Dynamics of Visual Supervised Learning: A Statistical-Physics Approach

Abstract

We analysed human supervised learning and classification performance for compound Gabor gray-level patterns. We found that internal visual representations for supervised learning and classification may not be constructed in a smooth process of gradual development (Jüttner and Rentschler, 1996 Vision Research in press). Rather, it seemed that certain learning states (‘stereotypes’) recur that may be considered as ‘perceptual hypotheses’. Such effects have a transient character and cannot, therefore, be studied on the basis of cumulative learning data, which allow smoothing at the expense of temporal resolution. Thus, we analyse classification behaviour in terms of the evolution of a thermodynamic system, that is a system characterised by Gibbs statistics. Here it is assumed that a classification error occurs when a noise-influenced decision process passes an ‘energy gap’ related to the distance of signals in feature space. This approach has been extended to a wide range of distance-based models, originated by different fields, such as classical psychometrics, signal detection theory, technical pattern recognition, and connectionism. We made use of the finding that all these models can be related to a uniform mathematical structure (Unzicker et al, 1995 Perception24 Supplement, 95). The subjects' performance can then be described as a cooling process that reveals adaptive feature extraction during learning.