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  1. Jun Tani (2004). The Dynamical Systems Accounts for Phenomenology of Immanent Time: An Interpretation by Revisiting a Robotics Synthetic Study. Journal of Consciousness Studies 11 (9):5-24.
    This paper discusses possible correspondences between the dynamical systems characteristics observed in our previously proposed cognitive model and phenomenological accounts of immanent time considered by Edmund Husserl. Our simulation experiments in the anticiparatory learning of a robot showed that encountering sensory-motor flow can be learned as segmented into chunks of reusable primitives with accompanying dynamic shifting between coherences and incoherences in local modules. It is considered that the sense of objective time might appear when the continuous sensory-motor flow input to (...)
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  2. Yuuya Sugita & Jun Tani (2002). A Connectionist Model Which Unifies the Behavioral and the Linguistic Processes. In Maxim I. Stamenov & Vittorio Gallese (eds.), Mirror Neurons and the Evolution of Brain and Language. John Benjamins.
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  3. Takashi Ikegami & Jun Tani (2001). Chaotic Itinerancy Needs Embodied Cognition to Explain Memory Dynamics. Behavioral and Brain Sciences 24 (5):818-819.
    Memory dynamics need both stable and unstable properties simultaneously. Hence memory dynamics cannot be simulated by chaotic itinerant dynamics alone, with no real world correspondence. Memory dynamics are constrained by both semantics and causalities in the embodied cognition.
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  4. Jun Tani (1999). A Constructivist Approach. In Jonathan Shear & Shaun Gallagher (eds.), Models of the Self. Imprint Academic. 149.
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  5. Jun Tani (1998). An Interpretation of Theself'from the Dynamical Systems Perspective: A Constructivist Approach. Journal of Consciousness Studies 5 (5-6):5-6.
    This study attempts to describe the notion of the ‘self’ using dynamical systems language based on the results of our robot learning experiments. A neural network model consisting of multiple modules is proposed, in which the interactive dynamics between the bottom-up perception and the top-down prediction are investigated. Our experiments with a real mobile robot showed that the incremental learning of the robot switches spontaneously between steady and unsteady phases. In the steady phase, the top-down prediction for the bottom-up perception (...)
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