Establishing a Learning Foundation in a Dynamically Changing World: Insights from Artificial Language Work

Persistent Link:
http://hdl.handle.net/10150/308884
Title:
Establishing a Learning Foundation in a Dynamically Changing World: Insights from Artificial Language Work
Author:
Gonzales, Kalim
Issue Date:
2013
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Embargo:
Dissertation not available (per author's request)
Abstract:
It is argued that infants build a foundation for learning about the world through their incidental acquisition of the spatial and temporal regularities surrounding them. A challenge is that learning occurs across multiple contexts whose statistics can greatly differ. Two artificial language studies with 12-month-olds demonstrate that infants come prepared to parse statistics across contexts using the temporal and perceptual features that distinguish one context from another. These results suggest that infants can organize their statistical input with a wider range of features that typically considered. Possible attention, decision making, and memory mechanisms are discussed.
Type:
text; Electronic Dissertation
Keywords:
dynamic change; exemplar theory; statistical learning; Psychology; artificial language learning
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Psychology
Degree Grantor:
University of Arizona
Advisor:
Gómez, Rebecca L.

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleEstablishing a Learning Foundation in a Dynamically Changing World: Insights from Artificial Language Worken_US
dc.creatorGonzales, Kalimen_US
dc.contributor.authorGonzales, Kalimen_US
dc.date.issued2013en
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.releaseDissertation not available (per author's request)en_US
dc.description.abstractIt is argued that infants build a foundation for learning about the world through their incidental acquisition of the spatial and temporal regularities surrounding them. A challenge is that learning occurs across multiple contexts whose statistics can greatly differ. Two artificial language studies with 12-month-olds demonstrate that infants come prepared to parse statistics across contexts using the temporal and perceptual features that distinguish one context from another. These results suggest that infants can organize their statistical input with a wider range of features that typically considered. Possible attention, decision making, and memory mechanisms are discussed.en_US
dc.typetexten_US
dc.typeElectronic Dissertationen_US
dc.subjectdynamic changeen_US
dc.subjectexemplar theoryen_US
dc.subjectstatistical learningen_US
dc.subjectPsychologyen_US
dc.subjectartificial language learningen_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplinePsychologyen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorGómez, Rebecca L.en_US
dc.contributor.committeememberGómez, Rebecca L.en_US
dc.contributor.committeememberGerken, LouAnnen_US
dc.contributor.committeememberLotto, Andrew J.en_US
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