Online library learning in human visual puzzle solving

2026-03-24

AI Summary

How do humans construct reusable abstractions to enhance learning efficiency in complex tasks under uncertainty about future demands? This study investigates this question through an online visual puzzle experiment, integrating behavioral analysis with a program induction model to uncover how participants dynamically create, select, and reuse intermediate constructs—referred to as helpers. The findings reveal that online library learning is a core mechanism in human problem solving: abstraction shifts from completeness toward reusability and cost-efficiency. The use of helpers significantly increases success rates on challenging puzzles. Computational modeling further demonstrates that the size of the search space predicts problem-solving effort, whereas the length of the ground-truth program correlates only with failure.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Search and Optimization: Heuristic Search

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

When learning a novel complex task, people often form efficient reusable abstractions that simplify future work, despite uncertainty about the future. We study this process in a visual puzzle task where participants define and reuse helpers -- intermediate constructions that capture repeating structure. In an online experiment, participants solved puzzles of increasing difficulty. Early on, they created many helpers, favouring completeness over efficiency. With experience, helper use became more selective and efficient, reflecting sensitivity to reuse and cost. Access to helpers enabled participants to solve puzzles that were otherwise difficult or impossible. Computational modelling shows that human decision times and number of operations used to complete a puzzle increase with search space estimated by a program induction model with library learning. In contrast, raw program length predicts failure but not effort. Together, these results point to online library learning as a core mechanism in human problem solving, allowing people to flexibly build, refine, and reuse abstractions as task demands grow.

Problem

• online library learning
• visual puzzle solving
• abstraction
• reuse
• human problem solving

Innovation

• online library learning
• program induction
• cognitive abstraction
• visual puzzle solving
• reuse of helpers