Skip to main content
Mijn bibliotheek
Shop
Nieuws Boeken Tijdschriften Toetsvragen Web-tv Video Audio
Tips voor Mijn BSL
Inloggen

Welkom bij THIM Hogeschool voor Fysiotherapie & Bohn Stafleu van Loghum

THIM Hogeschool voor Fysiotherapie heeft ervoor gezorgd dat je Mijn BSL eenvoudig en snel kunt raadplegen. Je kunt je links eenvoudig registreren. Met deze gegevens kun je thuis, of waar ook ter wereld toegang krijgen tot Mijn BSL. Heb je een vraag, neem dan contact op met helpdesk@thim.nl.

» Andere revante producten voor THIM studenten en medewerkers.

Registreer

Om ook buiten de locaties van THIM, thuis bijvoorbeeld, van Mijn BSL gebruik te kunnen maken, moet je jezelf eenmalig registreren. Dit kan alleen vanaf een computer op een van de locaties van THIM.

Eenmaal geregistreerd kun je thuis of waar ook ter wereld onbeperkt toegang krijgen tot Mijn BSL.

Login

Als u al geregistreerd bent, hoeft u alleen maar in te loggen om onbeperkt toegang te krijgen tot Mijn BSL.

Inloggen
Top
Psychological Research
Gepubliceerd in:

01-07-2009 | Original Article

Development of hierarchical structures for actions and motor imagery: a constructivist view from synthetic neuro-robotics study

Auteurs: Ryunosuke Nishimoto, Jun Tani

Gepubliceerd in: Psychological Research | Uitgave 4/2009

Log in om toegang te krijgen
share
DELEN

Deel dit onderdeel of sectie (kopieer de link)

  • Optie A:
    Klik op de rechtermuisknop op de link en selecteer de optie “linkadres kopiëren”
  • Optie B:
    Deel de link per e-mail
    Link delen
publish
Publiceer nu

