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  1. Five Ways in Which Computational Modeling Can Help Advance Cognitive Science: Lessons From Artificial Grammar Learning.Willem Zuidema, Robert M. French, Raquel G. Alhama, Kevin Ellis, Timothy J. O'Donnell, Tim Sainburg & Timothy Q. Gentner - 2020 - Topics in Cognitive Science 12 (3):925-941.
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  • Parallel Distributed Processing at 25: Further Explorations in the Microstructure of Cognition.Timothy T. Rogers & James L. McClelland - 2014 - Cognitive Science 38 (6):1024-1077.
    This paper introduces a special issue of Cognitive Science initiated on the 25th anniversary of the publication of Parallel Distributed Processing (PDP), a two-volume work that introduced the use of neural network models as vehicles for understanding cognition. The collection surveys the core commitments of the PDP framework, the key issues the framework has addressed, and the debates the framework has spawned, and presents viewpoints on the current status of these issues. The articles focus on both historical roots and contemporary (...)
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  • Modeling the Influence of Language Input Statistics on Children's Speech Production.Ingeborg Roete, Stefan L. Frank, Paula Fikkert & Marisa Casillas - 2020 - Cognitive Science 44 (12).
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  • Regularity Extraction Across Species: Associative Learning Mechanisms Shared by Human and Non‐Human Primates.Arnaud Rey, Laure Minier, Raphaëlle Malassis, Louisa Bogaerts & Joël Fagot - 2019 - Topics in Cognitive Science 11 (3):573-586.
  • Learning Higher‐Order Transitional Probabilities in Nonhuman Primates.Arnaud Rey, Joël Fagot, Fabien Mathy, Laura Lazartigues, Laure Tosatto, Guillem Bonafos, Jean-Marc Freyermuth & Frédéric Lavigne - 2022 - Cognitive Science 46 (4):e13121.
    Cognitive Science, Volume 46, Issue 4, April 2022.
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  • Forward Models and Their Implications for Production, Comprehension, and Dialogue.Martin J. Pickering & Simon Garrod - 2013 - Behavioral and Brain Sciences 36 (4):377-392.
    Our target article proposed that language production and comprehension are interwoven, with speakers making predictions of their own utterances and comprehenders making predictions of other people's utterances at different linguistic levels. Here, we respond to comments about such issues as cognitive architecture and its neural basis, learning and development, monitoring, the nature of forward models, communicative intentions, and dialogue.
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  • An Integrated Theory of Language Production and Comprehension.Martin J. Pickering & Simon Garrod - 2013 - Behavioral and Brain Sciences 36 (4):329-347.
    Currently, production and comprehension are regarded as quite distinct in accounts of language processing. In rejecting this dichotomy, we instead assert that producing and understanding are interwoven, and that this interweaving is what enables people to predict themselves and each other. We start by noting that production and comprehension are forms of action and action perception. We then consider the evidence for interweaving in action, action perception, and joint action, and explain such evidence in terms of prediction. Specifically, we assume (...)
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  • What Mechanisms Underlie Implicit Statistical Learning? Transitional Probabilities Versus Chunks in Language Learning.Pierre Perruchet - 2019 - Topics in Cognitive Science 11 (3):520-535.
  • Language Experience Changes Subsequent Learning.Luca Onnis & Erik Thiessen - 2013 - Cognition 126 (2):268-284.
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  • The Temporal Dynamics of Regularity Extraction in Non‐Human Primates.Laure Minier, Joël Fagot & Arnaud Rey - 2016 - Cognitive Science 40 (4):1019-1030.
    Extracting the regularities of our environment is one of our core cognitive abilities. To study the fine-grained dynamics of the extraction of embedded regularities, a method combining the advantages of the artificial language paradigm and the serial response time task was used with a group of Guinea baboons in a new automatic experimental device. After a series of random trials, monkeys were exposed to language-like patterns. We found that the extraction of embedded patterns positioned at the end of larger patterns (...)
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  • The Nature of the Memory Buffer in Implicit Learning: Learning Chinese Tonal Symmetries.Feifei Li, Shan Jiang, Xiuyan Guo, Zhiliang Yang & Zoltan Dienes - 2013 - Consciousness and Cognition 22 (3):920-930.
    Previous research has established that people can implicitly learn chunks, which do not require a memory buffer to process. The present study explores the implicit learning of nonlocal dependencies generated by higher than finite-state grammars, specifically, Chinese tonal retrogrades and inversions , which do require buffers . People were asked to listen to and memorize artificial poetry instantiating one of the two grammars; after this training phase, people were informed of the existence of rules and asked to classify new poems, (...)
