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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 11 of 11 1 Developing semantic knowledge through cross-situational word learning George Kachergis; Chen Yu; Richard M. Shiffrin In: http://www.kachergis.com/docs/kachergis_yu_shiffrin_2014_priming.pdf (2014) BASE Show details 2 Reviewed by: George Kachergis; Chen Yu; Richard M. Shiffrin... In: http://www.kachergis.com/docs/kachergis_yu_shiffrin_2014frontiers_implicit.pdf (2014) BASE Show details 3 Actively learning object names across ambiguous situations George Kachergis; Chen Yu; Richard M. Shiffrin In: http://www.kachergis.com/docs/kachergis_etal2013_active.pdf (2013) BASE Show details 4 Actively Learning Object Names Across Ambiguous Situations George Kachergis; Chen Yu; Richard M. Shiffrin In: http://www.indiana.edu/~dll/papers/kachergis2012_active_topics.pdf (2012) BASE Show details 5 Frequency and contextual diversity effects in cross-situational word learning George Kachergis; Chen Yu; Richard M. Shiffrin In: http://141.14.165.6/CogSci09/papers/521/paper521.pdf (2009) BASE Show details 6 Prior knowledge bootstraps cross-situational learning Krystal A. Klein; Chen Yu; Richard M. Shiffrin In: http://csjarchive.cogsci.rpi.edu/Proceedings/2008/pdfs/p1930.pdf (2008) BASE Show details 7 Cross-Situational Statistical Learning: Implicit or Intentional? George Kachergis; Chen Yu; Richard M. Shiffrin In: http://www.indiana.edu/%7Edll/papers/george_implicit_revised.pdf BASE Show details 8 Hypothesis Testing and Associative Learning in Cross-Situational Word Learning: Are They One and the Same? Richard M. Shiffrin In: http://www.indiana.edu/~dll/papers/yu_cogsci07_cs.pdf BASE Show details 9 Adaptive Constraints and Inference in Cross-Situational Word Learning George Kachergis; Chen Yu; Richard M. Shiffrin In: http://www.indiana.edu/%7Edll/papers/george_adaptive_revised.pdf BASE Show details 10 Frequency and Contextual Diversity Effects in Cross-Situational Word Learning George Kachergis; Chen Yu; Richard M. Shiffrin In: http://www.indiana.edu/~dll/papers/kachergis_cont_div_freq09.pdf BASE Show details 11 Hypothesis Testing and Associative Learning in Cross-Situational Word Learning: Are They One and the Same? Richard M. Shiffrin In: http://csjarchive.cogsci.rpi.edu/proceedings/2007/docs/p737.pdf Abstract: Recent studies (e.g. Yu & Smith, in press; Smith & Yu, submitted) show that both adults and young children possess powerful statistical computation capabilities-- they can infer the referent of a word from highly ambiguous contexts involving many words and many referents. This paper goes beyond demonstrating empirical behavioral evidence-- we seek to systematically investigate the nature of the underlying learning mechanisms. Toward this goal, we propose and implement a set of computational models based on three mechanisms: (1) hypothesis testing; (2) dumb associative learning; and (3) advanced associative learning. By applying these models to the same materials used in learning studies with adults and children, we first conclude that all the models can fit behavioral data reasonably well. The implication is that these mechanisms – despite their seeming difference--may be fundamentally (or formally) the same. In light of this, we propose a formal unified view of learning principles that is based on the shared ground between them. By doing so, we suggest that the traditional controversy between hypothesis testing and associative learning as two distinct learning machineries may not exist. Keyword: language acquisition; word learning URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.603.6649 http://csjarchive.cogsci.rpi.edu/proceedings/2007/docs/p737.pdf BASE Hide details

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