PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Perception-Action Learning as an Epistemologically-Consistent Model for Self-Updating Cognitive Representation
David Windridge and Josef Kittler
Advances in Experimental Medicine and Biology Volume 657, pp. 95-134, 2010.


As well as having the ability to formulate models of the world capable of experimental falsification, it is evident that human cognitive capability embraces some degree of representational plasticity, having the scope (at least in infancy) to modify the primitives in terms of which the world is delineated. We hence employ the term 'cognitive bootstrapping' to refer to the autonomous updating of an embodied agent's perceptual framework in response to the perceived requirements of the environment in such a way as to retain the ability to refine the environment model in a consistent fashion across perceptual changes.We will thus argue that the concept of cognitive bootstrapping is epistemically ill-founded unless there exists an a priori percept/motor interrelation capable of maintaining an empirical distinction between the various possibilities of perceptual categorization and the inherent uncertainties of environment modeling.As an instantiation of this idea, we shall specify a very general, logically-inductive model of perception-action learning capable of compact re-parameterization of the percept space. In consequence of the a priori percept/action coupling, the novel perceptual state transitions so generated always exist in bijective correlation with a set of novel action states, giving rise to the required empirical validation criterion for perceptual inferences. Environmental description is correspondingly accomplished in terms of progressively higher-level affordance conjectures which are likewise validated by exploratory action.Application of this mechanism within simulated perception-action environments indicates that, as well as significantly reducing the size and specificity of the a priori perceptual parameter-space, the method can significantly reduce the number of iterations required for accurate convergence of the world-model. It does so by virtue of the active learning characteristics implicit in the notion of cognitive bootstrapping.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:6568
Deposited By:David Windridge
Deposited On:08 March 2010