Human and computer learning: An experimental study

Abstract

Simple memorizing tasks have been chosen such as a binary code on a matrix. After the establishment of an appropriate protocol, the codified matrices were individually presented to 150 university students who had to memorize them. A computer simulation for a similar task is available which uses a perceptron on which an algorithm was implemented allowing for some degree of globality (technically referred to as entropic nonextensivity within a current generalization of the usual, Boltzmann-Gibbs, statistical mechanics). Our main observation is that, for the very specific learning task on which we focus here, humans perform similarly to slightly nonextensive perceptrons.