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Values and Bayesian Probabilities of Mental States from BSDT PL Analysis of Memory ROCs

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Mendel 2015 (ICSC-MENDEL 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 378))

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Abstract

BSDT PL is a binary (using the binary signal detection theory, BSDT) version of primary language, PL, an extension of mathematics by the hypothesis of concurrent infinity: all things of the world are describable by one-way infinite binary strings with one-way infinite common beginning. Together with its phenomenology formalization, BSDT PL enables descriptions of any life and mind phenomena with the rigor of mathematics. In this paper, BSDT PL knowledge and knowledge-related notions are defined and applied to designing BSDT PL semi-representational memory for words. By BSDT PL fitting of memory ROC curves measured in healthy humans and patients with injured brains, the values and Bayesian probabilities of mental states serving verbal memory’s items are found. BSDT PL measure of subjectivity of word recognition mechanisms is proposed and numerically estimated. It is demonstrated that statistical learning and Bayesian learning are the constituents of cognitive learning. Applications are briefly discussed.

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Author information

Authors and Affiliations

  1. Universal Power Systems USA-Ukraine LLC, 3 Kotsarskaya St., Kharkiv, 61012, Ukraine

    Petro Gopych

  2. Kharkiv Regional Clinical Oncology Centre, 4 Lisoparkivs’ka St., Kharkiv, 61070, Ukraine

    Ivan Gopych

Authors
  1. Petro Gopych
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  2. Ivan Gopych
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Corresponding author

Correspondence to Petro Gopych .

Editor information

Editors and Affiliations

  1. Faculty of Mechanical Engineering, Department of Applied Computer Science, Brno University of Technology, Brno, Czech Republic

    Radek Matoušek

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Gopych, P., Gopych, I. (2015). Values and Bayesian Probabilities of Mental States from BSDT PL Analysis of Memory ROCs. In: Matoušek, R. (eds) Mendel 2015. ICSC-MENDEL 2016. Advances in Intelligent Systems and Computing, vol 378. Springer, Cham. https://doi.org/10.1007/978-3-319-19824-8_17

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  • DOI: https://doi.org/10.1007/978-3-319-19824-8_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19823-1

  • Online ISBN: 978-3-319-19824-8

  • eBook Packages: EngineeringEngineering (R0)

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