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  1. Infants Rapidly Learn Word-Referent Mappings Via Cross-Situational Statistics.Linda Smith & Chen Yu - 2008 - Cognition 106 (3):1558-1568.
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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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  • Word and Object.Willard Van Orman Quine - 1960 - Les Etudes Philosophiques 17 (2):278-279.
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  • Word Learning as Bayesian Inference.Fei Xu & Joshua B. Tenenbaum - 2007 - Psychological Review 114 (2):245-272.
  • Context Influences Conscious Appraisal of Cross Situational Statistical Learning.Timothy J. Poepsel & Daniel J. Weiss - 2014 - Frontiers in Psychology 5.
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  • Naming in Young Children: A Dumb Attentional Mechanism?Linda B. Smith, Susan S. Jones & Barbara Landau - 1996 - Cognition 60 (2):143-171.
  • Temporal Dynamics of Categorization: Forgetting as the Basis of Abstraction and Generalization.Haley A. Vlach & Charles W. Kalish - 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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  • Non‐Bayesian Noun Generalization in 3‐ to 5‐Year‐Old Children: Probing the Role of Prior Knowledge in the Suspicious Coincidence Effect. [REVIEW]Gavin W. Jenkins, Larissa K. Samuelson, Jodi R. Smith & John P. Spencer - 2015 - Cognitive Science 39 (2):268-306.
    It is unclear how children learn labels for multiple overlapping categories such as “Labrador,” “dog,” and “animal.” Xu and Tenenbaum suggested that learners infer correct meanings with the help of Bayesian inference. They instantiated these claims in a Bayesian model, which they tested with preschoolers and adults. Here, we report data testing a developmental prediction of the Bayesian model—that more knowledge should lead to narrower category inferences when presented with multiple subordinate exemplars. Two experiments did not support this prediction. Children (...)
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  • Detailed Behavioral Analysis as a Window Into Cross-Situational Word Learning.Sumarga H. Suanda & Laura L. Namy - 2012 - Cognitive Science 36 (3):545-559.
    Recent research has demonstrated that word learners can determine word-referent mappings by tracking co-occurrences across multiple ambiguous naming events. The current study addresses the mechanisms underlying this capacity to learn words cross-situationally. This replication and extension of Yu and Smith (2007) investigates the factors influencing both successful cross-situational word learning and mis-mappings. Item analysis and error patterns revealed that the co-occurrence structure of the learning environment as well as the context of the testing environment jointly affected learning across observations. Learners (...)
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  • Tracking Multiple Statistics: Simultaneous Learning of Object Names and Categories in English and Mandarin Speakers.Chi-Hsin Chen, Lisa Gershkoff-Stowe, Chih-Yi Wu, Hintat Cheung & Chen Yu - 2017 - Cognitive Science 41 (6):1485-1509.
    Two experiments were conducted to examine adult learners' ability to extract multiple statistics in simultaneously presented visual and auditory input. Experiment 1 used a cross-situational learning paradigm to test whether English speakers were able to use co-occurrences to learn word-to-object mappings and concurrently form object categories based on the commonalities across training stimuli. Experiment 2 replicated the first experiment and further examined whether speakers of Mandarin, a language in which final syllables of object names are more predictive of category membership (...)
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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.
  • Simultaneous Cross-Situational Learning of Category and Object Names.Tarun Gangwani, George Kachergis & Chen Yu - 2010 - In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society. pp. 1595--1600.
     
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  • All Together Now: Concurrent Learning of Multiple Structures in an Artificial Language.Alexa R. Romberg & Jenny R. Saffran - 2013 - Cognitive Science 37 (7):1290-1320.
    Natural languages contain many layers of sequential structure, from the distribution of phonemes within words to the distribution of phrases within utterances. However, most research modeling language acquisition using artificial languages has focused on only one type of distributional structure at a time. In two experiments, we investigated adult learning of an artificial language that contains dependencies between both adjacent and non-adjacent words. We found that learners rapidly acquired both types of regularities and that the strength of the adjacent statistics (...)
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