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.<\/p>","protected":false},"excerpt":{"rendered":"
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.<\/p>\n","protected":false},"featured_media":54137,"template":"","meta":{"_acf_changed":false},"product_brand":[],"product_cat":[6229],"product_tag":[5632],"class_list":{"0":"post-51985","1":"product","2":"type-product","3":"status-publish","4":"has-post-thumbnail","6":"product_cat-sprachwissenschaft-literaturwissenschaft-web-kategorie","7":"product_tag-arbeitsgedaechtnis-computermodell-psycholinguistik-syntax","8":"autor-sabrina-gerth","10":"first","11":"instock","12":"shipping-taxable","13":"purchasable","14":"product-type-simple"},"acf":[],"yoast_head":"\n