Feature, Alignment, and Supervision in Category Learning: A Comparative Approach with Children and Neural Networks

• URL: http://arxiv.org/abs/2602.03124v1
• Date: Tue, 03 Feb 2026 05:31:06 GMT
• Title: Feature, Alignment, and Supervision in Category Learning: A Comparative Approach with Children and Neural Networks
• Authors: Fanxiao Wani Qiu, Oscar Leong,
• Abstract summary: We compare children and convolutional neural networks (CNNs) in a few-shot semi-supervised category learning task.<n>Children generalize rapidly from minimal labels but show strong feature-specific biases and sensitivity to alignment.<n>CNNs show a different interaction profile: added supervision improves performance, but both alignment and feature structure moderate the impact additional supervision has on learning.
• Score: 4.681760167323748
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding how humans and machines learn from sparse data is central to cognitive science and machine learning. Using a species-fair design, we compare children and convolutional neural networks (CNNs) in a few-shot semi-supervised category learning task. Both learners are exposed to novel object categories under identical conditions. Learners receive mixtures of labeled and unlabeled exemplars while we vary supervision (1/3/6 labels), target feature (size, shape, pattern), and perceptual alignment (high/low). We find that children generalize rapidly from minimal labels but show strong feature-specific biases and sensitivity to alignment. CNNs show a different interaction profile: added supervision improves performance, but both alignment and feature structure moderate the impact additional supervision has on learning. These results show that human-model comparisons must be drawn under the right conditions, emphasizing interactions among supervision, feature structure, and alignment rather than overall accuracy.

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