Psychology ETDs

Publication Date

Summer 7-28-2026

Abstract

The Morris Water Task (MWT) has been the gold standard assessment of place learning and memory for over four decades, yet the processes that contribute to successful navigation are not completely understood. It is generally posited that rats learn to navigate directly to a place; however, navigation is not perfectly direct but contains variation in swim speed and trajectory resulting in efficient, yet suboptimal paths. Gonçalves-Garcia et al. (2024) demonstrated that path topography is quantitatively similar across trials within individual rats for a given start location, and dissimilar between rats and different start locations within rat. This suggests rats may take unique paths to the platform from each release point. If such variation reflects learned behavior, it should be possible to objectively classify (identify) individual rats using machine learning. In the present study, a recurrent neural network was trained to classify 15 rats based on trial-level time-series data of path coordinates, velocity, and orientation. Testing on unseen coordinates or combined features from the same starting locations yielded classification accuracies that were 5.93-7.26-fold greater than chance and significantly greater than data from different start locations. Neither classification of scalar metrics with support vector machines nor path images with convolutional neural networks were able to match these outcomes. Collectively, these observations support the hypothesis that navigation in the MWT involves learning to perform behavioral sequences.

Degree Name

Psychology

Level of Degree

Masters

Department Name

Psychology

First Committee Member (Chair)

Dr. Derek Hamilton

Second Committee Member

Dr. Benjamin Clark

Third Committee Member

Dr. Jeremy Hogeveen

Language

English

Keywords

Spatial Navigation, Machine Learning, Morris Water Task, Learning, Memory, Sequential Behavior

Document Type

Thesis

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