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Online library learning in human visual puzzle solving

Pinzhe Zhao, Emanuele Sansone, Marta Kryven, Bonan Zhao

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.

Online library learning in human visual puzzle solving

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.
Paper Structure (23 sections, 2 equations, 7 figures)

This paper contains 23 sections, 2 equations, 7 figures.

Table of Contents

  1. Introduction
  2. Background
  3. Problem solving as program induction
  4. Library learning
  5. Visual puzzles
  6. Behavioral Paradigm: Pattern Builder
  7. Building patterns by composing programs
  8. Creating and using helpers
  9. Free play
  10. Computational Models
  11. Bottom-up search over primitives
  12. Online library learning
  13. Human-model comparison
  14. Behavioral Study
  15. Experiment
  16. ...and 8 more sections

Figures (7)

  • Figure 1: Task interface. A. Starting view of a trial. Left: Total points so far and the target shape for this trial. Middle: Work space, matching the target shape with the provided Operations and Primitive shapes. Right: List of steps, each step corresponds to one line of program. Black borders are only added in the paper for illustration. B. Example preview. C. Example programs. Each line has a line number, a thumbnail of the pattern that line creates, and the corresponding program (operations over primitives, steps, or helpers). D. The initial helper space. E. Helper space with example helpers.
  • Figure 2: Target patterns used in the Experiment.
  • Figure 3: Accuracy across 14 target patterns. Bars and standard errors for human participants. Colored dots for models.
  • Figure 4: Helper usage across 14 target patterns. A. Number of helpers created at each trial. B. Proportion of solution steps using saved helpers (blue) overlaid with ground-truth program length (red).
  • Figure 5: Helpers created by participants. Each row shows the patterns most frequently saved as helpers for that trial, ranked by popularity. Darker cell color indicates higher popularity. Only patterns saved by more than one participant are shown. Orange borders are for helpers saved by Library models.
  • ...and 2 more figures