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  1. Word Learning Under Infinite Uncertainty.Richard A. Blythe, Andrew D. M. Smith & Kenny Smith - 2016 - Cognition 151:18-27.
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  • Cognitive Science in the Era of Artificial Intelligence: A Roadmap for Reverse-Engineering the Infant Language-Learner.Emmanuel Dupoux - 2018 - Cognition 173:43-59.
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  • Integrating Constraints for Learning Word–Referent Mappings.Padraic Monaghan & Karen Mattock - 2012 - Cognition 123 (1):133-143.
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  • Attention and Word Learning in Autistic, Language Delayed and Typically Developing Children.Elena J. Tenenbaum, Dima Amso, Beau Abar & Stephen J. Sheinkopf - 2014 - Frontiers in Psychology 5.
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  • Gavagai Is as Gavagai Does: Learning Nouns and Verbs From Cross‐Situational Statistics.Padraic Monaghan, Karen Mattock, Robert A. I. Davies & Alastair C. Smith - 2015 - Cognitive Science 39 (5):1099-1112.
    Learning to map words onto their referents is difficult, because there are multiple possibilities for forming these mappings. Cross-situational learning studies have shown that word-object mappings can be learned across multiple situations, as can verbs when presented in a syntactic context. However, these previous studies have presented either nouns or verbs in ambiguous contexts and thus bypass much of the complexity of multiple grammatical categories in speech. We show that noun word learning in adults is robust when objects are moving, (...)
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  • Self Domestication and the Evolution of Language.James Thomas & Simon Kirby - 2018 - Biology and Philosophy 33 (1-2):9.
    We set out an account of how self-domestication plays a crucial role in the evolution of language. In doing so, we focus on the growing body of work that treats language structure as emerging from the process of cultural transmission. We argue that a full recognition of the importance of cultural transmission fundamentally changes the kind of questions we should be asking regarding the biological basis of language structure. If we think of language structure as reflecting an accumulated set of (...)
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  • Pragmatically Framed Cross-Situational Noun Learning Using Computational Reinforcement Models.Shamima Najnin & Bonny Banerjee - 2018 - Frontiers in Psychology 9.
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  • Retrieval Dynamics and Retention in Cross‐Situational Statistical Word Learning.Haley A. Vlach & Catherine M. Sandhofer - 2014 - Cognitive Science 38 (4):757-774.
    Previous research on cross-situational word learning has demonstrated that learners are able to reduce ambiguity in mapping words to referents by tracking co-occurrence probabilities across learning events. In the current experiments, we examined whether learners are able to retain mappings over time. The results revealed that learners are able to retain mappings for up to 1 week later. However, there were interactions between the amount of retention and the different learning conditions. Interestingly, the strongest retention was associated with a learning (...)
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  • Competitive Processes in Cross‐Situational Word Learning.Daniel Yurovsky, Chen Yu & Linda B. Smith - 2013 - Cognitive Science 37 (5):891-921.
    Cross-situational word learning, like any statistical learning problem, involves tracking the regularities in the environment. However, the information that learners pick up from these regularities is dependent on their learning mechanism. This article investigates the role of one type of mechanism in statistical word learning: competition. Competitive mechanisms would allow learners to find the signal in noisy input and would help to explain the speed with which learners succeed in statistical learning tasks. Because cross-situational word learning provides information at multiple (...)
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  • The Interplay of Cross‐Situational Word Learning and Sentence‐Level Constraints.Judith Koehne & Matthew W. Crocker - 2015 - Cognitive Science 39 (5):849-889.
    A variety of mechanisms contribute to word learning. Learners can track co-occurring words and referents across situations in a bottom-up manner. Equally, they can exploit sentential contexts, relying on top–down information such as verb–argument relations and world knowledge, offering immediate constraints on meaning. When combined, CSWL and SLCL potentially modulate each other's influence, revealing how word learners deal with multiple mechanisms simultaneously: Do they use all mechanisms? Prefer one? Is their strategy context dependent? Three experiments conducted with adult learners reveal (...)
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  • A Computational Model for the Item‐Based Induction of Construction Networks.Judith Gaspers & Philipp Cimiano - 2014 - Cognitive Science 38 (2):439-488.
    According to usage-based approaches to language acquisition, linguistic knowledge is represented in the form of constructions—form-meaning pairings—at multiple levels of abstraction and complexity. The emergence of syntactic knowledge is assumed to be a result of the gradual abstraction of lexically specific and item-based linguistic knowledge. In this article, we explore how the gradual emergence of a network consisting of constructions at varying degrees of complexity can be modeled computationally. Linguistic knowledge is learned by observing natural language utterances in an ambiguous (...)
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  • An Integrative Account of Constraints on Cross-Situational Learning.Daniel Yurovsky & Michael C. Frank - 2015 - Cognition 145:53-62.
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  • 2.5-Year-Olds Use Cross-Situational Consistency to Learn Verbs Under Referential Uncertainty.Rose M. Scott & Cynthia Fisher - 2012 - Cognition 122 (2):163-180.
  • Memory Constraints on Infants' Cross-Situational Statistical Learning.Haley A. Vlach & Scott P. Johnson - 2013 - Cognition 127 (3):375-382.
  • Iconicity and the Emergence of Combinatorial Structure in Language.Tessa Verhoef, Simon Kirby & Bart Boer - 2016 - Cognitive Science 40 (8):1969-1994.
    In language, recombination of a discrete set of meaningless building blocks forms an unlimited set of possible utterances. How such combinatorial structure emerged in the evolution of human language is increasingly being studied. It has been shown that it can emerge when languages culturally evolve and adapt to human cognitive biases. How the emergence of combinatorial structure interacts with the existence of holistic iconic form-meaning mappings in a language is still unknown. The experiment presented in this paper studies the role (...)
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  • The Pursuit of Word Meanings.Stevens Jon Scott, R. Gleitman Lila, C. Trueswell John & Yang Charles - 2017 - Cognitive Science 41 (S4):638-676.
    We evaluate here the performance of four models of cross-situational word learning: two global models, which extract and retain multiple referential alternatives from each word occurrence; and two local models, which extract just a single referent from each occurrence. One of these local models, dubbed Pursuit, uses an associative learning mechanism to estimate word-referent probability but pursues and tests the best referent-meaning at any given time. Pursuit is found to perform as well as global models under many conditions extracted from (...)
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