Mental bootstrapping enables human-level concept learning in self-supervised deep models.

Authors

Lingxiao Yang, Muyang Lyu, Ying-Jie Wang, Xiaohua Xie, Zonglei Zhen, Jian-Huang Lai, Ru-Yuan Zhang

Published in

Science advances. Volume 12. Issue 35. Pages eaea7202. Aug 28, 2026. Epub Aug 28, 2026.

Abstract

How agents acquire abstract concepts from sparse, diverse examples-often without explicit supervision-remains a central problem in cognitive science and artificial intelligence. Human studies suggest that this ability depends on mental bootstrapping, the gradual construction of complex concepts from simpler partial structures. Building on this idea, we develop a self-supervised framework that trains models on systematically simplified versions of abstract reasoning tasks containing incomplete but structured concept cues. This algorithm enables models to form internal abstractions under limited resources and later apply them to more complex problems. We evaluate the framework across 12 abstract visual reasoning datasets testing in-distribution concept induction, out-of-distribution generalization, and few-shot learning. To contextualize performance, we also measure human accuracy on the same tasks. Models trained on simplified problems generalize robustly, reaching or even surpassing human-level performance. These findings show that abstract reasoning can emerge from structured simplification and minimal data, offering a computational account of concept learning in humans and machines.

PMID:
42664331
Bibliographic data and abstract were imported from PubMed on 29 Aug 2026.

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