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Online library learning in human visual puzzle solving

Pinzhe Zhao, Emanuele Sansone, Marta Kryven, Bonan Zhao · Mar 24, 2026 · Citations: 0

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How to use this page

Provisional trust

This page is a lightweight research summary built from the abstract and metadata while deeper extraction catches up.

Best use

Background context only

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Evidence quality

Provisional

Derived from abstract and metadata only.

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.

Abstract-only analysis — low confidence

All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.

  • This page is still relying on abstract and metadata signals, not a fuller protocol read.
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Should You Rely On This Paper?

Signal extraction is still processing. This page currently shows metadata-first guidance until structured protocol fields are ready.

Best use

Background context only

Use if you need

A provisional background reference while structured extraction finishes.

Main weakness

This page is still relying on abstract and metadata signals, not a fuller protocol read.

Trust level

Provisional

Usefulness score

Unavailable

Eval-fit score is unavailable until extraction completes.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Weak / implicit signal

Usefulness for eval research

Provisional (processing)

Extraction confidence 0%

If you are doing eval pipeline work, start here:

Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub Tool-Use Eval Hub

What We Could Verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

provisional (inferred)

None explicit

No explicit feedback protocol extracted.

"When learning a novel complex task, people often form efficient reusable abstractions that simplify future work, despite uncertainty about the future."

Evaluation Modes

provisional (inferred)

None explicit

Validate eval design from full paper text.

"When learning a novel complex task, people often form efficient reusable abstractions that simplify future work, despite uncertainty about the future."

Quality Controls

provisional (inferred)

Not reported

No explicit QC controls found.

"When learning a novel complex task, people often form efficient reusable abstractions that simplify future work, despite uncertainty about the future."

Benchmarks / Datasets

provisional (inferred)

Not extracted

No benchmark anchors detected.

"When learning a novel complex task, people often form efficient reusable abstractions that simplify future work, despite uncertainty about the future."

Reported Metrics

provisional (inferred)

Not extracted

No metric anchors detected.

"When learning a novel complex task, people often form efficient reusable abstractions that simplify future work, despite uncertainty about the future."

Rater Population

provisional (inferred)

Unknown

Rater source not explicitly reported.

"When learning a novel complex task, people often form efficient reusable abstractions that simplify future work, despite uncertainty about the future."

Human Feedback Details

This page is using abstract-level cues only right now. Treat the signals below as provisional.

  • Potential human-data signal: No explicit human-data keywords detected.
  • Potential benchmark anchors: No benchmark names detected in abstract.
  • Abstract highlights: 3 key sentence(s) extracted below.

Evaluation Details

Evaluation fields are inferred from the abstract only.

  • Potential evaluation modes: No explicit eval keywords detected.
  • Potential metric signals: No metric keywords detected.
  • Confidence: Provisional (metadata-only fallback).

Research Brief

Metadata summary

When learning a novel complex task, people often form efficient reusable abstractions that simplify future work, despite uncertainty about the future.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

  • 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.

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended Queries

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