A computational characterization of domain-based causal reasoning development in children

To better understand human intelligence, we must first understand how humans use and learn from stories. One important aspect of how humans learn from stories is our ability to reason about cause and eect. Psychological evidence suggests that when children develop the ability to learn cause-and-eect relationships from stories, they do so in discrete stages where each new stage enables the child to incorporate new kinds of information. In this thesis, I attempt to shed light on the mechanisms that underlie the development of causal reasoning in children. I create a behavior-level model, an explanatory theory, and an explanation-level model that account for the developmental stages. I implement these models on top of the Genesis Story Understanding System. The result is a psychologically plausible explanation-level model that captures the observed causal reasoning behaviors of children at dierent stages of developments. The model also takes the observations from psychological evidence to another level by proposing mechanisms that enable such development in children.

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

2018

Author dc:creator

• Aronoff, Caroline Bradley

Advisor dc:contributor.advisor

• Patrick H. Winston.

• Electrical Engineering and Computer Science.

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

http://hdl.handle.net/1721.1/119744

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/119744