Robert J. Matthews

Classicists and connectionists alike claim to be able to explain systematicity. The proposed classicist explanation, I argue, is little more than a promissory note, one that classicists have no idea how to redeem. Smolensky's (1995) proposed connectionist explanation fares little better: it is not vulnerable to recent classicist objections, but it nonetheless fails, particularly if one requires, as some classicists do, that explanations of systematicity take the form of a‘functional analysis’. Nonetheless, there are, I argue, reasons for cautious optimism about the prospects of a connectionist explanation.

The Adaptive Character of Thought John R. Anderson, Hillside, NJ, Lawrence Erlbaum, Associates Inc., 1990The Psychology of Today's Woman: new psychoanalytic visions Toni Bernay & Dorothy W. Canton (Eds), Harvard University Press $12.95Leamability and Cognition: the acquisition of argument structure Steven Pinker, Cambridge, MA, The MIT Press, 1989 xiv + 410ppPropositional Attitudes, an essay on thoughts and how we ascribe them Mark Richard, Cambridge, Cambridge University Press, 1990 275 pp., 829.95Science and its Fabrication Alan Chalmers, Open University Press, Milton Keynes 1990, 142pp ISBN 0–335–09318–3 ISBN 0–335–09317–5 (paperback)

This paper defends the commonsense conception of linguistic competence according to which linguistic competence involves propositional knowledge of language. More specifically, the paper defends three propositions challenged by Devitt in his Ignorance af Language. First, Chomskian linguists were right to embrace this commonsense conception of linguistic cornpetence. Second, the grammars that these linguists propose make a substantive claim about the computational processes that are presumed to constitute a speaker’s linguistic competence. Third, Chomskian linguistics is indeed a subfield of psychology, in the business of characterizing the linguistic competence of speakers.

In the late 1970s and early 1980s a number of philosophers, notably Churchland, Field, Stalnaker, Dennett, and Davidson, began to argue that propositional attitude predicates (such as believes that it’s sunny outside) are a species of measure predicate, analogous in important ways to numerical predicates by which we attribute physical magnitudes (such as mass, length, and temperature). Other philosophers, including myself, have subsequently developed the idea in greater detail. In this paper I sketch the general outlines of measurement‐theoretic accounts of propositional attitudes, explaining in the briefest terms the basic idea of such accounts, why some have thought such accounts plausible, how these accounts might go, what their implications might be both for our conception of propositional attitudes and for their role in cognitive scientific theorizing, and where the potential problems with such accounts might lie.

Many believe that the grammatical sentences of a natural language are a recursive set. In this paper I argue that the commonly adduced grounds for this belief are inconclusive, if not simply unsound. Neither the native speaker's ability to classify sentences nor his ability to comprehend them requires it. Nor is there at present any reason to think that decidability has any bearing on first-language acquisition. I conclude that there are at present no compelling theoretical grounds for requiring that transformational grammars enumerate only recursive sets. Hence, the fact that proposed transformational grammars do not satisfy this requirement does not, as some have claimed, represent a shortcoming in current theory.

The “top-down” and “bottom-up” approaches have been thought to exhaust the possibilities for doing cognitive neuroscience. We argue that neither approach is likely to succeed in providing a theory that enables us to understand how cognition is achieved in biological creatures like ourselves. We consider a promising third way of doing cognitive neuroscience, what might be called the “neural dynamic systems” approach, that construes cognitive neuroscience as an autonomous explanatory endeavor, aiming to characterize in its own terms the states and processes responsible for brain-based cognition. We sketch the basic motivation for the approach, describe a particular version of the approach, so-called ‘Dynamic Causal Modeling’ (DCM), and consider a concrete example of DCM. This third way, we argue, has the potential to avoid the problems that afflict the other two approaches.