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Computational Cognitive Modeling and Linguistic Theory

Part of the Language, Cognition, and Mind Book Series (volume 6)

DOI: 10.1007/978-3-030-31846-8

ISBN: 978-3-030-31844-4

Publisher: Springer International Publishing - Springer Journals

May 15, 2020

1 - 10 of 10 Chapters

LitStream

Chapter 1Introduction

Brasoveanu, Adrian; Dotlačil, Jakub

Pages: 1 –5

[In this brief chapter, we summarize the background knowledge needed to be able to work through the book (Sect. 1.1). After that, we provide an overview of the remainder of the book (Sect. 1.2).]
LitStream

Chapter 2The ACT-R Cognitive Architecture and Its pyactr Implementation

Brasoveanu, Adrian; Dotlačil, Jakub

Pages: 7 –37

[In this chapter, we introduce the ACT-R cognitive architecture and the Python3 implementation pyactr we use throughout the book. We end with a basic ACT-R model for subject-verb agreement.]
LitStream

Chapter 3The Basics of Syntactic Parsing in ACT-R

Brasoveanu, Adrian; Dotlačil, Jakub

Pages: 39 –56

[In this chapter, we introduce the basics of syntactic parsing in ACT-R. We build a top-down parser and learn how we can extract intermediate stages of pyactr simulations. This enables us to inspect detailed snapshots of the cognitive states that our processing models predict.]
LitStream

Chapter 4Syntax as a Cognitive Process: Left-Corner Parsing with Visual and Motor Interfaces

Brasoveanu, Adrian; Dotlačil, Jakub

Pages: 57 –82

[In the previous chapters, we introduced and used several ACT-R modules and buffers: the declarative memory module and the associated retrieval buffer, the procedural memory module and the associated goal buffer, and the imaginal buffer. These are core ACT-R modules, but focusing exclusively on...
LitStream

Chapter 5Brief Introduction to Bayesian Methods and pymc3 for Linguists

Brasoveanu, Adrian; Dotlačil, Jakub

Pages: 83 –103

[In this chapter, we introduce the basics of Bayesian statistical modeling. Bayesian methods are not specific to ACT-R, or to cognitive modeling. They are a general framework for doing plausible inference based on data—both categorical (‘symbolic’) and numerical (‘subsymbolic’) data.]
LitStream

Chapter 6Modeling Linguistic Performance

Brasoveanu, Adrian; Dotlačil, Jakub

Pages: 105 –132

[The goal of ACT-R is to provide accurate cognitive models of learning and performance, as well as accurate neural mappings of cognitive activities. In this chapter, we introduce the ‘subsymbolic’ declarative memory components of ACT-R. These are essential to modeling performance, i.e., actual...
LitStream

Chapter 7Competence-Performance Models for Lexical Access and Syntactic Parsing

Brasoveanu, Adrian; Dotlačil, Jakub

Pages: 133 –167

[In Chap. 4, we introduced a simple lexical decision task and a simple left-corner parser. The models we introduced in that chapter might be sufficient with respect to the way they simulate interactions with the environment, but they are too simplistic in their assumptions about memory, since...
LitStream

Chapter 8Semantics as a Cognitive Process I: Discourse Representation Structures in Declarative Memory

Brasoveanu, Adrian; Dotlačil, Jakub

Pages: 169 –205

[In this chapter, we introduce our assumptions about semantic representations and build a semantic processor, that is, a basic parser able to incrementally construct such semantic representations. Our choice for a processing-friendly semantics framework is Discourse Representation Theory (DRT,...
LitStream

Chapter 9Semantics as a Cognitive Process II: Active Search for Cataphora Antecedents and the Semantics of Conditionals

Brasoveanu, Adrian; Dotlačil, Jakub

Pages: 207 –281

[In this chapter, we generalize our eager left-corner incremental interpreter to cover conditionals and conjunctions. We focus on the (dynamic) semantic contrast between conditionals and conjunctions because the interaction between these sentential operators and anaphora/cataphora provides a...
LitStream

Chapter 10Future Directions

Brasoveanu, Adrian; Dotlačil, Jakub

Pages: 283 –285

[Where do we go from here? We will keep this short because, if the reader has made it this far, the answer really is: in whatever direction the reader’s research interests lie.]

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