John L. Tienson

Phenomenal intentionality and the brain in a vat

In original contributions, internationally leading authors address themselves to one of the most important questions of contemporary theoretical philosophy. The debate between internalism and externalism has become a focal point of attention both in epistemology and in the philosophy of mind and language. Externalism challenges basic traditional internalist conceptions of the nature of knowledge, justification, thought and language.

A nonclassical framework for cognitive science

David Marr provided a useful framework for theorizing about cognition within classical, AI-style cognitive science, in terms of three levels of description: the levels of (i) cognitive function, (ii) algorithm and (iii) physical implementation. We generalize this framework: (i) cognitive state transitions, (ii) mathematical/functional design and (iii) physical implementation or realization. Specifying the middle, design level to be the theory of dynamical systems yields a nonclassical, alternative framework that suits (but is not committed to) connectionism. We consider how a brains (or a networks) being a dynamical system might be the key both to its realizing various essential features of cognition — productivity, systematicity, structure-sensitive processing, syntax — and also to a non-classical solution of (frame-type) problems plaguing classical cognitive science.

Précis of connectionism and the philosophy of psychology

Abstract Connectionism was explicitly put forward as an alternative to classical cognitive science. The questions arise: how exactly does connectionism differ from classical cognitive science, and how is it potentially better? The classical “rules and representations” conception of cognition is that cognitive transitions are determined by exceptionless rules that apply to the syntactic structure of symbols. Many philosophers have seen connectionism as a basis for denying structured symbols. We, on the other hand, argue that cognition is too rich and flexible to be simulable by the exceptionless representation‐level rules that classicism requires. However, this very richness of cognition requires syntactically structured representations—what philosophers call a language of thought. The natural mathematical characterization of neural networks comes from the theory of dynamical systems. We propose that the mathematics of dynamical systems, not the mathematics of algorithms, holds the key to how cognitive str...

What the differences are: Reply to Hardcastle

Abstract Hardcastle argues that we make distinctions where there are no differences when we speak of (1) levels of description, (2) cognitive forces, and (3) soft laws in psychology. Concerning (1) and (2), the differences just are differences in description. The same state is referred to by three different descriptions, and talk of cognitive forces is appropriate and useful at the cognitive level of description. And concerning (3), if our view of cognition is correct, then the laws of psychology are importantly different in form from those of physics.

Is this any way to be a realist

Abstract Andy Clark argues that the reality and causal efficacy of the folk psychological attitudes do not require in‐the‐head correlates of the that‐clauses by which they are attributed. The facts for which Fodor invokes a language of thought as empirical explanation—systemati‐city, for example—are, Clark argues, an a priori conceptual demand upon propositional attitude ascription, and hence not in need of empirical explanation. However, no such strategy can work. A priori demands imposed by our practices do not eliminate the need for empirical explanation of how these demands are satisfied by particular beings. And given the vast number of potential beliefs, and the interrelationships they exhibit, no one has the slightest idea how they might be explained except by in‐the‐head syntax.