Hierarchical Scaffolding Enables Human-Like Cognitive Selectivity under Data Scarcity

arXiv:2607.04709v1 Announce Type: new 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 sel

Source: arXiv cs.LG — read the full report at the original publisher.

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