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The (Spatial) Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition

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Solving geometric analogy problems through two-stage analogical mapping.

Andrew Lovett¹, Emmett Tomai, Kenneth Forbus

¹Qualitative Reasoning Group, Northwestern University.

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|May 19, 2011

Summary

This summary is machine-generated.

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Area of Science:

Cognitive Science
Artificial Intelligence
Computational Psychology

Background:

Evans' 1968 ANALOGY system pioneered computer models of analogy.
Geometric analogy tasks remain a challenge for computational models.

Purpose of the Study:

To demonstrate that the structure-mapping model of analogy, enhanced with visual processing, can solve geometric analogy problems.
To propose a general-purpose computational framework for analogy.

Main Methods:

Utilized the CogSketch system for visual structure computation.
Employed the Structure Mapping Engine (SME) for analogical matching.
Modeled second-order analogies over differences between pictures.

Main Results:

The combined model successfully solved geometric analogy problems previously addressed by ANALOGY.
Demonstrated the general-purpose nature of the computational components (CogSketch and SME).
Showed alignment with human data regarding chosen answers and reaction times.

Conclusions:

The structure-mapping model, integrated with high-level visual processing, provides a robust approach to geometric analogy.
Structural alignment plays a key role in mental rotation and visual matching.
The model offers an elegant and effective computational solution for geometric analogy tasks, validated by human performance data.