{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129985"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129985","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Evaluating the impact of channel density on representational similarity analysis of prediction-related effects in language","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms","abstract_has_math":false,"creators":["Gomaa, Aya"],"institution":"University of Illinois Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Psychology","degree_department":null,"school":null,"contributors":["Federmeier, Kara D.","Beck, Diane M."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-07-23","date_published":"2025-07-23","updated_at":"2026-07-22T22:25:06Z","subjects":["Representational Similarity Analysis (rsa)","Eeg"],"languages":["en","eng"],"rights":["Copyright 2025 Aya Gomaa"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129985","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Federmeier, Kara D.","Beck, Diane M."]},{"key":"dc:creator","label":"Author","values":["Gomaa, Aya"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-07-23","2025-08"]},{"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 Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Representational Similarity Analysis (rsa)","Eeg"]}]},{"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 2025 Aya Gomaa"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129985"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms","The student, Aya Gomaa, accepted the attached license on 2025-07-23 at 14:57.","The student, Aya Gomaa, submitted this Thesis for approval on 2025-07-23 at 14:59.","This Thesis was approved for publication on 2025-07-23 at 15:51.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22710 on 2025-10-20 at 20:15:43","This study investigated the impact of EEG channel density on language prediction-related effects using Representational Similarity Analysis (RSA), a multivariate technique that captures evolving neural similarity patterns over time when combined with EEG. Building on Hubbard and Federmeier (2021), we tested whether RSA could detect the pre-activation of upcoming linguistic input by correlating neural activity immediately before and after the onset of critical words. Similarity was expected to scale with the cloze probability of these final words, serving as an index of anticipatory processing. To examine how spatial resolution influences RSA signals, we manipulated EEG channel density. Participants read sentences with varying degrees of contextual constraint, with final words always being the best completion, spanning a continuous range of cloze probabilities (10%–100%). EEG data from 24 participants were analyzed across four spatial configurations (13, 26, 50, and 61 channels). A standard ERP analysis confirmed a graded N400 effect, validating sensitivity to cloze probability. Spatial RSA revealed increased similarity between pre-final and final word activity in an early time window (116–248 ms); however, this increase was not modulated by cloze probability, and no graded similarity pattern emerged across any configuration. The absence of graded effects may not reflect a limitation of the RSA method, but rather a shift in participants’ cognitive strategy under the experimental conditions—particularly given the exclusive use of expected sentence endings, in contrast to Hubbard and Federmeier’s use of both expected and unexpected completions. This lack of unexpected endings may have reduced participants’ motivation to actively monitor their predictions, as they were no longer confronted with prediction errors that would prompt ongoing engagement. Although higher-density EEG configurations (especially the 61-channel setup) produced greater overall RSA similarity, they did not enhance sensitivity to prediction-related effects. One possibility is that increasing spatial density beyond a certain threshold introduces redundancy or noise, potentially masking condition-specific patterns. Alternatively, participants may not have engaged predictive mechanisms at all, as suggested by the lack of graded effects. These findings highlight the importance of jointly considering methodological and task-related factors when interpreting RSA results in the context of language prediction research."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Evaluating the impact of channel density on representational similarity analysis of prediction-related effects in language"]}]}],"canonical_facts":{"dc:contributor":["Federmeier, Kara D.","Beck, Diane M."],"dc:creator":["Gomaa, Aya"],"dc:date":["2025-07-23","2025-08"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms","The student, Aya Gomaa, accepted the attached license on 2025-07-23 at 14:57.","The student, Aya Gomaa, submitted this Thesis for approval on 2025-07-23 at 14:59.","This Thesis was approved for publication on 2025-07-23 at 15:51.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22710 on 2025-10-20 at 20:15:43","This study investigated the impact of EEG channel density on language prediction-related effects using Representational Similarity Analysis (RSA), a multivariate technique that captures evolving neural similarity patterns over time when combined with EEG. Building on Hubbard and Federmeier (2021), we tested whether RSA could detect the pre-activation of upcoming linguistic input by correlating neural activity immediately before and after the onset of critical words. Similarity was expected to scale with the cloze probability of these final words, serving as an index of anticipatory processing. To examine how spatial resolution influences RSA signals, we manipulated EEG channel density. Participants read sentences with varying degrees of contextual constraint, with final words always being the best completion, spanning a continuous range of cloze probabilities (10%–100%). EEG data from 24 participants were analyzed across four spatial configurations (13, 26, 50, and 61 channels). A standard ERP analysis confirmed a graded N400 effect, validating sensitivity to cloze probability. Spatial RSA revealed increased similarity between pre-final and final word activity in an early time window (116–248 ms); however, this increase was not modulated by cloze probability, and no graded similarity pattern emerged across any configuration. The absence of graded effects may not reflect a limitation of the RSA method, but rather a shift in participants’ cognitive strategy under the experimental conditions—particularly given the exclusive use of expected sentence endings, in contrast to Hubbard and Federmeier’s use of both expected and unexpected completions. This lack of unexpected endings may have reduced participants’ motivation to actively monitor their predictions, as they were no longer confronted with prediction errors that would prompt ongoing engagement. Although higher-density EEG configurations (especially the 61-channel setup) produced greater overall RSA similarity, they did not enhance sensitivity to prediction-related effects. One possibility is that increasing spatial density beyond a certain threshold introduces redundancy or noise, potentially masking condition-specific patterns. Alternatively, participants may not have engaged predictive mechanisms at all, as suggested by the lack of graded effects. These findings highlight the importance of jointly considering methodological and task-related factors when interpreting RSA results in the context of language prediction research."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129985"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Aya Gomaa"],"dc:subject":["Representational Similarity Analysis (rsa)","Eeg"],"dc:title":["Evaluating the impact of channel density on representational similarity analysis of prediction-related effects in language"],"dc:type":["text"],"thesis:degree_discipline":["Psychology"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:06Z"}