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.

翻译：理解人类和机器如何从稀疏数据中学习是认知科学与机器学习的核心问题。本研究采用物种公平设计，在少样本半监督类别学习任务中比较儿童与卷积神经网络（CNNs）的表现。两种学习者在完全相同的条件下接触新物体类别：在学习过程中接收混合的标记与未标记样本，同时我们系统操纵监督条件（1/3/6个标签）、目标特征（尺寸、形状、纹理）与感知对齐程度（高/低）。研究发现，儿童能够从极少量标签中快速泛化，但表现出强烈的特征特异性偏好和对齐敏感性。CNNs则呈现不同的交互模式：增加监督能提升性能，但对齐程度与特征结构均会调节额外监督对学习效果的影响强度。这些结果表明，人类与模型的比较必须在恰当条件下进行，应重点关注监督、特征结构和对齐三者间的交互作用，而非单纯比较整体准确率。