Frances Egan

The computational theory of mind construes the mind as an information-processor and cognitive capacities as essentially representational capacities. Proponents of the view claim a central role for representational content in computational models of these capacities. In this paper I argue that the standard view of the role of representational content in computational models is mistaken; I argue that representational content is to be understood as a gloss on the computational characterization of a cognitive process.Keywords: Computation; Representational content; Cognitive capacities; Explanation

In the appendix to Psychosemantics, entitled ‘Why There Still has to be a Language of Thought,’ Jerry Fodor offers several arguments for the language of thought thesis. The LOT, as articulated by Fodor, is a thesis about propositional attitudes. It comprises the following two claims: propositional attitudes are relations to meaning-bearing tokens — for example, to believe that P is to bear a certain relation to a token of a symbol which means that P; and the representational tokens in question are quasi-linguistic — in particular, they have the constituent structure appropriate to a language.

Ever since Berkeley discussed the problem at length in his Essay Toward a New Theory of Vision, theorists of vision have attempted to explain why the moon appears larger on the horizon than it does at the zenith. Prevailing opinion has it that the contemporary perceptual psychologists Kaufman and Rock have finally explained the illusion. This paper argues that Kaufman and Rock have not refuted a Berkeleyan account of the illusion, and have over-interpreted their own experimental results. The moon illusion remains unexplained, and a Berkeleyan account is still a contender

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.