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dc.contributor.advisorLaura Schulz and Joshua Tenenbaum.en_US
dc.contributor.authorDhariwal, Manujen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2019-03-11T19:36:42Z
dc.date.available2019-03-11T19:36:42Z
dc.date.copyright2018en_US
dc.date.issued2018en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/120899
dc.descriptionThesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, 2018.en_US
dc.descriptionThesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 65-66).en_US
dc.description.abstractIn this thesis, I propose concretizing the Piagetian view of children as 'gifted learners' to children as 'gifted language builders', who construct and learn many languages to reduce their uncertainty about the world. These include languages such as, the language of geometry, the language of music & rhythm, even a child playing with blocks (eg: LEGO) is actually learning or rather building a language for themselves. As a specific case, I introduce an experimental paradigm and tool, Finding GoDot, for studying the cognitive language of geometry. Using the above lens, I model constructive actions as a language, specifically looking at the task of drawing shapes. Next, majority of this thesis deals with the problem of calculating the entropy and redundancy of such a language for which there is no readily available language data. For this, I utilize Shannon's insight of accessing our implicit statistical knowledge of the structure of a language by converting it to a reduced text form, through a prediction experiment. I generalize Shannon's experiment design to make it applicable for a wide variety of languages, beyond just text-based, especially those lacking existing language data. Finally, I compute entropy (average information per letter) values for individual shapes to show evidence of subjects using a rich forward model to mentally simulate incomplete shapes, thus gaining information about the underlying shape more than is visible. I also share results on bounds for the entropy and redundancy of the proposed language of actions for generating shape drawings.en_US
dc.description.statementofresponsibilityby Manuj Dhariwal.en_US
dc.format.extent66 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT 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.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectEngineering and Management Program.en_US
dc.subjectSystem Design and Management Program.en_US
dc.subjectIntegrated Design and Management Program.en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleAn experimental and theoretical tool for studying the language of geometric conceptsen_US
dc.typeThesisen_US
dc.description.degreeS.M. in Engineering and Managementen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Engineering and Management Program.en_US
dc.contributor.departmentSystem Design and Management Program.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Integrated Design and Management Program.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.identifier.oclc1088894032en_US


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