Computational Cognitive Modeling and Linguistic Theory.

• Superior document: Language, Cognition, and Mind Series ; v.6
• Author: Brasoveanu, Adrian.
• TeilnehmendeR: Dotlačil, Jakub.
• Place / Publishing House: Cham : : Springer International Publishing AG,, 2020.; ©2020.
• Year of Publication: 2020
• Edition: 1st ed.
• Language: English
• Series: Language, Cognition, and Mind Series
• Physical Description: 1 online resource (299 pages)

Table of Contents

Intro -- Foreword and Acknowledgments -- Contents -- 1 Introduction -- 1.1 Background Knowledge -- 1.2 The Structure of the Book -- 2 The ACT-R Cognitive Architecture and Its pyactr Implementation -- 2.1 Cognitive Architectures and ACT-R -- 2.2 ACT-R in Cognitive Science and Linguistics -- 2.3 ACT-R Implementation -- 2.4 Knowledge in ACT-R -- 2.4.1 Declarative Memory: Chunks -- 2.4.2 Procedural Memory: Productions -- 2.5 The Basics of pyactr: Declaring Chunks -- 2.6 Modules and Buffers -- 2.7 Writing Productions in pyactr -- 2.8 Running Our First Model -- 2.9 Some More Models -- 2.9.1 The Counting Model -- 2.9.2 Regular Grammars in ACT-R -- 2.9.3 Counter Automata in ACT-R -- 2.10 Appendix: The Four Models for Agreement, Counting, Regular Grammars and Counter Automata -- 3 The Basics of Syntactic Parsing in ACT-R -- 3.1 Top-Down Parsing -- 3.2 Building a Top-Down Parser in pyactr -- 3.2.1 Modules, Buffers, and the Lexicon -- 3.2.2 Production Rules -- 3.3 Running the Model -- 3.4 Failures to Parse and Taking Snapshots of the Mind When It Fails -- 3.5 Top-Down Parsing as an Imperfect Psycholinguistic Model -- 3.6 Appendix: The Top-Down Parser -- 4 Syntax as a Cognitive Process: Left-Corner Parsing with Visual and Motor Interfaces -- 4.1 The Environment in ACT-R: Modeling Lexical Decision Tasks -- 4.1.1 The Visual Module -- 4.1.2 The Motor Module -- 4.2 The Lexical Decision Model: Productions -- 4.3 Running the Lexical Decision Model and Understanding the Output -- 4.3.1 Visual Processes in Our Lexical Decision Model -- 4.3.2 Manual Processes in Our Lexical Decision Model -- 4.4 A Left-Corner Parser with Visual and Motor Interfaces -- 4.5 Appendix: The Lexical Decision Model -- 5 Brief Introduction to Bayesian Methods and pymc3 for Linguists -- 5.1 The Python Libraries We Need -- 5.2 The Data.

5.3 Prior Beliefs and the Basics of pymc3, matplotlib and seaborn -- 5.4 Our Function for Generating the Data (The Likelihood) -- 5.5 Posterior Beliefs: Estimating the Model Parameters and Answering the Theoretical Question -- 5.6 Conclusion -- 5.7 Appendix -- 6 Modeling Linguistic Performance -- 6.1 The Power Law of Forgetting -- 6.2 The Base Activation Equation -- 6.3 The Attentional Weighting Equation -- 6.4 Activation, Retrieval Probability and Retrieval Latency -- 6.5 Appendix -- 7 Competence-Performance Models for Lexical Access and Syntactic Parsing -- 7.1 The Log-Frequency Model of Lexical Decision -- 7.2 The Simplest ACT-R Model of Lexical Decision -- 7.3 The Second ACT-R Model of Lexical Decision: Adding the Latency Exponent -- 7.4 Bayes+ACT-R: Quantitative Comparison for Qualitative Theories -- 7.4.1 The Bayes+ACT-R Lexical Decision Model Without the Imaginal Buffer -- 7.4.2 Bayes+ACT-R Lexical Decision with Imaginal-Buffer Involvement and Default Encoding Delay for the Imaginal Buffer -- 7.4.3 Bayes+ACT-R Lexical Decision with Imaginal Buffer and 0 Delay -- 7.5 Modeling Self-paced Reading with a Left-Corner Parser -- 7.6 Conclusion -- 7.7 Appendix: The Bayes and Bayes+ACT-R Models -- 7.7.1 Lexical Decision Models -- 7.7.2 Left-Corner Parser Models -- 8 Semantics as a Cognitive Process I: Discourse Representation Structures in Declarative Memory -- 8.1 The Fan Effect and the Retrieval of DRSs from Declarative Memory -- 8.2 The Fan Effect Reflects the Way Meaning Representations (DRSs) Are Organized in Declarative Memory -- 8.3 Integrating ACT-R and DRT: An Eager Left-Corner Syntax/Semantics Parser -- 8.4 Semantic (Truth-Value) Evaluation as Memory Retrieval, and Fitting the Model to Data -- 8.5 Model Discussion and Summary -- 8.6 Appendix: End-to-End Model of the Fan Effect with an Explicit Syntax/Semantics Parser.

8.6.1 File ch8/parser_dm_fan.py -- 8.6.2 File ch8/parser_rules_fan.py -- 8.6.3 File ch8/run_parser_fan.py -- 8.6.4 File ch8/estimate_parser_fan.py -- 9 Semantics as a Cognitive Process II: Active Search for Cataphora Antecedents and the Semantics of Conditionals -- 9.1 Two Experiments Studying the Interaction Between Conditionals and Cataphora -- 9.1.1 Experiment 1: Anaphora Versus Cataphora in Conjunctions Versus Conditionals -- 9.1.2 Experiment 2: Cataphoric Presuppositions in Conjunctions Versus Conditionals -- 9.2 Mechanistic Processing Models as an Explanatory Goal for Semantics -- 9.3 Modeling the Interaction of Conditionals and Pronominal Cataphora -- 9.3.1 Chunk Types and the Lexical Information Stored in Declarative Memory -- 9.3.2 Rules to Advance Dref Peg Positions, Key Presses and Word-Related Rules -- 9.3.3 Phrase Structure Rules -- 9.3.4 Rules for Conjunctions and Anaphora Resolution -- 9.3.5 Rules for Conditionals and Cataphora Resolution -- 9.4 Modeling the Interaction of Conditionals and Cataphoric Presuppositions -- 9.4.1 Rules for `Again' and Presupposition Resolution -- 9.4.2 Rules for `Maximize Presupposition' -- 9.4.3 Fitting the Model to the Experiment 2 Data -- 9.5 Conclusion -- 9.6 Appendix: The Complete Syntax/Semantics Parser -- 9.6.1 File ch9/parser_dm.py -- 9.6.2 File ch9/parser_rules.py -- 9.6.3 File ch9/run_parser.py -- 9.6.4 File ch9/estimate_parser_parallel.py -- 10 Future Directions -- Appendix Bibliography.