Mental bootstrapping enables human-level concept learning in self-supervised deep models
TL;DR
A self-supervised framework that trains models on systematically simplified versions of abstract reasoning tasks containing incomplete but structured concept cues enables models to form internal abstractions under limited resources and later apply them to more complex problems.
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.