Characterizing human vision through large-scale brain imaging and computational models

Efforts to understand the neural underpinnings of human visual processing require sufficient experimental data and robust models. This thesis significantly contributes to both these fronts while simultaneously elucidating some of the most intriguing aspects of the human visual system. In the first chapter, I use a combination of classical machine learning, artificial neural networks, and a joint MEG/fMRI neuroimaging dataset to reveal that the human visual system extensively processes highly memorable images in regions distributed throughout visual cortex late in time. In the second chapter, I present the BOLD Moments Dataset, a large-scale fMRI dataset using short video stimuli to extend computational models of visual processing into the video domain to better understand how humans process visual content unfolding over time. The last chapter introduces a fMRI dataset aggregation framework titled MOSAIC to achieve the scale and stimulus diversity needed for training modern neural networks directly on brain responses. This body of work exemplifies how large-scale experimental data and artificial neural networks can contribute towards a robust and generalizable understanding of human visual processing.

Name thesis:degree_name

Doctoral

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

2025

Author dc:creator

• Lahner, Benjamin

Advisor dc:contributor.advisor

• Oliva, Aude

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/164053

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/164053