Idiographic Personality Gaussian Process for Psychological Assessment
Yehu Chen, Muchen Xi, Jacob Montgomery, Joshua Jackson, Roman Garnett

TL;DR
This paper introduces the idiographic personality Gaussian process (IPGP), a novel model that captures both shared and individual-specific personality traits, improving prediction and personalized diagnosis in psychometrics.
Contribution
The paper proposes the IPGP framework, combining Gaussian process coregionalization with non-Gaussian ordinal data handling, enabling scalable individualized personality assessment.
Findings
IPGP outperforms benchmarks in response prediction
IPGP accurately estimates personalized trait structures
IPGP identifies meaningful personality clusters
Abstract
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…
Peer Reviews
Decision·NeurIPS 2024 poster
The paper is very well-written. It brings an interesting application to life and convinces the reader that the proposed modeling approach is well-tailored to the problem at hand. The paper gives a clear review of the prerequisite concepts and presents its modeling approach clearly. The paper covers related work in psychometrics well is convincing that the modeling approach is novel within that applied community. Experiments bear out this claim, as the proposed model performs much better than m
The paper is vague about its technical contributions. It makes the following hedged novelty statement: "the first multi-task GP latent variables model _for dynamic idiographic assessment_". I took this to mean that the proposed approach is not very new technically, but it has never yet been applied to dynamic idiographic assessments. However, the paper also makes the general claim that it "advances the literatures on Gaussian process latent variable models...". I am of the opinion that tailoring
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Taxonomy
TopicsMental Health Research Topics · Cognitive Science and Mapping
MethodsGaussian Process · Variational Inference
