Linguistic Nativism and the Poverty of the Stimulus

Linguistic Nativism and the Poverty of the Stimulus
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Probabilistic Learning Theory for Language. Computational Complexity and Efficient Learning.

Poverty of the stimulus | Psychology Wiki | FANDOM powered by Wikia

Positive Results in Efficient Learning. Grammar Induction through Implemented Machine. Parameters in Linguistic Theory and Probabilistic. A Brief Look at Some Biological.

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As Bates and Roe argue in their survey of the childhood aphasia literature, outcomes differ wildly from case to case, and the reported studies exhibit numerous methodological confounds e. On the other hand, the developmental double dissociation between specific language impairment SLI and WS, is, on the face of it, much more convincing. Uniformity Some researchers e. The structure of the argument may be summarized as follows:. If Nevins et al. Child Dev.

Paradigm Case. These donations can be offset against your federal and sometimes your state tax return U. Many companies also offer a gift matching program, such that they will match any gift you make to a non-profit organization. Normally this entails your contacting your human resources department and sending us a form that the EMU Foundation fills in and returns to your employer. Please take a moment to check if your company operates such a program. Science; Lang. The book is intended for a general linguisticsaudience, but the reader needs some familiarity with basic concepts in formallinguistics, at least an elementary understanding of computational linguistics,and enough statistical, programming or mathematical knowledge not to shy awayfrom algorithms.

The book will most benefit linguists working in formal grammar,computational linguistics and linguistic theory.

The main aim is to replace the view that humans have an innate bias towardslearning language that is specific to language with the view that an innate biastowards language acquisition depends on abilities that are used in other domainsof learning. The first view is characterised as the argument for a strong bias,or linguistic nativism, while the second view is characterised as a weak bias ordomain-general view. The principle line of argument is that computational,statistical and machine-learning methods demonstrate superior success inmodeling, describing and explaining language acquisition, especially whencompared to studies from formal linguistic models based on strong bias arguments.

Theauthors' main focus is on a viable computationally-explicit model of languageacquisition. They pointout that it is fairly uncontroversial, first, that humans alone acquirelanguage, and, second, that the environment plays a significant role indetermining the language and the level of acquisition. What they intend toestablish in the book, however, is that any innate cognitive faculties employedin the acquisition of language are not specific to language, as suggested byChomsky and the Universal Grammar UG framework, but are general cognitiveabilities that are employed in other learning tasks.

The APS is considered central to the nativist position because it provides anexplanation for Chomsky's core assumptions for UG: 1 grammar is rich andabstract; 2 data-driven learning is inadequate; 3 children are exposed toqualitatively and quantitatively degenerate linguistic data; and 4 theacquired grammar for a given language is uniform despite variations inintelligence.

These assumptions are dealt with in chapter 2 and throughout thebook and replaced with assumptions from a machine-learning, 'weak bias'perspective, although the reader is referred to other sources to counterassumption 3.

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Chapter 3 examines the extent to which the stimulus really is impoverished. MacWhinney, , they focus on the role of negative evidence in thelearning process because a small amount of negative evidence can make asignificant difference to language acquisition.

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Indirect negative evidence, suchas the non-existence of hypothesised structures, can also significantly alterthe learning task. Gold's highly-influential study argues that because learning cannotsucceed within the cognitive and developmental limits imposed, then childrenmust have prior knowledge of the language system.

Clark and Lappin are keen to point out that demonstrating the tractability ofthe learning problem through viable formal computational descriptions does notequate to modelling the actual neurological or psychological processes that maybe employed in language acquisition. They do not allow for the unnatural conditionof the malevolent presentation of data -- intentionally withholding crucialevidence samples and offering evidence in a sequence detrimental to learning.

Yarden Kedar

Jan 7, This unique contribution to the ongoing discussion of language acquisition considers the Argument from the Poverty of the Stimulus in. “This book is not only very pertinent, but also succeeds in eschewing most of the polemical excess that tends to engulf us all in this field It's not an easy book.. but .

They reject Gold's lack of time limitation placed on learning. They reject theimpossibility of learners querying the grammaticality of a string. They rejectthe view that learning is through positive evidence only. Rather, theyinsist that while the learnable class of a language is limited, the learner isfree to form unlimited hypotheses.

Poverty of the stimulus

While it may be true thatthe primitive statistical models critiqued by Chomsky are incapable of producinga satisfactory distinction between grammatical and ungrammatical strings, thisdoes not prove that all statistical methods are inferior to UG descriptions. Central to a plausible probabilisticapproach to modelling language is the distinction between simple measures offrequency and distributional measures. Replacing Gold's paradigm with three key assumptions 1. This probabilisticalgorithm depends on the observed distribution of segmented strings and on theprinciple of converging on a probabilistic grammar.

Distributional measuresensure that the probability of any utterance will never be zero, allowing forerrors in the presented language data. This model predicts over-generalisationand under-generalisation in the learner's output because of the unlimitedhypothesis space. The addition of word class distributional data also ensuresgreater reliability of judging the probability of a string being grammatical.

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A major aim of this book is to provide a formal account of the language learningpuzzle that will make the acquisition problem tractable. They advocate algorithms that cansimulate learning under natural conditions. They assume that the input data isnot homogenous -- some language items are 'more learnable' than others, and morecomplex learning tasks can be deferred according to a Fixed ParameterTractability algorithm.

AcqOfLang2: Poverty of the Stimulus - Nativists & Linguistic Nativists

When UGtheories use the strong bias position as the only argument to deal withcomplexity, they have not solved the problem posed by a seemingly intractablelearning task. If we are to reject the presumption of the strong bias in linguistic nativism,we need to be confident that its replacement can produce reliable results. Theproposed algorithms in chapter 8 start to provide those results. The process ofhypothesis generation in Gold's IIL is described as being close to random, andconsequently "hopelessly inefficient" p.

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Various replacements that havebeen tested include Non- Deterministic Finite State Automata algorithms whichhave proved effective in restricted language learning contexts. Simpledistributional and statistical learning algorithms offer promising results, butmust be adapted to also simulate grammar deduction. Objections to distributional models are countered in chapter 9 by presenting theresults of real algorithms working on real data.

Innateness and Language

Typically the algorithmperforms a parsing, tagging, segmenting or similar task on a corpus, and theresults are measured against a previously-annotated version of the corpus -- a'gold standard. Learning algorithms can be divided into "supervised" --requiring the corpus to carry some form of information such as part of speechtagging -- and "unsupervised" -- working on a 'bare' corpus of language. More surprising, perhaps, are thehigh success rates of unsupervised learning algorithms in word segmentation, inlearning word classes and morphology, and in parsing.

Chapter 10 examines 'Principles and Parameters' and other UG models. Even more worrying for UG theories is thenear-indifference to questions of acquisition in Minimalist Program research,the latest version of UG. They argue for a weak innate bias towards learninglanguage based on general-domain learning abilities.

They point out that the UGframework has produced few concrete experiments or cases that "produce anexplicit, computationally viable, and psychologically credible account oflanguage acquisition" p. They have attempted to introduce explicit, formalcomputational models of learning that have produced a credible account oflearning.