Abstract

The current paper shows a neuro-robotics experiment on developmental learning of goal-directed actions. The robot was trained to predict visuo-proprioceptive flow of achieving a set of goal-directed behaviors through iterative tutor training processes. The learning was conducted by employing a dynamic neural network model which is characterized by their multiple time-scale dynamics. The experimental results showed that functional hierarchical structures emerge through stages of developments where behavior primitives are generated in earlier stages and their sequences of achieving goals appear in later stages. It was also observed that motor imagery is generated in earlier stages compared to actual behaviors. Our claim that manipulatable inner representation should emerge through the sensory–motor interactions is corresponded to Piaget’s constructivist view.
vorige artikel Brain mechanisms for predictive control by switching internal models: implications for higher-order cognitive functions
volgende artikel Thinking as the control of imagination: a conceptual framework for goal-directed systems
Bijlagen
Alleen toegankelijk voor geautoriseerde gebruikers
Literatuur
Arbib, M. (1981). Perceptual structures and distributed motor control. In Handbook of Physiology: The Nervous System, II. Motor Control (pp. 1448–1480). Cambridge: MIT Press.
Arie, H., Endo, T., Arakaki, T., Sugeno, S., & Tani, J. (2009). Creating novel goal-directed actions at criticality: A neuro-robotic experiment. New Mathematics and Natural Computation, 5(1), 307–334.
Beer, R. (1995). A dynamical systems perspective on agent-environment interaction. Artificial Intelligence, 72(1), 173–215.CrossRef
Butz, M. V. (2008). How and why the brain lays the foundations for a conscious self. Constructivist Foundations, 4(1), 1–14.
Butz, M., Sigaud, O., Pezzulo, G., & Baldassarre, G. (2007). Anticipatory behavior in adaptive learning systems: From brains to individual and social behavior. Berlin: Springer.CrossRef
Decety, J. (1996). Do executed and imagined movements share the same central structures? Cognitive Brain Research, 3, 87–93.PubMedCrossRef
Diamond, A. (1991). Neuropsychological insights into the meaning of object concept development. Hillsdale: Erlbaum.
Doya, K., & Yoshizawa, S. (1989). Memorizing oscillatory patterns in the analog neuron network. In Proceedings of 1989 international joint conference on neural networks, washington, D.C. (pp. I:27–32).
Ehrsson, H., Fagergren, A., Johansson, R., & Forssberg, H. (2003). Evidence for the involvement of the posterior parietal cortex in coordination of fingertip forces for grasp stability in manipulation. Journal of Neurophysiology, 90, 2978–2986.PubMedCrossRef
Elman, J. (1990). Finding structure in time. Cognitive Science, 14, 179–211.CrossRef
Elman, J. L., Bates, A. E. A., Johnson, M. H., Karmiloff-Smith, A., Parisi, D., & Plunkett, K. (1997). Rethinking innateness a connectionist perspective on development. Cambridge: MIT Press.
Eskandar, E., & Assad, J. (1999). Dissociation of visual, motor and predictive signals in parietal cortex during visual guidance. Nature Neuroscience, 2, 88–93.PubMedCrossRef
Feltz, D. L., & Landers, D. M. (1983). The effects of mental practice on motor skill learning and performance: A meta-analysis. Journal of sport psychology, 5, 25–57.
Flanagan, J., Vetter, P., Johansson, R., & Wolpert, D. (2003). Prediction precedes control in motor learning. Current Biology, 13(2), 146–150.PubMedCrossRef
Fuster, J. (1989). The Prefrontal Cortex. New York: Raven Press.
Haruno, M., Wolpert, D., & Kawato, M. (2003). Hierarchical mosaic for movement generation. International Congress Series, 1250, 575–590.CrossRef
Hesslow, G. (2002). Conscious thought as simulation of behaviour and perception. Trends in Cognitive Science, 6(6), 242-247.CrossRef
Imazu, S., Sugio, T., Tanaka, S., & Inui, T. (2007). Differences between actual and imagined usage of chopsticks: an fMRI study. Cortex, 43(3), 301-307.PubMedCrossRef
Ito, M., Noda, K., Hoshino, Y., & Tani, J. (2006). Dynamic and interactive generation of object handling behaviors by a small humanoid robot using a dynamic neural network model. Neural Networks, 19, 323–337.PubMedCrossRef
Jeannerod, M. (1994). The representing brain: neural correlates of motor imitation and imaginary. Behavioral and Brain Science, 17, 187–245.CrossRef
Jordan, M., & Jacobs, R. (1994). Hierarchical mixtures of experts and the EM algorithm. Neural Computation, 6(2), 181–214.CrossRef
Jordan, M., & Rumelhart, D. (1992). Forward models: Supervised learning with a distal teacher. Cognitive Science, 16, 307–354.CrossRef
Karmiloff-Smith, A. (1992). Beyond modularity: A developmental perspective on cognitive science. Cambridge: MIT Press.
Kawato, M., Maeda, Y., Uno, Y., & Suzuki, R. (1990). Trajectory formation of arm movement by cascade neural network model based on minimum torque-change criterion. Biological Cybernetics, 62(4), 275–288.PubMedCrossRef
