High-Dimensional Perception with the Double Machine Learning Lens Model

Abstract Traditional perceptual models are ill-equipped for the high-dimensional data, such as text embeddings, central to modern psychology and AI. We introduce the double machine learning lens model, a framework that utilizes machine learning to handle such data. We applied this model to analyze how a modern AI and human perceivers judge social class from 9,513 aspirational essays written by 11-year-olds in 1969. A systematic comparison of 45 analytical approaches revealed that regularized linear models using dimensionality-reduced language embeddings significantly outperformed traditional dictionary-based methods and more complex non-linear models. Our top model accurately predicted human ( R C V 2 = 0.61 ) $(R^{2}_{CV} =0.61)$ left parenthesis upper R Subscript upper C upper V Superscript 2 Baseline equals 0.61 right parenthesis and AI ( R C V 2 = 0.56 ) $(R^{2}_{CV} =0.56)$ left parenthesis upper R Subscript upper C upper V Superscript 2 Baseline equals 0.56 right parenthesis social class perceptions, capturing over 85% of the total accuracy. These results suggest that “unmodeled knowledge” in perception may be an artifact of insufficient measurement tools rather than an unmeasurable intuitive process. We find that both AI and humans use many of the same textual cues (e.g., grammar, occupations, and cultural activities), only a subset of which are valid. Both appear to amplify subtle, real-world patterns into powerful, yet potentially discriminatory heuristics, where a small difference in actual social class creates a large difference in perception.