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  1. What is Counterintuitive? Religious Cognition and Natural Expectation.Yvan I. Russell & Fernand Gobet - 2013 - Review of Philosophy and Psychology 4 (4):715-749.
    What is ‘counterintuitive’? There is general agreement that it refers to a violation of previously held knowledge, but the precise definition seems to vary with every author and study. The aim of this paper is to deconstruct the notion of ‘counterintuitive’ and provide a more philosophically rigorous definition congruent with the history of psychology, recent experimental work in ‘minimally counterintuitive’ concepts, the science vs. religion debate, and the developmental and evolutionary background of human beings. We conclude that previous definitions of (...)
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  • The Utility of Cognitive Plausibility in Language Acquisition Modeling: Evidence From Word Segmentation.Lawrence Phillips & Lisa Pearl - 2015 - Cognitive Science 39 (8):1824-1854.
    The informativity of a computational model of language acquisition is directly related to how closely it approximates the actual acquisition task, sometimes referred to as the model's cognitive plausibility. We suggest that though every computational model necessarily idealizes the modeled task, an informative language acquisition model can aim to be cognitively plausible in multiple ways. We discuss these cognitive plausibility checkpoints generally and then apply them to a case study in word segmentation, investigating a promising Bayesian segmentation strategy. We incorporate (...)
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  • Input distribution influences degree of auxiliary use by children with specific language impairment.Laurence B. Leonard & Patricia Deevy - 2011 - Cognitive Linguistics 22 (2):247-273.
    Children with specific language impairment (SLI) show a protracted period of inconsistent use of tense/agreement morphemes. The purpose of this investigation was to determine whether this inconsistent use could be attributed to the children's misinterpretations of particular syntactic structures in the input. In Study 1, preschool-aged children with SLI and typically developing peers heard sentences containing novel verbs preceded by auxiliarywasor sentences in which the novel verb formed part of a nonfinite subject-verb sequence within a larger syntactic structure (e.g.We saw (...)
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  • Defragmenting Learning.Vsevolod Kapatsinski - 2023 - Cognitive Science 47 (6):e13301.
    In the 1990s, language acquisition researchers and theoretical linguists developed an interest in learning mechanisms, and learning theorists rediscovered the verbal learning tradition. Nonetheless, learning theory and language acquisition continued to develop largely independently, which has stymied progress in both fields. However, exciting progress is happening in applying learning theory to language, and, more recently, in using language learning data to advance domain‐general learning theory. These developments raise hopes for a bidirectional flow of information between the fields. The importance of (...)
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  • Computer Simulations of Developmental Change: The Contributions of Working Memory Capacity and Long‐Term Knowledge.Gary Jones, Fernand Gobet & Julian M. Pine - 2008 - Cognitive Science 32 (7):1148-1176.
    Increasing working memory (WM) capacity is often cited as a major influence on children's development and yet WM capacity is difficult to examine independently of long‐term knowledge. A computational model of children's nonword repetition (NWR) performance is presented that independently manipulates long‐term knowledge and WM capacity to determine the relative contributions of each in explaining the developmental data. The simulations show that (a) both mechanisms independently cause the same overall developmental changes in NWR performance, (b) increase in long‐term knowledge provides (...)
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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):e12848.
    The computations involved in statistical learning have long been debated. Here, we build on work suggesting that a basic memory process, chunking, may account for the processing of statistical regularities into larger units. Drawing on methods from the memory literature, we developed a novel paradigm to test statistical learning by leveraging a robust phenomenon observed in serial recall tasks: that short‐term memory is fundamentally shaped by long‐term distributional learning. In the statistically induced chunking recall (SICR) task, participants are exposed to (...)
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  • Analogy and the brain: A new perspective on relational primacy.Usha Goswami - 2008 - Behavioral and Brain Sciences 31 (4):387-388.
    Leech et al.'s demonstration that analogical reasoning can be an emergent property of low-level incremental learning processes is critical for analogical theory. Along with insights into neural learning based on the salience of dynamic spatio-temporal structure, and the neural priming mechanism of repetition suppression, it establishes relational primacy as a plausible theoretical description of how brains make analogies.
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  • Simulating the cross-linguistic pattern of Optional Infinitive errors in children’s declaratives and Wh- questions.Daniel Freudenthal, Julian M. Pine, Gary Jones & Fernand Gobet - 2015 - Cognition 143 (C):61-76.
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  • Modeling the Developmental Patterning of Finiteness Marking in English, Dutch, German, and Spanish Using MOSAIC.Daniel Freudenthal, Julian M. Pine, Javier Aguado-Orea & Fernand Gobet - 2007 - Cognitive Science 31 (2):311-341.
    In this study, we apply MOSAIC (model of syntax acquisition in children) to the simulation of the developmental patterning of children's optional infinitive (OI) errors in 4 languages: English, Dutch, German, and Spanish. MOSAIC, which has already simulated this phenomenon in Dutch and English, now implements a learning mechanism that better reflects the theoretical assumptions underlying it, as well as a chunking mechanism that results in frequent phrases being treated as 1 unit. Using 1, identical model that learns from child‐directed (...)
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  • Developing structured representations.Leonidas A. A. Doumas & Lindsey E. Richland - 2008 - Behavioral and Brain Sciences 31 (4):384-385.
    Leech et al.'s model proposes representing relations as primed transformations rather than as structured representations (explicit representations of relations and their roles dynamically bound to fillers). However, this renders the model unable to explain several developmental trends (including relational integration and all changes not attributable to growth in relational knowledge). We suggest looking to an alternative computational model that learns structured representations from examples.
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  • Analogy as relational priming: The challenge of self-reflection.Andrea Cheshire, Linden J. Ball & Charlie N. Lewis - 2008 - Behavioral and Brain Sciences 31 (4):381-382.
    Despite its strengths, Leech et al.'s model fails to address the important benefits that derive from self-explanation and task feedback in analogical reasoning development. These components encourage explicit, self-reflective processes that do not necessarily link to knowledge accretion. We wonder, therefore, what mechanisms can be included within a connectionist framework to model self-reflective involvement and its beneficial consequences.
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  • When Children's Production Deviates From Observed Input: Modeling the Variable Production of the English Past Tense.Libby Barak, Zara Harmon, Naomi H. Feldman, Jan Edwards & Patrick Shafto - 2023 - Cognitive Science 47 (8):e13328.
    As children gradually master grammatical rules, they often go through a period of producing form‐meaning associations that were not observed in the input. For example, 2‐ to 3‐year‐old English‐learning children use the bare form of verbs in settings that require obligatory past tense meaning while already starting to produce the grammatical –ed inflection. While many studies have focused on overgeneralization errors, fewer studies have attempted to explain the root of this earlier stage of rule acquisition. In this work, we use (...)
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