Date of Award

6-4-2020

Document Type

Thesis (Undergraduate)

Department

Department of Computer Science

First Advisor

Yaroslav Halchenko

Abstract

The importance of the position of cephalopods, and particularly octopuses, as the most intelligent group of invertebrates is becoming increasingly appreciated by the neuroscience research community. Cephalopods are the most distantly related species to humans that possesses advanced cognitive abilities; as their intelligence evolved independently from vertebrates, comparative analyses reveal trends in the evolution of nervous systems and the foundations of intelligence itself. Vision is an especially important area of cephalopod cognition to research because cephalopods are predominantly visual creatures, like humans, and the rapid transduction of visual signals allows the inner-workings of octopus cognition to be revealed in real time. While octopuses can be conditioned to indicate what they see through responses to conditioned visual stimuli, no system as of yet provides a non-invasive means of determining what an octopus is looking at without training. This thesis introduces an automated methodological framework to predict the direction of an octopuses gaze for use in visual cognition research. The system utilizes deep learning models to track the eyes of octopuses, then predicts where an octopus is looking based off of the orientation of their eyes and known anatomical traits that constrain where their vision could be directed. Data could not be collected this spring to train a model and test the tool in the experimental setting the system utilizes, however analyses conducted on data not intended for this project suggest the approach is feasible for estimating an octopus' gaze and offer insights into how to do so most effectively.

Comments

Originally posted in the Dartmouth College Computer Science Technical Report Series, number TR2020-883.

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