A computationally and cognitively plausible model of supervised and unsupervised learning

TL;DR

This paper introduces a new learning model inspired by psychological findings, incorporating chance correction to improve supervised and unsupervised learning, demonstrated through computational experiments.

Contribution

It proposes two chance-corrected models, Informatron and AdaBook, that enhance learning performance based on empirical psychological data.

Findings

Chance correction improves learning efficiency

Informatron functions as a chance-corrected Perceptron

AdaBook operates as a chance-corrected AdaBoost

Abstract

Both empirical and mathematical demonstrations of the importance of chance-corrected measures are discussed, and a new model of learning is proposed based on empirical psychological results on association learning. Two forms of this model are developed, the Informatron as a chance-corrected Perceptron, and AdaBook as a chance-corrected AdaBoost procedure. Computational results presented show chance correction facilitates learning.