Experimental research during the last few decades has provided evidence that language is embedded in a mosaic of cognitive functions. An account of how language interfaces with memory, perception, action and control is no longer beyond the scope of linguistics, and can now be seen as part of an explanation of linguistic structure itself. However, although our view of language has changed, linguistic methodology is lagging behind. This chapter is a sustained argument for a diversification of the kinds of evidence applicable to linguistic questions at di↵erent levels of theory, and a defense of the role of linguistics in experimental cognitive science. 1.1 Linguistic methodology and cognitive science At least two conceptual issues are raised by current interactions between linguistics and cognitive science. One is whether the structures and rules described by linguists are cognitively real. There exist several opinions in this regard, that occupy di↵erent positions on the mentalism/anti-mentalism spectrum. At one extreme is cognitive linguistics [ Croft and Cruse, 2004 ] , endorsing both theoretical and methodological mentalism. The former is the idea that linguistic structures are related formally and causally to other mental entities. The latter calls for a revision of traditional linguistic methodology, and emphasizes the role of cognitive data in linguistics. At the opposite side of the spectrum lies formal semantics which, partly inspired by Frege’s anti-psychologistic stance on meaning and thought [ Frege, 1980; Lewis, 1970; Burge, 2005 ] , rejects both versions of mentalism. Somewhere between the two poles is Chomsky’s [ Chomsky, 1965 ] theoretical mentalism, which sees linguistic rules as ultimately residing in the brain of speakers. However, his commitment to the cognitive reality of grammar does not imply a revision of linguistic methodology, which is maintained in its traditional form based on native speakers’ intuitions and the competence/performance distinction. The second problem, in part dependent on the first, is whether experimental data on language acquisition, comprehension and production have any bearing on linguistic theory. On this point too, there is no consensus among linguists. The division between competence and performance has often been used to secure linguistics from experimental evidence of various sorts [ Bunge, 1984 ] , while

Beginning Linguistics students are sometimes treated to an array of mock “theories” about the evolution of language, including the “Bow-wow” theory, the “Dingdong” theory and others with equally silly and dismissive names. The 1886 ban on the subject (along with proposals for a universal language) by the Societe Linguistique de Paris is well known, and also sometimes thrown up as a proscription that should be reimposed. Research into the evolution of language never really died, though its serious contributors, such as C.F. Hockett [1960] and Philip Lieberman [1984], were tiny in number. In the past twenty years, the resurrection of the subject has accelerated dramatically. The resurgence can be attributed to a general increase in multidisciplinary research, and to impressive empirical advances in relevant fields such as genetics, psychology of language, ethology (especially primatology), computer modelling, linguistics (especially language typology and some formal modelling) and neuroscience. Linguistics has traditionally been isolated from evolutionary considerations. Saussure’s [1916] emphasis on the primacy of synchronic descriptions coloured all of mainstream 20 century Linguistics. The core of generative grammar is synchronic work. Moreover, the emphasis in generative theory on the discovery of abstract formal principles governing the shape a language can take tends to isolate the study of language from neighbouring disciplines. The prevailing assumption within this dominant paradigm has been that the principles to be discovered are peculiar to language alone [Chomsky, 1965; 1975; 1981]. If regularities are observed that can be accounted for in terms of more general human behaviour, or even animal behaviour, such as memory limitations, limitations on the physiology of the output device (vocal or manual), or constraints on processing complexity, these have tended to be sidelined as not within the domain of Linguistics proper, which is taken to be whatever is special to language alone. There is more than a whiff of Platonism in much 20 century theorizing about language. Of course, as Linguistics is a large field, there have been dissenting voices (e.g. [Bybee, 1985; 1994; Givon, 1979; 1990]), emphasizing the integration of the study of language structure with the study of human and animal behaviour generally, and taking a

1 Positing representations In the analysis of natural language phenomena, linguistic theories typically have recourse to representations of one form or another. Different types of representation are often posited as a means of generalising over aspects of form or interpretation as displayed in natural language constructions, and these are frequently invested with considerable theoretical significance. There are proposed representations of structure at all levels of linguistic systems: sounds, words, sentence strings, as well as representations of the meanings of words, sentences in the abstract and uttered sentences, and even representations of other people’s intentions. Such a representationalist stance was firmly set in place by Chomsky (1965) as part of, indeed the central core of, cognitive science, with language defined as a system of principles for correlating phonological representations (on some abstraction from phonetics) with some representation of interpretation (on some abstraction from denotational contents), via mappings from a central syntactic system. In such an approach, more than one level of representation may be posited as interacting in different ways with other types of representation, for example deep structure and surface structure levels of syntax of Chomsky (1965) were taken to interact in different ways with other types of representation, in particular semantics and phonology. Chomsky’s move towards the explicit representation of linguistic properties as part of human cognition came to be assumed almost universally within theoretical linguistic frameworks, whether formally characterised or This chapter in part reports work to which many people have contributed. We thank in particular, Eleni Gregoromichelaki, Wilfried Meyer-Viol, Matthew Purver for essential input to these developments. We also thank Andrew Gargett, Stergios Chatzikyriakidis, Peter Sutton, Graham White and many others for comments both over the years and in the preparation of this chapter. Research for this paper includes support from the Leverhulme Trust to the first author (XXX), and from the ESRC to the second author (YYY).

Computational learning theory explores the limits of learnability. Studying language acquisition from this perspective involves identifying classes of languages that are learnable from the available data, within the limits of time and computational resources available to the learner. Different models of learning can yield radically different learnability results, where these depend on the assumptions of the model about the nature of the learning process, and the data, time, and resources that learners have access to. To the extent that such assumptions accurately reflect human language learning, a model that invokes them can offer important insights into the formal properties of natural languages, and the way in which their representations might be efficiently acquired. In this chapter we consider several computational learning models that have been applied to the language learning task. Some of these have yielded results that suggest that the class of natural languages cannot be efficiently learned from the primary linguistic data (PLD) available to children, through

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