通过分层结构引导模型在数据少时像人一样高效学习。
Hierarchical Scaffolding Enables Human-Like Cognitive Selectivity under Data Scarcity
- 用分层框架从粗到细构建概念，模仿人类认知过程。
- 数据稀缺下准确率显著提升，类内差异变小，语义区分更清晰。
- 适合低资源场景下的模型训练，尤其对新类别泛化能力强。
现代机器学习系统需要大量数据进行视觉识别，而人类在数据严重受限的情况下仍能高效学习，通常先掌握广泛的类别结构，再细化细微差别。受此启发，我们提出SCALA（Scaffolded Cognitive Architecture for Learning under limited dAta），一种基于认知心理学的分层学习框架，引导模型从粗略的概念结构逐步过渡到精细识别。该模型表现出类人的认知选择性，能有效聚焦任务相关特征并抑制背景干扰，引发表征学习的根本性转变：簇形成加速、类内离散度降低、语义可分性增强。实验证明，SCALA在极端数据稀缺条件下实现了显著的准确率提升；同时，这种分层引导提升了对未见类别的鲁棒泛化能力，并加快了新类别习得速度。综合来看，SCALA为在数据受限环境中实现人类级样本效率和强健类别泛化提供了有力框架。
原文摘要 · Abstract (English)
Modern machine learning systems demand extensive datasets for visual recognition. Conversely, humans learn with high efficiency despite severe data limitations, often by acquiring broad categorical structures before refining finer distinctions. Inspired by this contrast, we introduce SCALA (Scaffolded Cognitive Architecture for Learning under limited dAta), a hierarchical learning framework grounded in cognitive psychology that guides models from coarse conceptual structures to fine-grained recognition. Our model exhibits human-like cognitive selectivity by effectively prioritizing task-relevant features while suppressing background distractors, a mechanism that induces a fundamental shift in representation learning. This shift is characterized by accelerated cluster formation, reduced intra-class dispersion, and enhanced semantic separability. Empirically, SCALA achieves significant accuracy improvements under severe data scarcity. Furthermore, this hierarchical scaffolding promotes robust generalization to unseen classes and accelerates the acquisition of novel categories. Collectively, our results establish SCALA as a powerful framework for achieving human-level sample efficiency and resilient category generalization in data-constrained environments.
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