Observational learning: basis, experimental results, models and implications to robotics

Taylor, John G., Cutsuridis, Vassilis, Hartley, Matthew , Althoefer, Kaspar and Nanayakkara, Thrishantha (2013) Observational learning: basis, experimental results, models and implications to robotics. Cognitive Computation, 5 (3). pp. 340-354. ISSN 1866-9956


Request a copy
[img] PDF
TayCutEtAlCognComp2013.pdf - Whole Document
Restricted to Repository staff only

Item Type:Article
Item Status:Live Archive


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.

Additional Information:Special Issue: In Memory of John G Taylor: A Polymath Scholar Issue Editors: Vassilis Cutsuridis, Amir Hussain
Keywords:Neural model, Cognition, Perception, Action, Inverse model, Observational learning, DARWIN robot
Subjects:G Mathematical and Computer Sciences > G750 Cognitive Modelling
B Subjects allied to Medicine > B140 Neuroscience
G Mathematical and Computer Sciences > G730 Neural Computing
Divisions:College of Science > School of Computer Science
Related URLs:
ID Code:27724
Deposited On:04 Jul 2017 12:12

Repository Staff Only: item control page