{"id":{"repo_id":"potsdam-diss","oai_identifier":"oai:kobv.de-opus4-uni-potsdam:7155"},"canonical_url":"https://search.dev.ndltd.org/etd/potsdam-diss/oai:kobv.de-opus4-uni-potsdam:7155","repository":{"repo_id":"potsdam-diss","name":"Universität Potsdam - Diss","base_url":"https://publishup.uni-potsdam.de/opus4-ubp/oai"},"display":{"title":"Memory limitations in sentence comprehension","abstract":"This dissertation addresses the question of how linguistic structures can be represented in working memory. We propose a memory-based computational model that derives offline and online complexity profiles in terms of a top-down parser for minimalist grammars (Stabler, 2011). The complexity metric reflects the amount of time an item is stored in memory. The presented architecture links grammatical representations stored in memory directly to the cognitive behavior by deriving predictions about sentence processing difficulty. Results from five different sentence comprehension experiments were used to evaluate the model's assumptions about memory limitations. The predictions of the complexity metric were compared to the locality (integration and storage) cost metric of Dependency Locality Theory (Gibson, 2000). Both metrics make comparable offline and online predictions for four of the five phenomena. The key difference between the two metrics is that the proposed complexity metric accounts for the structural complexity of intervening material. In contrast, DLT's integration cost metric considers the number of discourse referents, not the syntactic structural complexity. We conclude that the syntactic analysis plays a significant role in memory requirements of parsing. An incremental top-down parser based on a grammar formalism easily computes offline and online complexity profiles, which can be used to derive predictions about sentence processing difficulty.","abstract_html":"This dissertation addresses the question of how linguistic structures can be represented in working memory. We propose a memory-based computational model that derives offline and online complexity profiles in terms of a top-down parser for minimalist grammars (Stabler, 2011). The complexity metric reflects the amount of time an item is stored in memory. The presented architecture links grammatical representations stored in memory directly to the cognitive behavior by deriving predictions about sentence processing difficulty. Results from five different sentence comprehension experiments were used to evaluate the model's assumptions about memory limitations. The predictions of the complexity metric were compared to the locality (integration and storage) cost metric of Dependency Locality Theory (Gibson, 2000). Both metrics make comparable offline and online predictions for four of the five phenomena. The key difference between the two metrics is that the proposed complexity metric accounts for the structural complexity of intervening material. In contrast, DLT's integration cost metric considers the number of discourse referents, not the syntactic structural complexity. We conclude that the syntactic analysis plays a significant role in memory requirements of parsing. An incremental top-down parser based on a grammar formalism easily computes offline and online complexity profiles, which can be used to derive predictions about sentence processing difficulty.","abstract_has_math":false,"creators":["Gerth, Sabrina"],"institution":"Universität Potsdam","degree_name":null,"degree_level":"thesis.doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Vasishth, Shravan","Kobele, Gregory"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-11-20","date_published":"2014-11-20","updated_at":"2026-07-24T03:51:50Z","subjects":["psycholinguistics","syntax","working memory","Psycholinguistik","Computermodell","Arbeitsgedächtnis","computational model"],"languages":[],"rights":["Keine öffentliche Lizenz: Unter Urheberrechtsschutz"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://publishup.uni-potsdam.de/frontdoor/index/index/docId/7155","outbound_label":"Repository record","outbound_source":"source_url"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Vasishth, Shravan","Kobele, Gregory"]},{"key":"dc:creator","label":"Author","values":["Gerth, Sabrina"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:publisher","label":"Institution","values":["Universität Potsdam"]},{"key":"dc:type","label":"Dc Type","values":["doctoralThesis"]},{"key":"thesis:degree_level","label":"Degree Level","values":["thesis.doctoral"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Universität Potsdam"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["psycholinguistics","syntax","working memory","Psycholinguistik","Computermodell","Arbeitsgedächtnis","computational model"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["Keine öffentliche Lizenz: Unter Urheberrechtsschutz"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This dissertation addresses the question of how linguistic structures can be represented in working memory. We propose a memory-based computational model that derives offline and online complexity profiles in terms of a top-down parser for minimalist grammars (Stabler, 2011). The complexity metric reflects the amount of time an item is stored in memory. The presented architecture links grammatical representations stored in memory directly to the cognitive behavior by deriving predictions about sentence processing difficulty. Results from five different sentence comprehension experiments were used to evaluate the model's assumptions about memory limitations. The predictions of the complexity metric were compared to the locality (integration and storage) cost metric of Dependency Locality Theory (Gibson, 2000). Both metrics make comparable offline and online predictions for four of the five phenomena. The key difference between the two metrics is that the proposed complexity metric accounts for the structural complexity of intervening material. In contrast, DLT's integration cost metric considers the number of discourse referents, not the syntactic structural