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Titlebook: Computational Cognitive Modeling and Linguistic Theory; Adrian Brasoveanu,Jakub Dotla?il Book‘‘‘‘‘‘‘‘ 2020 The Editor(s) (if applicable) a

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發(fā)表于 2025-3-21 18:57:44 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Computational Cognitive Modeling and Linguistic Theory
編輯Adrian Brasoveanu,Jakub Dotla?il
視頻videohttp://file.papertrans.cn/233/232171/232171.mp4
概述Presents an introduction to ACT-R for linguists and semanticists, with a focus on semantic phenomena in natural language.Provides a novel incremental dynamic semantic system and detailed theory of sem
叢書(shū)名稱(chēng)Language, Cognition, and Mind
圖書(shū)封面Titlebook: Computational Cognitive Modeling and Linguistic Theory; Adrian Brasoveanu,Jakub Dotla?il Book‘‘‘‘‘‘‘‘ 2020 The Editor(s) (if applicable) a
描述.This open access book introduces a general framework that allows natural language researchers to enhance existing competence theories with fully specified performance and processing components. Gradually developing increasingly complex and cognitively realistic competence-performance models, it provides running code for these models and shows how to fit them to real-time experimental data. This computational cognitive modeling approach opens up exciting new directions for research in formal semantics, and linguistics more generally, and offers new ways of (re)connecting semantics and the broader field of cognitive science..The approach of this book is novel in more ways than one. Assuming the mental architecture and procedural modalities of Anderson’s ACT-R framework, it presents fine-grained computational models of human language processing tasks which make detailed quantitative predictions that can be checked against the results of self-paced reading and other psycho-linguistic experiments. All models are presented as computer programs that readers can run on their own computer and on inputs of their choice, thereby learning to design, program and run their own models. But even
出版日期Book‘‘‘‘‘‘‘‘ 2020
關(guān)鍵詞Open Access; ACT-R Based Left-corner Parser; Incremental Dynamic Predicate Logic; Cataphoric Presupposi
版次1
doihttps://doi.org/10.1007/978-3-030-31846-8
isbn_ebook978-3-030-31846-8Series ISSN 2364-4109 Series E-ISSN 2364-4117
issn_series 2364-4109
copyrightThe Editor(s) (if applicable) and The Author(s) 2020
The information of publication is updating

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書(shū)目名稱(chēng)Computational Cognitive Modeling and Linguistic Theory被引頻次




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發(fā)表于 2025-3-21 21:57:27 | 只看該作者
Modeling Linguistic Performance, this chapter, we introduce the ‘subsymbolic’ declarative memory components of ACT-R. These are essential to modeling performance, i.e., actual human behavior in experimental tasks. We then build end-to-end models for a variety of psycholinguistic tasks—list recall, lexical decision, (self-paced) re
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發(fā)表于 2025-3-22 01:01:36 | 只看該作者
Competence-Performance Models for Lexical Access and Syntactic Parsing, with respect to the way they simulate interactions with the environment, but they are too simplistic in their assumptions about memory, since memory retrievals are not dependent on any parameters of the retrieved word. In this chapter, we will improve on both models by incorporating the ACT-R model
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發(fā)表于 2025-3-22 07:27:51 | 只看該作者
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發(fā)表于 2025-3-22 15:06:05 | 只看該作者
Kenzo Fushitani,Austen F. Riggsretrievals are not dependent on any parameters of the retrieved word. In this chapter, we will improve on both models by incorporating the ACT-R model of declarative memory we just introduced in the previous chapter.
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發(fā)表于 2025-3-22 18:17:51 | 只看該作者
Competence-Performance Models for Lexical Access and Syntactic Parsing,retrievals are not dependent on any parameters of the retrieved word. In this chapter, we will improve on both models by incorporating the ACT-R model of declarative memory we just introduced in the previous chapter.
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發(fā)表于 2025-3-23 07:26:09 | 只看該作者
Book‘‘‘‘‘‘‘‘ 2020ified performance and processing components. Gradually developing increasingly complex and cognitively realistic competence-performance models, it provides running code for these models and shows how to fit them to real-time experimental data. This computational cognitive modeling approach opens up
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