approaches to psychological diagnosis and treatment.

翻译：我们提出了一种基于高斯过程协同区域化模型的新型测量框架，以解决心理测量学中长期存在的争论：心理特征（如人格）是否在人群中共享共同结构、因人而异，或是两者的某种结合。我们提出了个体人格高斯过程（IPGP）框架，这是一种中间模型，既能容纳人群间共享的特质结构，又能适应个体的“个体性”偏差。IPGP利用高斯过程协同区域化模型处理成套测试反应的分组性质，但调整以适应非高斯序数数据。我们进一步采用随机变分推理，以实现大规模个体建模所需的潜在因子高效估计。通过合成数据和真实数据，我们证明IPGP相较于现有基准方法，在预测实际反应和估计个体化因子结构方面均有提升。在第三项研究中，我们表明IPGP还能在真实数据中识别出独特的人格分类簇，显示出在推进心理诊断与治疗的个体化方法方面的巨大潜力。