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BSDT ROC and Cognitive Learning Hypothesis

  • Petro Gopych
  • Ivan Gopych
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 85)

Abstract

For sensory data processing and decision-making, recent binary signal detection theory (BSDT) defines a common neural space. Thanks to this conceptual advance, it enables to find some brain’s internal parameters. Here a methodology for BSDT analysis of measured ROC curves has been developed and applied to the fitting of empirical data. It has been demonstrated that BSDT leads naturally to a cognitive (motivational) learning (‘learning-to-be-certain’) hypothesis, describes successfully ROCs of any form, supports semi-representational memory architectures and predicts a kind of an irremovable physiology-behavior or brain-mind uncertainty. Results provide a ground for designing biologically inspired intelligent high performance codes and devices.

Keywords

False Alarm Psychometric Function Decision Confidence Sensory Data Processing Neural Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Petro Gopych
    • 1
  • Ivan Gopych
    • 2
  1. 1.Universal Power Systems USA-Ukraine LLCKharkivUkraine
  2. 2.Kharkiv Regional Clinical Oncology CentreKharkivUkraine

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