Nikiforov
Vladimir O.
D.Sc., Prof.
doi: 10.17586/2226-1494-2016-16-1-168-173
INFORMATIONAL MODEL OF MENTAL ROTATION OF FIGURES
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For citation: Lyakhovetskiy V.A., Potapov A.S., Krumina G. Informational model of mental rotation of figures. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2016, vol. 16, no. 1, pp. 168–173.
Abstract
Subject of Study.The subject of research is the information structure of objects internal representations and operations over them, used by man to solve the problem of mental rotation of figures. To analyze this informational structure we considered not only classical dependencies of the correct answers on the angle of rotation, but also the other dependencies obtained recently in cognitive psychology. Method.The language of technical computing Matlab R2010b was used for developing information model of the mental rotation of figures. Such model parameters as the number of bits in the internal representation, an error probability in a single bit, discrete rotation angle, comparison threshold, and the degree of difference during rotation can be changed. Main Results.The model reproduces qualitatively such psychological dependencies as the linear increase of time of correct answers and the number of errors on the angle of rotation for identical figures, "flat" dependence of the time of correct answers and the number of errors on the angle of rotation for mirror-like figures. The simulation results suggest that mental rotation is an iterative process of finding a match between the two figures, each step of which can lead to a significant distortion of the internal representation of the stored objects. Matching is carried out within the internal representations that have no high invariance to rotation angle. Practical Significance.The results may be useful for understanding the role of learning (including the learning with a teacher) in the development of effective information representation and operations on them in artificial intelligence systems.
Acknowledgements. The work has been supported by grant 2013/0021/1DP/1.1.1.2.0/13/APIA/VIAA/001 of the University of Latvia and by the Ministry of Education and Science of the Russian Federation.
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