Compositional simulation in perception and cognition

Despite rapid recent progress in machine perception and models of biological perception, fundamental questions remain open. In particular, the paradigm underlying these advances, pattern recognition, requires large amounts of training data and struggles to generalize to situations outside the domain of training. In this thesis, I focus on a broad class of perceptual concepts - those that are generated by the composition of multiple causal processes, in this case certain physical interactions - that human use essentially and effortlessly in making sense of the world, but for which any specific instance is extremely rare in our experience. Pattern recognition, or any strongly learning-based approach, might then be an inappropriate way to understand people's perceptual inferences.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Siegel, Max Harmon.

Advisor dc:contributor.advisor

• Joshua B. Tenenbaum.

• Brain and Cognitive Sciences.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

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

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/121814

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/121814