Abstract
Although many models of consciousness have been proposed from various viewpoints, these models have not been based on learning activities in a whole system with capability of autonomous adaptation. Through investigating a learning process as the whole system, consciousness is basically modeled as system level learning activity to modify both own configuration and states in autonomous adaptation. The model not only explains the time delay of Libet’s experiment, but also is positioned as an improved model of Global Workspace Theory.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Seth, A.: Models of Consciousness. Scholarpedia 2(1), 1328 (2007)
Taylor, J., Freeman, W., Cleeremans, A.(eds.): Brain and Consciousness, special issue of Neural networks. Neural Networks 20(9) (2007)
Kinouchi, Y.: A logical model of consciousness on an autonomously adaptive system. IJMCÂ 1(2) (2009)
Kinouchi, Y., Kato, Y., Hayashi, H., Katsumata, Y., Kitakaze, K., Inabayashi, S.: A model of primitive consciousness in autonomously adaptive system under a framework of reinforcement learning. In: AISB 2011 Machine Consciousness (2011)
Kinouchi, Y., Kato, Y., Inabayashi, S.: Model of primitive consciousness based on learning activity in autonomously adaptive system. In: ASSC, vol. 16 (2012)
Libet, B.: Mind time: The temporal Factor in Consciousness. Harvard University Press (2004)
Baars, B.J.: A cognitive theory of consciousness. Cambridge University Press (1988)
Baars, B., Franklin, S.: How conscious experience and working memory interact. Trend in Cognitive Sciences 7(4) (2003)
Changeux, J.P., Dehaene, S.: The Neural Workspace Model: Conscious Processing and Learning. In: Learning Theory and Behavior, vol. 1. Elsevier (2008)
Franklin, S., Strain, S., Snaider, J., McCall, R., Faghihi, U.: Global workspace theory, its LIDA model and the underlying neuroscience. Biologically Inspired Cognitive Architectures 1(1) (2012)
Sutton, R., Barto, A.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)
Franklin, S., D’Mello, S., Baars, B., Ramamurthy, U.: Evolutionary Pressures for Perceptual Stability and Self as Guides to Machine Consciousness. International Journal of Machine Consciousness 1(1), 99–110 (2009)
Carpenter, G.A., Grossberg, S.: Pattern recognition by self-organizing neural networks. The MIT Press (1991)
Patterson, D., Hennessy, J.: Computer Organization and Design: The hardware/software interface. Morgan Kaufman Publishers, San Francisco (1994)
Norman, D.: Invisible Computer. MIT Press (1998)
Damasio, A.: Descartes’ error-Emotion, Reason, and the Human Brain. Putnam’s Sons (1994)
Koch, C.: The Quest for Consciousness: A Neurobiological Approach. Roberts and Company Publishers, Colorado (2004)
Brook, A., Raymont, P.: The Unity of Consciousness: The Stanford Encyclopedia of Philosophy (Fall 2010 Edition), http://plato.stanford.edu/entries/consciousness-unity/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kinouchi, Y., Kato, Y. (2013). A Model of Primitive Consciousness Based on System-Level Learning Activity in Autonomous Adaptation. In: Chella, A., Pirrone, R., Sorbello, R., Jóhannsdóttir, K. (eds) Biologically Inspired Cognitive Architectures 2012. Advances in Intelligent Systems and Computing, vol 196. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34274-5_33
Download citation
DOI: https://doi.org/10.1007/978-3-642-34274-5_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34273-8
Online ISBN: 978-3-642-34274-5
eBook Packages: EngineeringEngineering (R0)