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  1. Language learning in infancy: Does the empirical evidence support a domain specific language acquisition device?Christina Behme & Helene Deacon - 2008 - Philosophical Psychology 21 (5):641 – 671.
    Poverty of the Stimulus Arguments have convinced many linguists and philosophers of language that a domain specific language acquisition device (LAD) is necessary to account for language learning. Here we review empirical evidence that casts doubt on the necessity of this domain specific device. We suggest that more attention needs to be paid to the early stages of language acquisition. Many seemingly innate language-related abilities have to be learned over the course of several months. Further, the language input contains rich (...)
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  • Innateness, canalization, and 'biologicizing the mind'.Paul E. Griffiths & Edouard Machery - 2008 - Philosophical Psychology 21 (3):397 – 414.
    This article examines and rejects the claim that 'innateness is canalization'. Waddington's concept of canalization is distinguished from the narrower concept of environmental canalization with which it is often confused. Evidence is presented that the concept of environmental canalization is not an accurate analysis of the existing concept of innateness. The strategy of 'biologicizing the mind' by treating psychological or behavioral traits as if they were environmentally canalized physiological traits is criticized using data from developmental psychobiology. It is concluded that (...)
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  • Indirect Evidence and the Poverty of the Stimulus: The Case of Anaphoric One.Stephani Foraker, Terry Regier, Naveen Khetarpal, Amy Perfors & Joshua Tenenbaum - 2009 - Cognitive Science 33 (2):287-300.
    It is widely held that children’s linguistic input underdetermines the correct grammar, and that language learning must therefore be guided by innate linguistic constraints. Here, we show that a Bayesian model can learn a standard poverty‐of‐stimulus example, anaphoric one, from realistic input by relying on indirect evidence, without a linguistic constraint assumed to be necessary. Our demonstration does, however, assume other linguistic knowledge; thus, we reduce the problem of learning anaphoric one to that of learning this other knowledge. We discuss (...)
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  • Ability, Breadth, and Parsimony in Computational Models of Higher‐Order Cognition.Nicholas L. Cassimatis, Paul Bello & Pat Langley - 2008 - Cognitive Science 32 (8):1304-1322.
    Computational models will play an important role in our understanding of human higher‐order cognition. How can a model's contribution to this goal be evaluated? This article argues that three important aspects of a model of higher‐order cognition to evaluate are (a) its ability to reason, solve problems, converse, and learn as well as people do; (b) the breadth of situations in which it can do so; and (c) the parsimony of the mechanisms it posits. This article argues that fits of (...)
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