Methods for Quantifying the Informational Structure of
Sensory and Motor Data
Statistical regularities in the sensory data relayed
to the brain are critical for enabling appropriate developmental processes,
perceptual categorization, adaptation, and learning. For robots such
regularities are important because they avoid the allocation of potentially
limited processing resources if not strictly necessary. There are at least
three alternative mechanisms by which informational structure is produced in
the sensory channels of natural organisms and robotic artifacts: (1) clever
pre-processing strategies (computational mechanism), (2) an adequate bodily
setup (morphological mechanism), and (3) appropriate behavioral interaction.
The aim of this project is to shed light on the third (behavioral)
mechanism. The core idea is to quantitatively characterize the informational
structure of sensory and motor data by means of a set of univariate
and multivariate statistical and information-theoretical measures. More
specifically, we propose a MatLab toolbox comprising
functions for density estimation, entropy, mutual information, complexity,
linear and nonlinear dimensionality reduction, and so on.
All the functions have been implemented in MatLab, but should be easily portable to Octave. The OS
platforms supported are all variants of Windows and Linux. A detailed
illustration of the toolbox can be also found in a scientific paper which is
under submission (available upon request). The potential importance of this
project is to further our understanding of processes of sensorimotor
coordination in organisms and for robot design. We also expect to apply the
proposed and similar methods to the design of robotic artifacts.