{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/105710"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/105710","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Experiencing language in the order that children do: Training on age-ordered child-directed speech facilitates semantic category learning in a recurrent neural network","abstract":"Previous work has shown that semantic category knowledge can be captured by a distributional learning algorithm operating over naturalistic, noisy child-directed speech (Huebner & Willits, 2018). In chapter 1 of this work, I discuss the algorithm behind this study, and its ability to represent hierarchically organized and abstract knowledge. In chapter 2, I replicate the findings of Huebner & Willits (2018) using a variant of their corpus in which fewer post-processing modifications were applied to the raw transcripts. In chapter 3, I investigate whether training on input in order that children actually experience language provides any learning advantage relative to training in the reverse order Indeed, I found that semantic categorization benefits from training on input which was ordered by the age of the target child compared to input which was ordered in reverse. I refer to this effect as the age-order effect. To investigate what corpus-statistical factors may underlie the age-order effect, I explore structural differences between speech to younger vs. older children in chapter 4. In alignment with previous studies, I found that speech to younger children is syntactically less complex compared to speech to older children. Evidence for differences in semantic category structure was inconsistent. In chapter 5, I propose a number of competing explanations of the age-order effect, and identify one hypothesis, termed the good-start hypothesis, as the most promising. In chapter 6, I expand and refine the good-start hypothesis, and provide further empirical support for it. In chapter 7, I test two core assumptions of the theory developed in chapter 6 using carefully controlled artificial language corpora and find strong support for both. I close with a brief overview of findings in infant behavioral studies consistent with the theory and discuss the implications of the theory for infant acquisition of semantic category knowledge.","abstract_html":"Previous work has shown that semantic category knowledge can be captured by a distributional learning algorithm operating over naturalistic, noisy child-directed speech (Huebner & Willits, 2018). In chapter 1 of this work, I discuss the algorithm behind this study, and its ability to represent hierarchically organized and abstract knowledge. In chapter 2, I replicate the findings of Huebner & Willits (2018) using a variant of their corpus in which fewer post-processing modifications were applied to the raw transcripts. In chapter 3, I investigate whether training on input in order that children actually experience language provides any learning advantage relative to training in the reverse order Indeed, I found that semantic categorization benefits from training on input which was ordered by the age of the target child compared to input which was ordered in reverse. I refer to this effect as the age-order effect. To investigate what corpus-statistical factors may underlie the age-order effect, I explore structural differences between speech to younger vs. older children in chapter 4. In alignment with previous studies, I found that speech to younger children is syntactically less complex compared to speech to older children. Evidence for differences in semantic category structure was inconsistent. In chapter 5, I propose a number of competing explanations of the age-order effect, and identify one hypothesis, termed the good-start hypothesis, as the most promising. In chapter 6, I expand and refine the good-start hypothesis, and provide further empirical support for it. In chapter 7, I test two core assumptions of the theory developed in chapter 6 using carefully controlled artificial language corpora and find strong support for both. I close with a brief overview of findings in infant behavioral studies consistent with the theory and discuss the implications of the theory for infant acquisition of semantic category knowledge.","abstract_has_math":false,"creators":["Huebner, Philip"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Psychology","degree_department":null,"school":null,"contributors":["Willits, Jon A"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-11-26T20:35:14Z","date_published":"2019-11-26T20:35:14Z","updated_at":"2026-07-22T22:24:44Z","subjects":["language acquisition","recurrent neural network, starting small, Elman, CHILDES, child-directed speech, RNN"],"languages":["en"],"rights":["Copyright 2019 Philip Huebner"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/105710","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Willits, Jon A"]},{"key":"dc:creator","label":"Author","values":["Huebner, Philip"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-11-26T20:35:14Z","2019-07-18","2019-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Psychology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["language acquisition","recurrent neural network, starting small, Elman, CHILDES, child-directed speech, RNN"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019 Philip Huebner"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/105710"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Previous work has shown that semantic category knowledge can be captured by a distributional learning algorithm operating over naturalistic, noisy child-directed speech (Huebner & Willits, 2018). In chapter 1 of this work, I discuss the algorithm behind this study, and its ability to represent hierarchically organized and abstract knowledge. In chapter 2, I replicate the findings of Huebner & Willits (2018) using a variant of their corpus in which fewer post-processing modifications were applied to the raw transcripts. In chapter 3, I investigate whether training on input in order that children actually experience language provides any learning advantage relative to training in the reverse order Indeed, I found that semantic categorization benefits from training on input which was ordered by the age of the target child compared to input which was ordered in reverse. I refer to this effect as the age-order effect. To investigate what corpus-statistical factors may underlie the age-order effect, I explore structural differences between speech to younger vs. older children in chapter 4. In alignment with previous studies, I found that speech to younger children is syntactically less complex compared to speech to older children. Evidence for differences in semantic category structure was inconsistent. In chapter 5, I propose a number of competing explanations of the age-order effect, and identify one hypothesis, termed the good-start hypothesis, as the most promising. In chapter 6, I expand and refine the good-start hypothesis, and provide further empirical support for it. In chapter 7, I test two core assumptions of the theory developed in chapter 6 using carefully controlled artificial language corpora and find strong support for both. I close with a brief overview of findings in infant behavioral studies consistent with the theory and discuss the implications of the theory for infant acquisition of semantic category knowledge.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2019-11-26 without embargo terms","The student, Philip Huebner, accepted the attached license on 2019-07-17 at 13:17.","The student, Philip Huebner, submitted this Thesis for approval on 2019-07-17 at 13:26.","This Thesis was approved for publication on 2019-07-18 at 10:33.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14355 on 2019-11-26 at 12:53:56","Made available in DSpace on 2019-11-26T20:35:14Z (GMT). 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In chapter 1 of this work, I discuss the algorithm behind this study, and its ability to represent hierarchically organized and abstract knowledge. In chapter 2, I replicate the findings of Huebner & Willits (2018) using a variant of their corpus in which fewer post-processing modifications were applied to the raw transcripts. In chapter 3, I investigate whether training on input in order that children actually experience language provides any learning advantage relative to training in the reverse order Indeed, I found that semantic categorization benefits from training on input which was ordered by the age of the target child compared to input which was ordered in reverse. I refer to this effect as the age-order effect. To investigate what corpus-statistical factors may underlie the age-order effect, I explore structural differences between speech to younger vs. older children in chapter 4. In alignment with previous studies, I found that speech to younger children is syntactically less complex compared to speech to older children. Evidence for differences in semantic category structure was inconsistent. In chapter 5, I propose a number of competing explanations of the age-order effect, and identify one hypothesis, termed the good-start hypothesis, as the most promising. In chapter 6, I expand and refine the good-start hypothesis, and provide further empirical support for it. In chapter 7, I test two core assumptions of the theory developed in chapter 6 using carefully controlled artificial language corpora and find strong support for both. I close with a brief overview of findings in infant behavioral studies consistent with the theory and discuss the implications of the theory for infant acquisition of semantic category knowledge.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2019-11-26 without embargo terms","The student, Philip Huebner, accepted the attached license on 2019-07-17 at 13:17.","The student, Philip Huebner, submitted this Thesis for approval on 2019-07-17 at 13:26.","This Thesis was approved for publication on 2019-07-18 at 10:33.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14355 on 2019-11-26 at 12:53:56","Made available in DSpace on 2019-11-26T20:35:14Z (GMT). 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