On a Unique Solution of a T-Maze Model Arising in the Psychology and Theory of Learning

Anita Tomar,Wajahat Ali,Juan J. Nieto,Ali Turab

doi:10.1155/2022/6081250

Abstract

In the form of a T, a T-maze is an experimental design in which each trial consists of decisions between two or more options. It contains choices with particular kinds of symmetries that have gained considerable attention in psychology and learning theories. One of the simplest mazes utilized by rats is the T-maze since it requires just a single point of preference. At a T-maze base, the mouse chooses to turn right or left to get food. This paper aims at analyzing the rat’s behavior in such circumstances and proposing a suitable mathematical model for it. The existence and uniqueness of a solution to the proposed T-maze model are investigated by using the appropriate fixed point method.

Highlights

Mathematical psychology is an approach to psychological study focused on mathematical modeling of perceptual, thinking, cognitive, and motor processes
In an animal or human being, the learning phase may often be viewed as a series of choices between multiple possible reactions
We present a specific type of psychological learning theory experiment related to the T-maze model proposed by Brunswik and Stanley in [23, 24], and suggest a mathematical model that is appropriate for it

Summary

Introduction

Mathematical psychology is an approach to psychological study focused on mathematical modeling of perceptual, thinking, cognitive, and motor processes. The mathematical methods are used to develop more reliable theories and produce more rigorous empirical validations. The biggest issue with today’s application of mathematics to psychological problems and most likely for some time to come is modeling these problems. In an animal or human being, the learning phase may often be viewed as a series of choices between multiple possible reactions. It is helpful to identify structural adjustments in the series of alternatives that reflect changes in trial-to-trial outcome probabilities. From this perspective, most of the learning analysis explains the probability of a trial-to-test occurrence that describes a stochastic mechanism

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