Davies, J., Nersessian, N. J., & Goel, A. K. jimmyd/research/visual-analogy/ March 2001 1 Visual...

Davies, J., Nersessian, N. J., & Goel, A. K. http://www.cc.gatech.edu/~jimmyd/research/visual-analogy/ March 2001

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Visual Analogy in Scientific Discovery

Jim Davies, Nancy J. Nersessian, Ashok K. Goel {jimmyd, nancyn, goel}@cc.gatech.edu

Program in Cognitive Science

Georgia Institute of Technology


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Outline

• Background: where these ideas are coming from

• Our computational analysis• How our visual analogy theory

contributes back to cognitive science


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Background: Cognitive-historical Analysis

• Focuses on the creation, change, and communication of representations of nature (Nersessian 1994).

• Sources of data for cognitive history are things like: diaries, notebooks, publications, correspondence, equipment, drawings, diagrams, and pedagogical notes.


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Cognitive-historical Analysis of James Clerk Maxwell

• Nersessian did an extensive study of Maxwell’s writings and his context

• She reviewed the relevant psychological research (e.g. Analogy, mental models)

• This lead to hypotheses about conceptual change and scientific reasoning

• Conceptual change as problem solving


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James Clerk Maxwell


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Maxwell’s Subsequent Model


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Our Hypothesis: Generic Abstraction


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What Is the Nature of the Source Analog?

• The solution very much looks like an idle wheel from a gear system.

• Maxwell’s knowledge of gear systems allowed him to generate an abstract model of spinning wheels with idle wheels between them.


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Gear System Retrieval Hypothesis

• This generic abstraction representation visually resembled the model of the gears system in memory.


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Computational Models

• Bhatta & Goel (1997).– Computational work on generic

abstraction, e.g. Generic Teleological Mechanisms for devices

• Griffith et al. (2000). – Analysis of a problem solving protocol


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System: Galatea

• Davies & Goel (2001) created a model (Galatea) of visual analogical problem solving for Duncker’s radiation/tumor problem.


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This Work

• This work combines the Nersessian’s Cognitive-Historical analysis of the Maxwell case with the computational theory of visual analogy from Davies and Goel.

• The computational theory will flesh out, contribute to, and test our hypotheses about Maxwell’s case.


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Visual Analogy

• Visual analogy is analogy with visual elements


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Symbols Are Mapped


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Primitive Visualization Language (Privlan)

• Primitive visual elements (privels)– Circle, line, generic visual element

• Primitive visual transformations (privits)– Add-component, decompose-line, move

• Symbolic images (simages)


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Privels

• Circle (size, location)• Line (thickness, start point, end

point)• Generic-Visual-Element (size,

location)


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Privits

• Decompose-line (object, number)• Add-component (kind)• Move (object, new-location)• Put-between (object, object1,

object2)


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System: Galatea


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Maxwell’s Case


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Cognitive Contributions

• Our computational theory of visual analogy has been applied to two examples, supporting Privlan and the simage representation structure.

• We conjecture that visual representations and generic abstractions are useful for a wide variety of problem-solving instances, within scientific discovery and without.


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Conclusions

• The cognitive-historical approach can contribute to our understanding of general cognitive processes

• Visual analogy was generative in the development of Maxwell’s models

• Visual analogy can be useful for problem solving• Privlan provides a useful level of abstraction for

analogy


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Thank you

• http://www.cc.gatech.edu/~jimmyd/research /visual-analogy/

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