aiqbench analysis
This study demonstrates that the choice of representation learning objective fundamentally alters the latent structures recovered from psychometric data, revealing distinct trade-offs between retrieval accuracy and behavioral phenotype preservation. The findings highlight a critical nuance for machine learning practitioners: optimizing for one specific task (e.g., teacher-child matching) may inadvertently obscure other meaningful organizational patterns inherent in the dataset. Ultimately, the research underscores that there is no universal latent organization; rather, the mathematical objective function dictates which aspects of reality the model prioritizes.
Different representation learning objectives recover distinct latent structures from the same psychometric data
arXiv:2609.00100v1 Announce Type: cross Abstract: Psychometric questionnaires contain rich item-level information, yet it remains unclear whether different representation learning objectives recover the same latent organization. We investigated this question using 757 matched teacher-child pairs from the baseline assessment of the Cyprus ProW preschool trial. Behavioral structure was characterized from child SDQ, ASBI, and CBRS item responses using principal component analysis and clustering, yielding four behavioral phenotypes. A contrastive objective substantially improved teacher-child retrieval relative to PCA-based representations, increasing Top-1 accuracy from 0.13% to 7.27% and Top-10 accuracy from 1.98% to 56.14%. However, contrastive representations preserved behavioral phenotype structure less effectively than PCA-based representations. A multi-task objective jointly optimizing alignment and behavioral prediction partially restored behavioral organization but reduced retrieval performance. These findings indicate that teacher-child correspondence and behavioral phenotypes represent distinct forms of latent organization and demonstrate that the latent structure recovered from linked psychometric data depends on the representation learning objective.
Abstract:Psychometric questionnaires contain rich item-level information, yet it remains unclear whether different representation learning objectives recover the same latent organization. We investigated this question using 757 matched teacher-child pairs from the baseline assessment of the Cyprus ProW preschool trial. Behavioral structure was characterized from child SDQ, ASBI, and CBRS item responses using principal component analysis and clustering, yielding four behavioral phenotypes. A contrastive objective substantially improved teacher-child retrieval relative to PCA-based representations, increasing Top-1 accuracy from 0.13% to 7.27% and Top-10 accuracy from 1.98% to 56.14%. However, contrastive representations preserved behavioral phenotype structure less effectively than PCA-based representations. A multi-task objective jointly optimizing alignment and behavioral prediction partially restored behavioral organization but reduced retrieval performance. These findings indicate that teacher-child correspondence and behavioral phenotypes represent distinct forms of latent organization and demonstrate that the latent structure recovered from linked psychometric data depends on the representation learning objective.
Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Methodology (stat.ME)
arXiv:2609.00100 [cs.AI]
(or arXiv:2609.00100v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.00100 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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