{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/116091"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/116091","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Modeling the mechanisms of verb bias learning","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-08-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-08-01","abstract_has_math":false,"creators":["Kelley, Amanda"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Psychology","degree_department":null,"school":null,"contributors":["Dell, Gary S.","Fisher, Cynthia L.","Federmeier, Kara D.","Willits, Jon","Montag, Jessica L."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-08","date_published":"2022-08","updated_at":"2026-07-22T22:24:55Z","subjects":["cognitive modeling","verb bias","implicit learning","distributional learning"],"languages":["en","eng"],"rights":["Copyright 2022 Amanda Kelley"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/116091","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dell, Gary S.","Fisher, Cynthia L.","Federmeier, Kara D.","Willits, Jon","Montag, Jessica L."]},{"key":"dc:creator","label":"Author","values":["Kelley, Amanda"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-08","2022-07-13"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Psychology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"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":["cognitive modeling","verb bias","implicit learning","distributional learning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2022 Amanda Kelley"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/116091"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-08-01","The student, Amanda Kelley, accepted the attached license on 2022-07-12 at 10:24.","The student, Amanda Kelley, submitted this Dissertation for approval on 2022-07-12 at 11:40.","This Dissertation was approved for publication on 2022-07-13 at 13:15.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18249 on 2022-11-15 at 21:39:57","During language production, speakers acquire statistical information about linguistic units. One example of this occurs in verb bias learning, in which speakers update statistics about how likely a particular verb is to occur in a specific syntactic structure. This dissertation explores the mechanisms that support verb bias learning using a combination of empirical work and cognitive models of empirical results. Chapter 2 implements two cognitive models that reproduce previous empirical results. The models in this section behave similarly to humans when asked to learn and unlearn rules, and when they learn expected and unexpected verb-structure combinations. Chapters 3 and 4 detail two experimental investigations into how learning a new bias for one verb can transfer to another, semantically-related verb. Chapter 3 details the initial investigation, and shows that verb bias learning can transfer to similar dative verbs, but that transitive verbs show neither training nor transfer. Chapter 4 explains two follow-up norming studies to select new verb pairs, and then a behavioral replication of the study in Chapter 3. This new study shows no transfer for the new set of dative verbs, and replicates the finding of no training and no transfer for the transitive verbs. Chapter 5 uses a cognitive model to generate the results of the two dative transfer studies, and shows that the finding of transfer in Chapter 3 but not in Chapter 4 can be explained by a difference in how unexpected a structure is for each verb. Chapter 6 models the transitive results found in Chapters 3 and 4, and shows that a predictive object-first cue effectively blocks verb bias learning, consistent with the possibility that transitive learning is blocked by these or related cues. Finally, Chapter 7 addresses findings from Lin (2020) and Thothathiri and Braiuca (2021), which explore how humans switch from using verbs to predict sentence structure to using other cues. However, the models struggle to replicate these results, which suggests a need for a more complex model. This dissertation shows that many of the findings in the verb bias learning literature can be accounted for using the proposed cognitive model, and contributes new findings that also fit within this framework. Additionally, it lays out specific reasons why the model fails to account for cue-switching results, and how improvements to the model could guide further empirical research."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Modeling the mechanisms of verb bias learning"]}]}],"canonical_facts":{"dc:contributor":["Dell, Gary S.","Fisher, Cynthia L.","Federmeier, Kara D.","Willits, Jon","Montag, Jessica L."],"dc:creator":["Kelley, Amanda"],"dc:date":["2022-08","2022-07-13"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-08-01","The student, Amanda Kelley, accepted the attached license on 2022-07-12 at 10:24.","The student, Amanda Kelley, submitted this Dissertation for approval on 2022-07-12 at 11:40.","This Dissertation was approved for publication on 2022-07-13 at 13:15.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18249 on 2022-11-15 at 21:39:57","During language production, speakers acquire statistical information about linguistic units. One example of this occurs in verb bias learning, in which speakers update statistics about how likely a particular verb is to occur in a specific syntactic structure. This dissertation explores the mechanisms that support verb bias learning using a combination of empirical work and cognitive models of empirical results. Chapter 2 implements two cognitive models that reproduce previous empirical results. The models in this section behave similarly to humans when asked to learn and unlearn rules, and when they learn expected and unexpected verb-structure combinations. Chapters 3 and 4 detail two experimental investigations into how learning a new bias for one verb can transfer to another, semantically-related verb. Chapter 3 details the initial investigation, and shows that verb bias learning can transfer to similar dative verbs, but that transitive verbs show neither training nor transfer. Chapter 4 explains two follow-up norming studies to select new verb pairs, and then a behavioral replication of the study in Chapter 3. This new study shows no transfer for the new set of dative verbs, and replicates the finding of no training and no transfer for the transitive verbs. Chapter 5 uses a cognitive model to generate the results of the two dative transfer studies, and shows that the finding of transfer in Chapter 3 but not in Chapter 4 can be explained by a difference in how unexpected a structure is for each verb. Chapter 6 models the transitive results found in Chapters 3 and 4, and shows that a predictive object-first cue effectively blocks verb bias learning, consistent with the possibility that transitive learning is blocked by these or related cues. Finally, Chapter 7 addresses findings from Lin (2020) and Thothathiri and Braiuca (2021), which explore how humans switch from using verbs to predict sentence structure to using other cues. However, the models struggle to replicate these results, which suggests a need for a more complex model. This dissertation shows that many of the findings in the verb bias learning literature can be accounted for using the proposed cognitive model, and contributes new findings that also fit within this framework. Additionally, it lays out specific reasons why the model fails to account for cue-switching results, and how improvements to the model could guide further empirical research."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/116091"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Amanda Kelley"],"dc:subject":["cognitive modeling","verb bias","implicit learning","distributional learning"],"dc:title":["Modeling the mechanisms of verb bias learning"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Psychology"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:55Z"}