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Aude Oliva. Zhao, Anthony Dong Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2016-01-04T19:58:33Z 2016-01-04T19:58:33Z 2015 2015 http://hdl.handle.net/1721.1/100611 932618780 Thesis: M. Eng. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Title as it appears in MIT Commencement Exercises program, June 5, 2015: Metamers in memory: predicting pairwise image confusions with deep learning. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 81-83). Previous experiments have examined what causes images to be remembered or forgotten. In these experiments, participants sometimes create false positives when identifying images they have seen before, but the precise cause of these false positives has remained unclear. We examine confusions between individual images as a possible cause of these false positives. We first introduce a new experimental task for examining measuring the rates at which participants confuse one image for another and show that the images prone to false positives are also ones that people tend to confuse. Second, we show that there is a correlation between how often people confuse pairs of images and how similar they find those pairs. Finally, we train a Siamese neural network to predict confusions between pairs of images. By studying the mechanisms behind the failures of memory, we hope to increase our understanding of memory as a whole and move closer to a computational model of memory. by Anthony Dong Zhao. M.Eng. in Computer Science and Engineering 83 pages eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Modeling image-to-image confusions in memory Metamers in memory : predicting pairwise image confusions with deep learning Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="86241387-5702-4994-a123-12d5baffd0f8"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Modeling image-to-image confusions in memory</Title> <Subtitle>Metamers in memory : predicting pairwise image confusions with deep learning</Subtitle> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2015</PublicationDate> <Authors> <Author> <DisplayName>Zhao, Anthony Dong</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>Previous experiments have examined what causes images to be remembered or forgotten. In these experiments, participants sometimes create false positives when identifying images they have seen before, but the precise cause of these false positives has remained unclear. We examine confusions between individual images as a possible cause of these false positives. We first introduce a new experimental task for examining measuring the rates at which participants confuse one image for another and show that the images prone to false positives are also ones that people tend to confuse. Second, we show that there is a correlation between how often people confuse pairs of images and how similar they find those pairs. Finally, we train a Siamese neural network to predict confusions between pairs of images. By studying the mechanisms behind the failures of memory, we hope to increase our understanding of memory as a whole and move closer to a computational model of memory.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>