complexity. We conclude that the syntactic analysis plays a significant role in memory requirements of parsing. An incremental top-down parser based on a grammar formalism easily computes offline and online complexity profiles, which can be used to derive predictions about sentence processing difficulty.","Diese Dissertation beschäftigt sich mit der Frage wie linguistische Strukturen im Arbeitsgedächtnis dargestellt werden. Es wurde ein gedächtnisbasiertes Computermodell entwickelt, das offline und online Komplexitätsprofile für bestimmte Satzstrukturen erstellt. Diese Komplexitätsprofile basieren auf den Berechnungen eines top-down Parsers für minimalistische Grammatiken (Stabler, 2011). Die Komplexitätsprofile spiegeln die Zeit wider, die ein Item im Gedächtnis oder Speicher gehalten wird. Das entwickelte Modell verbindet grammatische Repräsentationen, die im Speicher gehalten werden, direkt mit dem kognitiven menschlichen Verhalten in Sprachverarbeitungsexperimenten indem es Vorhersagen über Satzverarbeitungsschwierigkeiten trifft. Die Ergebnisse von fünf Satzverarbeitungsexperimenten wurden verwendet, um die Vorhersagen des Modells über die Begrenzungen des Gedächtnisses zu evaluieren. Die Vorhersagen der Komplexitätsmetrik wurden mit den Vorhersagen einer in der Psycholinguistik etablierten Komplexitätsmetrik (Dependency Locality Theory, DLT (Gibson, 2000)) verglichen. Beide Metriken machen vergleichbare offline und online Vorhersagen für vier der fünf Experimente. Der Unterschied zwischen beiden Metriken ist, dass die in dieser Dissertation erarbeitete Komplexitätsmetrik die strukturelle Komplexität der Satzstruktur mit einbezieht. Im Gegensatz zu DLTs Integrationsmetrik, die die Komplexität eines Satzes lediglich anhand der Anzahl der Wörter misst. Diese Dissertation zeigt, dass ein inkrementeller Top-down Parser, der auf einem Grammatikformalismus basiert, offline und online Komplexitätsprofile berechnen kann, die verwendet werden können, um Vorhersagen über Satzverarbeitungsschwierigkeiten zu treffen."]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Memory limitations in sentence comprehension","Begrenzungen des Arbeitsgedächtnisses in der Satzverarbeitung"]}]}],"canonical_facts":{"dc:contributor":["Vasishth, Shravan","Kobele, Gregory"],"dc:creator":["Gerth, Sabrina"],"dc:description.abstract":["This dissertation addresses the question of how linguistic structures can be represented in working memory. We propose a memory-based computational model that derives offline and online complexity profiles in terms of a top-down parser for minimalist grammars (Stabler, 2011). The complexity metric reflects the amount of time an item is stored in memory. The presented architecture links grammatical representations stored in memory directly to the cognitive behavior by deriving predictions about sentence processing difficulty. Results from five different sentence comprehension experiments were used to evaluate the model's assumptions about memory limitations. The predictions of the complexity metric were compared to the locality (integration and storage) cost metric of Dependency Locality Theory (Gibson, 2000). Both metrics make comparable offline and online predictions for four of the five phenomena. The key difference between the two metrics is that the proposed complexity metric accounts for the structural complexity of intervening material. In contrast, DLT's integration cost metric considers the number of discourse referents, not the syntactic structural complexity. We conclude that the syntactic analysis plays a significant role in memory requirements of parsing. An incremental top-down parser based on a grammar formalism easily computes offline and online complexity profiles, which can be used to derive predictions about sentence processing difficulty.","Diese Dissertation beschäftigt sich mit der Frage wie linguistische Strukturen im Arbeitsgedächtnis dargestellt werden. Es wurde ein gedächtnisbasiertes Computermodell entwickelt, das offline und online Komplexitätsprofile für bestimmte Satzstrukturen erstellt. Diese Komplexitätsprofile basieren auf den Berechnungen eines top-down Parsers für minimalistische Grammatiken (Stabler, 2011). Die Komplexitätsprofile spiegeln die Zeit wider, die ein Item im Gedächtnis oder Speicher gehalten wird. Das entwickelte Modell verbindet grammatische Repräsentationen, die im Speicher gehalten werden, direkt mit dem kognitiven menschlichen Verhalten in Sprachverarbeitungsexperimenten indem es Vorhersagen über Satzverarbeitungsschwierigkeiten trifft. Die Ergebnisse von fünf Satzverarbeitungsexperimenten wurden verwendet, um die Vorhersagen des Modells über die Begrenzungen des Gedächtnisses zu evaluieren. Die Vorhersagen der Komplexitätsmetrik wurden mit den Vorhersagen einer in der Psycholinguistik etablierten Komplexitätsmetrik (Dependency Locality Theory, DLT (Gibson, 2000)) verglichen. Beide Metriken machen vergleichbare offline und online Vorhersagen für vier der fünf Experimente. Der Unterschied zwischen beiden Metriken ist, dass die in dieser Dissertation erarbeitete Komplexitätsmetrik die strukturelle Komplexität der Satzstruktur mit einbezieht. Im Gegensatz zu DLTs Integrationsmetrik, die die Komplexität eines Satzes lediglich anhand der Anzahl der Wörter misst. Diese Dissertation zeigt, dass ein inkrementeller Top-down Parser, der auf einem Grammatikformalismus basiert, offline und online Komplexitätsprofile berechnen kann, die verwendet werden können, um Vorhersagen über Satzverarbeitungsschwierigkeiten zu treffen."],"dc:format.medium":["application/pdf"],"dc:publisher":["Universität Potsdam"],"dc:rights":["Keine öffentliche Lizenz: Unter Urheberrechtsschutz"],"dc:subject":["psycholinguistics","syntax","working memory","Psycholinguistik","Computermodell","Arbeitsgedächtnis","computational model"],"dc:title":["Memory limitations in sentence comprehension","Begrenzungen des Arbeitsgedächtnisses in der Satzverarbeitung"],"dc:type":["doctoralThesis"],"thesis:degree_level":["thesis.doctoral"],"thesis:institution_name":["Universität Potsdam"]},"updated_at":"2026-07-24T03:51:50Z"}