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Experimental Psychology (Russia)

Publisher: Moscow State University of Psychology and Education

ISSN (printed version): 2072-7593

ISSN (online): 2311-7036

DOI: http://dx.doi.org/10.17759/exppsy

License: CC BY-NC 4.0

Started in 2008

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Simple neuro network algorithms for evaluating latent links of younger adolescent’s psychological characteristics 129

Slavutskaya E.V., Doctor of Psychology, professor of Psychology and Social Pedagogic department, Chuvash State Pedagogical University of I.Ya. Yakovlev, Cheboksary, Russia, slavutskayaev@gmail.com
Abrukov V.S., PhD in Physics and Matematics, Head of Department of Applied Physics and Nanotechnology, Chuvash State University, Cheboksary, Russia, abrukov@yandex.ru
Slavutskii L.A., PhD in Physics and Matematics, Professor of the Automatics and Control department, Chuvash State University, Cheboksary, Russia, lenya@slavutskii.ru
Abstract
The artificial neural networks (ANN) for the psycho-diagnostics data analyzing is used. It is shown that the training of a simple ANN of direct propagation, as the problem of nonlinear multi-parameter optimization, allows to carry out the vertical system analysis and to assess the latent, non-linear relationship between different level’s psychological characteristics (the system of relationships, motivational characteristics, personality traits, intelligence, the type of nervous system). The detection of such links using the traditional for psychology the correlative ore factor analysis is difficult. Quantitative criteria are proposed for evaluating the quality of ANN algorithms, which are based on a scattering diagram and the statistical distribution of errors in the learning and testing of a neural network. As an example, the data of psycho-diagnostics of younger adolescents are analyzed. The proposed algorithms and criteria made it possible to detect latent links between psychological characteristics, to evaluate the ratio of psychological level-based indicators.

Keywords: younger adolescents, psychological characteristics, latent links, artificial neural networks, neural network algorithms

Column: Research Methods

DOI: http://dx.doi.org/10.17759/exppsy.2019120210

For Reference

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