A standard view of reference holds that a speaker's use of a name refers to a certain thing in virtue of the speaker's associating a condition with that use that singles the referent out. This view has been criticized by Saul Kripke as empirically inadequate. Recently, however, it has been argued that a version of the standard view, a /response-based theory of reference/, survives the charge of empirical inadequacy by allowing that associated conditions may be largely or even entirely implicit. (...) This paper argues that response-based theories of reference are prey to a variant of the empirical inadequacy objection, because they are ill-suited to accommodate the successful use of proper names by pre-school children. Further, I argue that there is reason to believe that normal adults are, by and large, no different from children with respect to how the referents of their names are determined. I conclude that speakers typically refer /positionally/: the referent of a use of a proper name is typically determined by aspects of the speaker's position, rather than by associated conditions present, however implicitly, in her psychology. (shrink)

A fundamental goal of linguistic theory is to explain how natural languages are acquired. This paper describes some recent findings on how learners acquire syntactic knowledge for which there is little, if any, decisive evidence from the environment. The first section presents several general observations about language acquisition that linguistic theory has tried to explain and discusses the thesis that certain linguistic properties are innate because they appear universally and in the absence of corresponding experience. A third diagnostic for innateness, (...) early emergence, is the focus of the second section of the paper, in which linguistic theory is tested against recent experimental evidence on children's acquisition of syntax. (shrink)

There is a school of thought which links connectionist models of cognition to eliminativism-the thesis that the constructs of commonsense psychology do not exist. This way of construing the impact of connectionist modelling is, I argue, deeply mistaken and depends crucially on a shallow analysis of the notion of explanation. I argue that good, higher level descriptions may group together physically heterogenous mechanisms, and that the constructs of folk psychology may fulfil such a grouping function even if they fail to (...) pick out discrete states of individual processing. More speculatively, the paper goes on to suggest that the virtues which recommend such constructs from a theorist’s third person perspective may nonetheless have close analogues in individual processing. Various difficulties for what I term a pure distributed connectionism may be solved by systems which utilise both distributed and classical symbolic representations-the latter exhibiting the discreteness and semantic interpretability which the eliminativist requires to vindicate common-sense psychology. If human beings turned out to be such mixed systems, then the eliminativist claim would be doubly misguided. It would be false as a conditional since even if pure distributed connectionism were a complete and accurate formal model of individual processing, it would not follow that the other, higher level constructs were not accurate and essential tools for a different kind of explanation. And it would involve a false antecedent since in a mixed system the symbolic descriptions may indeed be incarnate in the system’s own individual processing. The paper is in four main sections. Section 1 attempts to systematise a certain received picture of the relative status and accuracy of various levels of description of a connectionist system. Section 2 then introduces a general model of explanations which aim to group systems into equivalence classes defined for various purposes. Each grouping requires a special vocabulary and the constructs of any given vocabulary are legitimate just insofar as the grouping is interesting and useful. Section 3 goes on to show that, relative to such a model of explanation, the constructs of both symbolic AI and commonsense psychology may have a legitimate role to play in giving psychological explanations. This role is not just that of a useful approximation. The paper ends with a speculative section in which the argument for the theoretical usefulness of such symbolic constructs is extended, in a very natural way, to the domain of individual processing. Here the cognizer, in the process of regulating, debugging and understanding her own representations, creates symbols to stand for sets of distributed activity patterns. The section points out some difficulties for a pure distributed approach which may be eased by the addition of such symbolic constructs and relates the speculations to the current debate over the ’correct’architecture of cognition. If the speculations are on target, this whole debate turns out to be fundamentally illposed. (shrink)

Naive physics beliefs can be systematically mistaken. They provide a useful test-bed because they are common, and also because their existence must rely on some adaptive advantage, within a given context. In the second part of the commentary we also ask questions about when a whole family of misbeliefs should be considered together as a single phenomenon.

We focus on Karmiloff-Smith's Representational redescription model, arguing that it poses some problems concerning the architecture of a redescribing system. To discuss the topic, we consider the implicit/explicit dichotomy and the relations between natur al language and the language of thought. We argue that the model regards how knowledge is employed rather than how it is represented in the system.

This paper compares two theories and their two corresponding computational models of human moral judgment. In order to better address psychological realism and generality of theories of moral judgment, more detailed and more psychologically nuanced models are needed. In particular, a motivationally based theory of moral judgment is developed in this paper that provides a more accurate account of human moral judgment than an existing emotion-reason conflict theory. Simulations based on the theory capture and explain a range of relevant human (...) data. They account not only for the original data that were used to support the emotion–reason conflict theory, but also for a wider range of data and phenomena. (shrink)

What is creativity? One new idea may be creative, whereas another is merely new: What's the difference? And how is creativity possible? These questions about human creativity can be answered, at least in outline, using computational concepts. There are two broad types of creativity, improbabilist and impossibilist. Improbabilist creativity involves novel combinations of familiar ideas. A deeper type involves METCS: the mapping, exploration, and transformation of conceptual spaces. It is impossibilist, in that ideas may be generated which – with respect (...) to the particular conceptual space concerned – could not have been generated before. The more clearly conceptual spaces can be defined, the better we can identify creative ideas. Defining conceptual spaces is done by musicologists, literary critics, and historians of art and science. Humanist studies, rich in intuitive subtleties, can be complemented by the comparative rigour of a computational approach. Computational modelling can help to define a space, and to show how it may be mapped, explored, and transformed. Impossibilist creativity can be thought of in “classical” Al terms, whereas connectionism illuminates improbabilist creativity. Most Al models of creativity can only explore spaces, not transform them, because they have no self-reflexive maps enabling them to change their own rules. A few, however, can do so. A scientific understanding of creativity does not destroy our wonder at it, nor does it make creative ideas predictable. Demystification does not imply dehumanization. (shrink)