Modeling visual problem solving as analogical reasoning

Andrew Lovett*, Kenneth D Forbus

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

43 Scopus citations

Abstract

We present a computational model of visual problem solving, designed to solve problems from the Raven's Progressive Matrices intelligence test. The model builds on the claim that analogical reasoning lies at the heart of visual problem solving, and intelligence more broadly. Images are compared via structure mapping, aligning the common relational structure in 2 images to identify commonalities and differences. These commonalities or differences can themselves be reified and used as the input for future comparisons. When images fail to align, the model dynamically rerepresents them to facilitate the comparison. In our analysis, we find that the model matches adult human performance on the Standard Progressive Matrices test, and that problems which are difficult for the model are also difficult for people. Furthermore, we show that model operations involving abstraction and rerepresentation are particularly difficult for people, suggesting that these operations may be critical for performing visual problem solving, and reasoning more generally, at the highest level.

Original languageEnglish (US)
Pages (from-to)60-90
Number of pages31
JournalPsychological Review
Volume124
Issue number1
DOIs
StatePublished - Jan 1 2017

Keywords

  • Analogy
  • Cognitive modeling
  • Problem solving
  • Visual comparison

ASJC Scopus subject areas

  • Psychology(all)

Fingerprint Dive into the research topics of 'Modeling visual problem solving as analogical reasoning'. Together they form a unique fingerprint.

Cite this