The Induction of Dynamical Recognizers
Machine Learning - Connectionist approaches to language learning
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In this article, we propose a model which interprets the crossed-hand deficit of temporal order judgment (TOJ) from the dynamical systems perspective. The TOJ paradigm is important to understanding how the body image is dynamically sustained in our daily life. Our aim is to show one possible example of how the TOJ deficit could be consistently expressed in the dynamical systems framework. According to the study reported by Yamamoto and Kitazawa, using the genetic algorithm, we reconstruct the crossed-hand effect with a recurrent neural network, and show that it was caused by slow relaxation dynamics near the critical point in our model agent. Moreover, by using the same agent, we demonstrate the occurrence of the deficit in response to three successive stimuli which may help reveal the mechanism of TOJ deficit in the real human case.