1996 Fiscal Year Final Research Report Summary
Understanding of Human Perceptual-Alternation Phenomena and its Neural Network Model
| Project/Area Number |
07680410
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | HIMEJI INSTITUTE OF TECHNOLOGY |
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Principal Investigator |
MATSUI Nobuyuki Himeji Institute of Technology Computer Engineering Associate Professor, 工学部, 助教授 (10173783)
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| Co-Investigator(Kenkyū-buntansha) |
TSURUNO Reiji Kyushu Institute of Design Visual Communication Design Assistant Professor, 芸術工学部, 講師 (10197775)
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| Project Period (FY) |
1995 – 1996
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| Keywords | ambiguous figure / visual perception / perceptual-alternation / neural network information processing / Gamma distribution / Chaos / neuron firing-rate / perceptual duration time |
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| Research Abstract |
In this research, we dealt with the phenomena of human perceptual-alternation at the time one observes the ambiguous figures such as the "Rubin's face and vase". Using a primary model based on the multilayr neural network proposed by M.Riani et al. to represent the information processing in the higher level of brain for this phenomenon, we examined the input pattern-dependence of the firing-rate for the perceptual-alternation. In our approach, this alternation is interpreted as that of the rate of firing neurons for the pattern perceived. Experimentally, we could obtain the Gamma distribution of the perceptual duration of the alternating interpretations. We then regards this Gamma distribution as the universal character of the perceptual alternation. From our result of computer simulation we could show the efficiency of the model. In particular, we have shown the chaotic behavior of visual inputs corresponding to attention eye movement makes it possible to obtain clearer the Gamma distributions than those of the random and/or 1/f visual behavior. We also find some other efficiencies of the chaotic inputs, for example, such as the input pattern-dependence of the rate of firing neurons and discuss an other model, for example, the chaotic neural network.
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