Learning by seeing-associative learning of visual features through mental simulation of observed action

ECAL 2011: The 11th European Conference on Artificial Life

Volumn , Issue , 2011, Pages

  Schilling, Malte ^a,b  

^a INTERNATIONAL COMPUTER SCIENCE INSTITUTE   (United States)

^b BIELEFELD UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ASSOCIATIVE LEARNING; BODY MODELS; COGNITIVE TASK; FLEXIBLE USE; HIGH-LEVEL FUNCTIONS; INTERNAL MODELS; INTERNAL REPRESENTATION; MENTAL SIMULATION; MULTIPLE MODALITIES; VISUAL FEATURE;

ARTIFICIAL INTELLIGENCE;

EID: 84865065659     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.7551/978-0-262-29714-1-ch111     Document Type: Conference Paper

References (20)

1. Bernstein, N. A. (1967). The Co-ordination and regulation of movements. Pergamon Press Ltd., Oxford.
2. Cruse, H. and Steinkühler, U. (1993). Solution of the direct and inverse kinematic problems by a common algorithm based on the mean of multiple computations. Biological Cybernetics, 69:345-351.
3. Decety, J. and Grezes, J. (1999). Neural mechanisms subserving the perception of human actions. Trends in Cognitive Sciences, 3(5):172-178.
4. Grush, R. (2004). The emulation theory of representation: Motor control, imagery, and perception. Behavioral and Brain Sciences, 27:377-442.
5. Hebb, D. O. (1949). The Organization of Behavior. John Wiley, New York.
6. Hesslow, G. (2002). Conscious thought as simulation of behaviour and perception. Trends in Cognitive Sciences, 6(6):242-247.
7. Hoffmann, M., Marques, H., Arieta, A. H., Sumioka, H., Lungarella, M., and Pfeifer, R. (2010). Body schema in robotics: a review. IEEE Trans. Auton. Mental Develop., 2(4):304-324.
8. Jeannerod, M. (2006). Motor Cognition - What Action tells the Self. Oxford: University Press.
9. Makarov, V., Song, Y., Velarde, M., Hübner, D., and Cruse, H. (2008). Elements for a general memory structure: properties of recurrent neural networks used to form situation models. Biological Cybernetics., 98(5):371-395.
10. Morasso, P. (1981). Spatial control of arm movements. Experimental Brain Research, 42(2):223-227.
11. Mukundan, R. and Ramakrishnan, K. (1998). Moment Functions in Image Analysis: Theory and Applications. World Scientific, London, UK.
12. Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986). Learning internal representations by error propagation, pages 318-362. MIT Press, Cambridge, MA, USA.
13. Schacter, D. L., Addis, D. R., and Buckner, R. (2007). Remembering the past to imagine the future: the prospective brain. Nature Reviews Neuroscience, 8(7):657-661.
14. Schilling, M. (2009). Dynamic equations in MMC networks: Construction of a dynamic body model. In Proc. of The 12th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines (CLAWAR).
15. Schilling, M. (2011). Universally manipulable body models - dual quaternion representations in layered and dynamic MMCs. Autonomous Robots, 30(4):399-425.
16. Schilling, M. and Cruse, H. (2007). Hierarchical MMC Networks as a manipulable body model. In Proceedings of the International Joint Conference on Neural Networks (IJCNN 2007), Orlando, FL, pages 2141-2146.
17. Steels, L. (2003). Intelligence with representation. Philosophical Transactions: Mathematical, Physical and Engineering Sciences, 361(1811):2381-2395.
18. Steels, L. and Belpaeme, T. (2005). Coordinating perceptually grounded categories through language: A case study for colour. Behavioral and Brain Sciences, 28(04):469-489.
19. Steels, L. and Spranger, M. (2008). The robot in the mirror. Connection Science, 20(4):337-358.
20. Steinkühler, U. and Cruse, H. (1998). A holistic model for an internal representation to control the movement of a manipulator with redundant degrees of freedom. Biological Cybernetics, 79(6):457-466.