Probing Human Visual Strategies Using Interpretability Methods for Artificial Neural Networks

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dc.contributor.advisorKar, Kohitij
dc.contributor.authorKashef Alghetaa, Yousif Khalid Faeq
dc.date.accessioned2024-10-28T13:37:35Z
dc.date.available2024-10-28T13:37:35Z
dc.date.copyright2024-07-15
dc.date.issued2024-10-28
dc.date.updated2024-10-28T13:37:35Z
dc.degree.disciplineBiology
dc.degree.levelMaster's
dc.degree.nameMSc - Master of Science
dc.description.abstractUnraveling human visual strategies during object recognition remains a challenge in vision science. Existing psychophysical methods used to investigate these strategies are limited in accurately interpreting human decisions. Recently, artificial neural network (ANN) models, which show remarkable similarities to human vision, provide a window into human visual strategies. However, inconsistencies among different techniques hinder the use of explainable AI (XAI) methods to interpret ANN decision-making. Here, we first develop and validate a novel surrogate method, in silico, using behavioral probes in ANNs with explanation-masked images to address these challenges. Finally, by identifying the XAI method and ANN with the highest human alignment, we provide a working hypothesis and an effective approach to explain human visual strategies during object recognition -- a framework relevant to many other behaviors.
dc.identifier.urihttps://hdl.handle.net/10315/42386
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectBiology
dc.subjectNeurosciences
dc.subjectArtificial intelligence
dc.titleProbing Human Visual Strategies Using Interpretability Methods for Artificial Neural Networks
dc.typeElectronic Thesis or Dissertation

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