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Defragmenting Learning

Cognitive Science 47 (6):e13301 (2023)

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  1. (1 other version)The Myth of Cognitive Decline: Non‐Linear Dynamics of Lifelong Learning.Michael Ramscar, Peter Hendrix, Cyrus Shaoul, Petar Milin & Harald Baayen - 2014 - Topics in Cognitive Science 6 (1):5-42.
    As adults age, their performance on many psychometric tests changes systematically, a finding that is widely taken to reveal that cognitive information-processing capacities decline across adulthood. Contrary to this, we suggest that older adults'; changing performance reflects memory search demands, which escalate as experience grows. A series of simulations show how the performance patterns observed across adulthood emerge naturally in learning models as they acquire knowledge. The simulations correctly identify greater variation in the cognitive performance of older adults, and successfully (...)
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  • Modeling the Development of Children's Use of Optional Infinitives in Dutch and English Using MOSAIC.Daniel Freudenthal, Julian M. Pine & Fernand Gobet - 2006 - Cognitive Science 30 (2):277-310.
    In this study we use a computational model of language learning called model of syntax acquisition in children (MOSAIC) to investigate the extent to which the optional infinitive (OI) phenomenon in Dutch and English can be explained in terms of a resource-limited distributional analysis of Dutch and English child-directed speech. The results show that the same version of MOSAIC is able to simulate changes in the pattern of finiteness marking in 2 children learning Dutch and 2 children learning English as (...)
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  • Language learning as language use: A cross-linguistic model of child language development.Stewart M. McCauley & Morten H. Christiansen - 2019 - Psychological Review 126 (1):1-51.
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  • Prediction and error in early infant speech learning: A speech acquisition model.Jessie S. Nixon & Fabian Tomaschek - 2021 - Cognition 212 (C):104697.
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  • Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory.James L. McClelland, Bruce L. McNaughton & Randall C. O'Reilly - 1995 - Psychological Review 102 (3):419-457.
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  • Pigeons acquire multiple categories in parallel via associative learning: A parallel to human word learning?Edward A. Wasserman, Daniel I. Brooks & Bob McMurray - 2015 - Cognition 136 (C):99-122.
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  • Granularity and the acquisition of grammatical gender: How order-of-acquisition affects what gets learned.Inbal Arnon & Michael Ramscar - 2012 - Cognition 122 (3):292-305.
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  • Of mice and men: Speech sound acquisition as discriminative learning from prediction error, not just statistical tracking.Jessie S. Nixon - 2020 - Cognition 197 (C):104081.
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  • A neuropsychological theory of multiple systems in category learning.F. Gregory Ashby, Leola A. Alfonso-Reese, And U. Turken & Elliott M. Waldron - 1998 - Psychological Review 105 (3):442-481.
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  • Weighted Constraints in Generative Linguistics.Joe Pater - 2009 - Cognitive Science 33 (6):999-1035.
    Harmonic Grammar (HG) and Optimality Theory (OT) are closely related formal frameworks for the study of language. In both, the structure of a given language is determined by the relative strengths of a set of constraints. They differ in how these strengths are represented: as numerical weights (HG) or as ranks (OT). Weighted constraints have advantages for the construction of accounts of language learning and other cognitive processes, partly because they allow for the adaptation of connectionist and statistical models. HG (...)
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  • The Effects of Feature-Label-Order and Their Implications for Symbolic Learning.Michael Ramscar, Daniel Yarlett, Melody Dye, Katie Denny & Kirsten Thorpe - 2010 - Cognitive Science 34 (6):909-957.
    Symbols enable people to organize and communicate about the world. However, the ways in which symbolic knowledge is learned and then represented in the mind are poorly understood. We present a formal analysis of symbolic learning—in particular, word learning—in terms of prediction and cue competition, and we consider two possible ways in which symbols might be learned: by learning to predict a label from the features of objects and events in the world, and by learning to predict features from a (...)
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  • Learning mechanisms in cue reweighting.Zara Harmon, Kaori Idemaru & Vsevolod Kapatsinski - 2019 - Cognition 189 (C):76-88.
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  • An amorphous model for morphological processing in visual comprehension based on naive discriminative learning.R. Harald Baayen, Petar Milin, Dusica Filipović Đurđević, Peter Hendrix & Marco Marelli - 2011 - Psychological Review 118 (3):438-481.
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  • Implicit Statistical Learning: A Tale of Two Literatures.Morten H. Christiansen - 2019 - Topics in Cognitive Science 11 (3):468-481.
    In this review article, Christiansen provides a historical perspective on the two research traditions, implicit learning and statistical learning, thus nicely setting the scene for this special issue of Topics in Cognitive Science. In this “tale of two literatures”, he first traces the history of both literatures before sketching a framework that provides a basis for understanding implicit learning and statistical learning as a unified phenomenon.
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  • Echoes of echoes? An episodic theory of lexical access.Stephen D. Goldinger - 1998 - Psychological Review 105 (2):251-279.
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