Luria, A. (1973). The working brain. London: Penguin Books Ltd.
McCarthy, J. (1963). Situations, actions and causal laws. (Stanford Artificial Intelligence Project, Memo2).
Namikawa, J., & Tani, J. (2008). A model for learning to segment temporal sequences, utilizing a mixture of rnn experts together with adaptive variance. Neural Networks, 21, 1466–1475.PubMedCrossRef
Nishimoto, R., Namikawa, J., & Tani, J. (2008). Learning multiple goal-directed actions through self-organization of a dynamic neural network model: A humanoid robot experiment. Adaptive Behavior, 16, 166–181.CrossRef
Nolfi, S. (2002). Evolving robots able to self-localize in the environment: The importance of viewing cognition as the result of processes occurring at different time scales. Connection Science, 14(3), 231–244.CrossRef
Pezzulo, G. (2008). Coordinating with the future: The anticipatory nature of representation. Minds and Machines, 18, 179–225.CrossRef
Piaget, J. (1954). The construction of reality in the child. New York: Basic Books.CrossRef
Rizzolatti, G., Fadiga, L., Galless, V., & Fogassi, L. (1996). Premotor cortex and the recognition of motor actions. Cognitive Brain Research, 3, 131–141.PubMedCrossRef
Rumelhart, D., Hinton, G., & Williams, R. (1986). Learning internal representations by error propagation. In D. Rumelhart & J. McClelland (Eds.), Parallel distributed processing (pp. 318–362). Cambridge: MIT Press.
Schoner, S., & Kelso, S. (1988). Dynamic pattern generation in behavioral and neural systems. Science, 239, 1513–1519.PubMedCrossRef
Smith, L., & Thelen, E. (1994). A dynamic systems approach to the development of cognition and action. Cambridge: MIT Press.
Sugita, Y., & Tani, J. (2005). Learning semantic combinatoriality from the interaction between linguistic and behavioral processes. Adaptive Behavior, 13(3), 33–51.CrossRef
Tani, J. (1996). Model-Based Learning for Mobile Robot Navigation from the Dynamical Systems Perspective. IEEE Transaction on SMC (B), 26(3), 421–436.
Tani, J. (2003). Learning to generate articulated behavior through the bottom-up and the top-down interaction process. Neural Networks, 16, 11–23.PubMedCrossRef
Tani, J., & Fukumura, N. (1994). Learning Goal-directed Sensory-based Navigation of a Mobile Robot. Neural Networks, 7(3).
Tani, J., Ito, M., & Sugita, Y. (2004). Self-organization of distributedly represented multiple behavior schemata in a mirror system: Reviews of robot experiments using RNNPB. Neural Networks, 17, 1273–1289.PubMedCrossRef
Tani, J., Nishimoto, R., Namikawa, J., & Ito, M. (2008a). Codevelopmental learning between human and humanoid robot using a dynamic neural network model. IEEE Transaction on System, Man and Cybernetics, 38(1), 43–59.CrossRef
Tani, J., Nishimoto, R., & Paine, R. (2008b). Achieving “organic compositionality” through self-organization: Reviews on brain-inspired robotics experiments. Neural Networks, 21, 584–603.PubMedCrossRef
Tani, J., & Nolfi, S. (1999). Learning to perceive the world as articulated: An approach for hierarchical learning in sensory–motor systems. Neural Networks, 12, 1131–1141.PubMedCrossRef
Vogt, S. (1995). On relations between perceiving, imaging and performing in the learning of cyclical movement sequences. British journal of Psychology, 86, 191–216.PubMed
Wolpert, D., & Kawato, M. (1998). Multiple paired forward and inverse models for motor control. Neural Networks, 11, 1317–1329.PubMedCrossRef
Yamashita, Y., & Tani, J. (2008). Emergence of functional hierarchy in a multiple timescale neural network model: A humanoid robot experiment. PLoS Computational Biology, 4(11).
Ziemke, T., Jirenhed, D., & Hesslow, G. (2005). Internal simulation of perception: Minimal neurorobotic model. Neurocomputing, 68, 85–104.
Metagegevens
Titel
Development of hierarchical structures for actions and motor imagery: a constructivist view from synthetic neuro-robotics study
Auteurs
Ryunosuke Nishimoto
Jun Tani
Publicatiedatum
01-07-2009
Uitgeverij
Springer-Verlag
Gepubliceerd in
Psychological Research / Uitgave 4/2009
Print ISSN: 0340-0727
Elektronisch ISSN: 1430-2772
DOI
https://doi.org/10.1007/s00426-009-0236-0

Andere artikelen Uitgave 4/2009

The role of motor simulation in action perception: a neuropsychological case study

  • Open Access
  • Original Article

Associative theories of goal-directed behaviour: a case for animal–human translational models

  • Open Access
  • Original Article

Thinking as the control of imagination: a conceptual framework for goal-directed systems

  • Original Article

Intentional action: from anticipation to goal-directed behavior

  • Editorial

Brain mechanisms for predictive control by switching internal models: implications for higher-order cognitive functions

  • Original Article

Strategic influences on implementing instructions for future actions

  • Open Access
  • Original Article