ArticleFrontiers in psychology2026
Construction of a personality trait prediction model based on color perception preference and personalized cognitive intervention pathways in art education.
Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Color perception preference encodes traceable signatures of personality, yet existing approaches to art education seldom translate this link into computationally grounded, individualized instruction. We develop an integrated framework that spans chromatic feature representation, deep-learning-based trait inference, and closed-loop pedagogical intervention. A multidimensional feature system was constructed across HSV and CIE L*a*b* color spaces, combining dominant color centroids, distribution entropy, perceptual contrast, harmony quantification, and an affective coordinate mapping. We propose CA-PPNet, a multi-branch convolutional architecture coupled with multi-head self-attention and five parallel regression heads that jointly predict the Big Five trait dimensions. Trained on 1,247 paired chromatic-personality records, CA-PPNet attained a macro F1 of 0.798 on tertile-binned trait classes and a mean Pearson
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