---
title: "Analogical problem solving — paper by Gick and Holyoak (1980)"
description: "Gick and Holyoak study how people solve a new problem by drawing on a structurally similar situation from a… Cited by 14 papers here."
authors: ["Mary L. Gick", "Keith J. Holyoak"]
year: 1980
venue: "Cognitive Psychology"
source: "https://reasoninglab.psych.ucla.edu/publications/keith-holyoak-publications/"
doi: "10.1016/0010-0285(80)90013-4"
licence: "author's copy (Reasoning Lab, UCLA) — read only"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/gick-1980-analogical-problem-solving/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/gick-1980-analogical-problem-solving/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [1980](https://md.research.elamur.ai/free/en/papers/1980/) / Analogical problem solving

# Analogical problem solving

**Authors:** Mary L. Gick and [Keith J. Holyoak](https://md.research.elamur.ai/free/en/authors/keith-j-holyoak/) · **Year:** 1980 · **Venue:** Cognitive Psychology · **Publisher:** [Elsevier BV](https://md.research.elamur.ai/free/en/publishers/elsevier-bv/) · **Licence:** author's copy (Reasoning Lab, UCLA) — read only

**Links:** [PDF](https://sites.lifesci.ucla.edu/psych-reasoninglab/wp-content/uploads/sites/273/2021/04/Gick-Holyoak1980Analogical-Problem-Solving.pdf) · [OpenAlex](https://openalex.org/W1980169328)

**Citations:** 2,547 citations (OpenAlex, 2026-10-14) · [14](https://md.research.elamur.ai/free/en/papers/gick-1980-analogical-problem-solving/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

> **Note for the library:** The provided PDF extraction contains only page markers (pp. 1–50) and no text. The summary below is based on the paper's widely known content, not on the supplied file. Exact percentages and the experiment count are omitted because they could not be checked against the text. Please re-extract the PDF if precise figures are needed.

## Summary
Gick and Holyoak study how people solve a new problem by drawing on a structurally similar situation from a different domain. Their target task is Duncker's classic radiation problem. A doctor must destroy an inoperable tumor with rays, but rays strong enough to kill it would also destroy the healthy tissue they pass through. In a series of experiments, participants first read a story that served as a source analog. In the main story, a general cannot send his whole army down any single mined road to a fortress, so he splits it into small groups that converge from several directions. Participants then attempted the radiation problem, sometimes thinking aloud. The authors measured how often the analogous "convergence" solution appeared, with and without a hint to use the story. People could map the story onto the problem and transfer its solution, but they often failed to notice the connection unless prompted. The work established a mapping-based account of analogical transfer and became a cornerstone of research on analogy in cognition and AI.

## Key points
- Target problem: Duncker's radiation problem. The analogous solution is several weak rays aimed from different angles that meet at the tumor.
- Source analogs were military stories, chiefly the fortress-attack ("General") story. Variants with different solutions led participants toward correspondingly different answers, which shows that the specific solution structure transferred.
- Without the source story, convergence solutions were rare. Reading the story helped, but the largest gains came when participants were explicitly told to use it.
- The core finding is that retrieving or noticing an analogy is a major bottleneck. Once the link is pointed out, mapping and applying the solution is comparatively easy.
- Analogical problem solving is framed as mapping correspondences between a source and a target representation. Think-aloud protocols showed that this mapping was often partial.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of Michigan](https://md.research.elamur.ai/free/en/universities/university-of-michigan/) — University, Ann Arbor, United States · 94 works · 18,548 citations

📚 Shelves (6): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 19,503 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 18,805 works · [💭 Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/) · 1,673 works · [💭 Experimental and Cognitive Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/experimental-and-cognitive-psychology/) · 950 works · [🧠 Neuroscience](https://md.research.elamur.ai/free/en/shelves/neuroscience/) · 192 works · [🧠 Cognitive Neuroscience](https://md.research.elamur.ai/free/en/shelves/neuroscience/cognitive-neuroscience/) · 191 works

🧭 Topics (1): [🧮 Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/) · 1,948 works

## Cite

Cite as: Mary L. Gick and Keith J. Holyoak. 1980. Analogical problem solving. Cognitive Psychology.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{gick1980analogical,
  title = {{Analogical problem solving}},
  author = {Mary L. Gick and Keith J. Holyoak},
  booktitle = {Cognitive Psychology},
  year = {1980},
  doi = {10.1016/0010-0285(80)90013-4},
  url = {https://doi.org/10.1016/0010-0285(80)90013-4}
}
```

- [next → NETL: A System for Representing and Using Real-World Knowledge](https://md.research.elamur.ai/free/en/papers/fahlman-1980-netl-system-representing-real-world/)
- [↑ back to papers of 1980](https://md.research.elamur.ai/free/en/papers/1980/)

*Updated 3 November 2026*