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  • Zipfian Frequency Distributions Facilitate Word Segmentation in Context.Chigusa Kurumada, Stephan C. Meylan & Michael C. Frank - 2013 - Cognition 127 (3):439-453.
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  • Learning a Generative Probabilistic Grammar of Experience: A Process-Level Model of Language Acquisition.Oren Kolodny, Arnon Lotem & Shimon Edelman - 2015 - Cognitive Science 39 (2):227-267.
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  • Learning a Generative Probabilistic Grammar of Experience: A Process‐Level Model of Language Acquisition.Oren Kolodny, Arnon Lotem & Shimon Edelman - 2014 - Cognitive Science 38 (4):227-267.
    We introduce a set of biologically and computationally motivated design choices for modeling the learning of language, or of other types of sequential, hierarchically structured experience and behavior, and describe an implemented system that conforms to these choices and is capable of unsupervised learning from raw natural-language corpora. Given a stream of linguistic input, our model incrementally learns a grammar that captures its statistical patterns, which can then be used to parse or generate new data. The grammar constructed in this (...)
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  • The Influence of Children’s Exposure to Language From Two to Six Years: The Case of Nonword Repetition.Gary Jones - 2016 - Cognition 153:79-88.
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  • Prediction Plays a Key Role in Language Development as Well as Processing.Matt A. Johnson, Nicholas B. Turk-Browne & Adele E. Goldberg - 2013 - Behavioral and Brain Sciences 36 (4):360-361.
    Although the target article emphasizes the important role of prediction in language use, prediction may well also play a key role in the initial formation of linguistic representations, that is, in language development. We outline the role of prediction in three relevant language-learning domains: transitional probabilities, statistical preemption, and construction learning.
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  • Input and Age‐Dependent Variation in Second Language Learning: A Connectionist Account.Marius Janciauskas & Franklin Chang - 2018 - Cognitive Science 42 (S2):519-554.
    Language learning requires linguistic input, but several studies have found that knowledge of second language rules does not seem to improve with more language exposure. One reason for this is that previous studies did not factor out variation due to the different rules tested. To examine this issue, we reanalyzed grammaticality judgment scores in Flege, Yeni-Komshian, and Liu's study of L2 learners using rule-related predictors and found that, in addition to the overall drop in performance due to a sensitive period, (...)
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  • Statistically Induced Chunking Recall: A Memory‐Based Approach to Statistical Learning.Erin S. Isbilen, Stewart M. McCauley, Evan Kidd & Morten H. Christiansen - 2020 - Cognitive Science 44 (7).
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  • Chunks, Schemata, and Retrieval Structures: Past and Current Computational Models.Fernand Gobet, Peter C. R. Lane & Martyn Lloyd-Kelly - 2015 - Frontiers in Psychology 6.
  • Simultaneous Segmentation and Generalisation of Non-Adjacent Dependencies From Continuous Speech.Rebecca L. A. Frost & Padraic Monaghan - 2016 - Cognition 147 (C):70-74.
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  • Interactive Effects of Explicit Emergent Structure: A Major Challenge for Cognitive Computational Modeling.Robert M. French & Elizabeth Thomas - 2015 - Topics in Cognitive Science 7 (2):206-216.
    David Marr's three-level analysis of computational cognition argues for three distinct levels of cognitive information processing—namely, the computational, representational, and implementational levels. But Marr's levels are—and were meant to be—descriptive, rather than interactive and dynamic. For this reason, we suggest that, had Marr been writing today, he might well have gone even farther in his analysis, including the emergence of structure—in particular, explicit structure at the conceptual level—from lower levels, and the effect of explicit emergent structures on the level that (...)
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  • Predictive Movements and Human Reinforcement Learning of Sequential Action.Roy de Kleijn, George Kachergis & Bernhard Hommel - 2018 - Cognitive Science 42 (S3):783-808.
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  • Chunk Formation in Immediate Memory and How It Relates to Data Compression.Mustapha Chekaf, Nelson Cowan & Fabien Mathy - 2016 - Cognition 155 (C):96-107.
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  • Computational Modeling in Cognitive Science: A Manifesto for Change.Caspar Addyman & Robert M. French - 2012 - Topics in Cognitive Science 4 (3):332-341.
    Computational modeling has long been one of the traditional pillars of cognitive science. Unfortunately, the computer models of cognition being developed today have not kept up with the enormous changes that have taken place in computer technology and, especially, in human-computer interfaces. For all intents and purposes, modeling is still done today as it was 25, or even 35, years ago. Everyone still programs in his or her own favorite programming language, source code is rarely made available, accessibility of models (...)
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