Modeling image-to-image confusions in memory

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2015

Author dc:creator

• Zhao, Anthony Dong

Advisor dc:contributor.advisor

• Aude Oliva.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/100611

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/100611