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Related Experiment Videos

Feature development, object concepts, and the scope slip.

Michael R W Dawson1

  • 1Biological Computation Project, Department of Psychology, University of Alberta, Edmonton, Alberta, Canada T6E 2P9 mdawson@psych.ualberta.ca www.bcp.psych.ualberta.ca/~mike.

The Behavioral and Brain Sciences
|February 5, 2008
PubMed
Summary
This summary is machine-generated.

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Cognitive science faces a challenge balancing fixed architecture with learning new features. Resolving this involves distinguishing object properties from their representations to avoid scope slip.

Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Explanatory cognitive science requires a fixed functional architecture for robust models.
  • Learning systems necessitate the ability to detect and incorporate novel features.
  • A potential conflict arises between architectural stability and adaptive feature detection.

Purpose of the Study:

  • To address the conflict between fixed cognitive architecture and feature learning.
  • To propose a resolution to the identified tension in cognitive science models.
  • To clarify the distinction between object properties and representational properties.

Main Methods:

  • Conceptual analysis of cognitive architecture and feature detection.
  • Examination of scope slip in representational theories.

Related Experiment Videos

  • Theoretical framework development to reconcile fixed and adaptive systems.

    • Main Results:

    • Identified 'scope slip' as a key conceptual error.
    • Demonstrated that properties of objects are distinct from properties of their representations.
    • Proposed that avoiding scope slip resolves the conflict.

    Conclusions:

    • The conflict between fixed architecture and feature learning is resolvable.
    • Distinguishing object properties from representational properties is crucial.
    • This distinction facilitates the development of more flexible and accurate cognitive models.