Hierarchical Scaffolding Enables Human-Like Cognitive Selectivity under Data Scarcity
2026-07-06 • Machine Learning
Machine LearningComputer Vision and Pattern Recognition
AI summaryⓘ
The authors created a new learning method called SCALA that helps computers recognize things better with very little data. Inspired by how humans learn, their system first looks at broad groups before focusing on small details. This approach helps the model ignore irrelevant background information and group similar things more clearly. As a result, their method improves accuracy when data is limited and helps the model quickly learn new categories it hasn't seen before.
machine learningvisual recognitionhierarchical learningdata scarcitycognitive psychologyfeature selectioncluster formationsemantic separabilitygeneralizationsample efficiency
Authors
Juhyoung Park, Jaehyuk Bae, Hyeonbo Yang, Se-Bum Paik
Abstract
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.