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Related Experiment Videos

Automatic classification of dysfunctional thoughts: a feasibility test.

Katja Wiemer-Hastings1, Adrian S Janit, Peter M Wiemer-Hastings

  • 1Department of Psychology, Northern Illinois University, DeKalb 60115, USA. katja@niu.edu

Behavior Research Methods, Instruments, & Computers : a Journal of the Psychonomic Society, Inc
|September 10, 2004
PubMed
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This study presents an automated system for identifying dysfunctional thoughts in cognitive therapy. While effective for some categories, further development is needed for reliable classification across all thought types.

Area of Science:

  • Cognitive Psychology
  • Computational Linguistics
  • Artificial Intelligence in Healthcare

Background:

  • Dysfunctional thoughts are a key target in cognitive therapy.
  • Current methods for identifying these thoughts can be labor-intensive.
  • Automating this process could enhance therapeutic efficiency.

Purpose of the Study:

  • To describe the first version of an automated computer module for classifying dysfunctional thoughts.
  • To develop a component of the COGNO system for automatic feedback on these thoughts.
  • To explore the potential of automatic classification for natural dialogue systems in therapy.

Main Methods:

  • Development of a rule-based computer module.
  • Rules derived from language markers in 149 dysfunctional thoughts.

Related Experiment Videos

  • Testing the module on an independent set of 112 example thoughts.

    • Main Results:

    • The system successfully detects a majority of dysfunctional thoughts.
    • Reliable classification was achieved for specific thought categories only.
    • Performance varied across different types of dysfunctional thoughts.

    Conclusions:

    • Automatic classification of dysfunctional thoughts is feasible.
    • The developed module shows promise but requires further refinement for broader application.
    • This technology could be foundational for future AI-driven cognitive therapy tools.