How does of initial inaccuracy benefit cross-situational word learning?
London, UK : Cognitive Science Society
InGunzelmann, G.; Howes, A.; Tenbrink, T. (ed.), 39th Annual Meeting of the Cognitive Science Society (CogSci 2017): Proceedings, pp. 3722
The 39th Annual Meeting of the Cognitive Science Society (CogSci 2017) (London, UK, 26-29 July 2017)
Article in monograph or in proceedings
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SW OZ DCC AI
Gunzelmann, G.; Howes, A.; Tenbrink, T. (ed.), 39th Annual Meeting of the Cognitive Science Society (CogSci 2017): Proceedings
SubjectCognitive artificial intelligence; DI-BCB_DCC_Theme 4: Brain Networks and Neuronal Communication
Both children and adults are able to extract several intended word-referent mappings from a series of scenes containing multiple words and objects. Known as cross-situational learning, this ability is thought to be an important means of acquiring language. Proposed models of this ability range from hypothesis-testing accounts to associative accounts, but most formal models assume learners store one or more feasible word-referent mappings per experience, and that the correct mappings emerge through consistent co-occurrence. These theories would all predict that presenting unambiguous evidence for a correct pair would benefit learning, but recent evidence indicates the reverse is true: giving unambiguous evidence for incorrect pairs improves subsequent cross-situational learning (Fitneva and Christiansen, 2015). With some nuances, we replicate these results, and show why future models may need to include an error-driven learning mechanism to explain word learning.
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