Generative grammar is a research tradition in linguistics that aims to explain the cognitive basis of language by formulating and testing explicit models of humans' subconscious grammatical knowledge. Generative linguists, or generativists (), tend to share certain working assumptions such as the competence–performance distinction and the notion that some domain-specific aspects of grammar are partly innate in humans. These assumptions are often rejected in non-generative approaches such as usage-based models of language. Generative linguistics includes work in core areas such as syntax, semantics, phonology, psycholinguistics, and language acquisition, with additional extensions to topics including biolinguistics and music cognition. Generative grammar began in the late 1950s with the work of Noam Chomsky, having roots in earlier approaches such as structural linguistics. The earliest version of Chomsky's model was called transformational grammar, with subsequent iterations known as government and binding theory and the minimalist program. Other present-day generative models include optimality theory, categorial grammar, and tree-adjoining grammar.
Principles Generative grammar is an umbrella term for a variety of approaches to linguistics. What unites these approaches is the goal of uncovering the cognitive basis of language by formulating and testing explicit models of humans' subconscious grammatical knowledge.
Cognitive science Generative grammar studies language as part of cognitive science. Thus, research in the generative tradition involves formulating and testing hypotheses about the mental processes that allow humans to use language. Like other approaches in linguistics, generative grammar engages in linguistic description rather than linguistic prescription.
Explicitness and generality Generative grammar proposes models of language consisting of explicit rule systems, which make testable falsifiable predictions. This is different from traditional grammar where grammatical patterns are often described more loosely. These models are intended to be parsimonious, capturing generalizations in the data with as few rules as possible. As a result, empirical research in generative linguistics often seeks to identify commonalities between phenomena, and theoretical research seeks to provide them with unified explanations. For example, Paul Postal observed that English imperative tag questions obey the same restrictions that second person future declarative tags do, and proposed that the two constructions are derived from the same underlying structure. This hypothesis was able to explain the restrictions on tags using a single rule. Particular theories within generative grammar have been expressed using a variety of formal systems, many of which are modifications or extensions of context free grammars.
Competence versus performance Generative grammar generally distinguishes linguistic competence and linguistic performance. Competence is the collection of subconscious rules that one knows when one knows a language; performance is the system which puts these rules to use. This distinction is related to the broader notion of Marr's levels used in other cognitive sciences, with competence corresponding to Marr's computational level. For example, generative theories generally provide competence-based explanations for why English speakers would judge the sentence in (1) as odd. In these explanations, the sentence would be ungrammatical because the rules of English only generate sentences where demonstratives agree with the grammatical number of their associated noun.
(1) *That cats is eating the mouse. By contrast, generative theories generally provide performance-based explanations for the oddness of center embedding sentences like one in (2). According to such explanations, the grammar of English could in principle generate such sentences, but doing so in practice is so taxing on working memory that the sentence ends up being unparsable.
(2) *The cat that the dog that the man fed chased meowed. In general, performance-based explanations deliver a simpler theory of grammar at the cost of additional assumptions about memory and parsing. As a result, the choice between a competence-based explanation and a performance-based explanation for a given phenomenon is not always obvious and can require investigating whether the additional assumptions are supported by independent evidence. For example, while many generative models of syntax explain island effects by positing constraints within the grammar, it has also been argued that some or all of these constraints are in fact the result of limitations on performance. Non-generative approaches often do not posit any distinction between competence and performance. For instance, usage-based models of language assume that grammatical patterns arise as the result of usage.
… excerpt ends here. Continue reading the full article.
