Cognitive Computation

, Volume 5, Issue 3, pp 340–354

Observational Learning: Basis, Experimental Results and Models, and Implications for Robotics


DOI: 10.1007/s12559-013-9208-1

Cite this article as:
Taylor, J.G., Cutsuridis, V., Hartley, M. et al. Cogn Comput (2013) 5: 340. doi:10.1007/s12559-013-9208-1


In this paper, we describe a brief survey of observational learning, with particular emphasis on how this could impact on the use of observational learning in robots. We present a set of simulations of a neural model which fits recent experimental data and such that it leads to the basic idea that observational learning uses simulations of internal models to represent the observed activity, so allowing for efficient learning of the observed actions. We conclude with a set of recommendations as to how observational learning might most efficiently be used in developing and training robots for their variety of tasks.


Neural model Cognition Perception Action Inverse model Observational learning DARWIN robot 

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • John G. Taylor
    • 1
  • Vassilis Cutsuridis
    • 2
    • 4
  • Matthew Hartley
    • 3
  • Kaspar Althoefer
    • 2
  • Thrishantha Nanayakkara
    • 2
  1. 1.Department of MathematicsKings College LondonStrandUK
  2. 2.Division of EngineeringKings College LondonStrandUK
  3. 3.Department of Computational and Systems BiologyJohn Innes CentreNorwichUK
  4. 4.Institute of Molecular Biology and BiotechnologyFoundation of Research and Technology - Hellas (FORTH)Heracklion, CreteGreece

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