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.
类别学习中的特征、对齐与监督：儿童与神经网络的比较研究 / Feature, Alignment, and Supervision in Category Learning: A Comparative Approach with Children and Neural Networks
这项研究通过公平的实验设计比较了儿童和卷积神经网络在少量标签下的类别学习能力，发现儿童能快速从极少的标签中学习但受特定特征和感知对齐的强烈影响，而神经网络则更多依赖于增加监督，其学习效果受特征结构和对齐的调节，表明比较人类与模型必须在考虑监督、特征和对齐三者交互的具体条件下进行。
源自 arXiv: 2602.03124
