A mathematical model for simple learning.

TLDR

The model builds on and aligns with Estes’ earlier work on learning. The study introduces a mathematical model of simple learning. The model describes how response probabilities evolve over time using operators linked to reward, work, and empirical response rates, and examines partial reinforcement schedules. The model predicts acquisition and extinction patterns, yielding equations for mean latent time, response rates, and cumulative responses in runway and Skinner box experiments.

Abstract

A mathematical model for simple learning is presented. Changes in the probability of occurrence of a response in a small time h are described with the aid of mathematical operators. The parameters which appear in the operator equations are related to experimental variables such as the amount of reward and work. Relations between the probability and empirical measures of rate of responding and latent time are defined. Acquisition and extinction of behavior habits are discussed for the simple runway and for the Skinner box. Equations of mean latent time as a function of trial number are derived for the runway problem; equations for the mean rate of responding and cumulative numbers of responses versus time are derived for the Skinner box experiments. An attempt is made to analyze the learning process with various schedules of partial reinforcement in the Skinner type experiment. Wherever possible, the correspondence between the present model and the work of Estes [2] is pointed out.