Online research in philosophy

Modeling cognition by structural analysis of representation leads to systematic difficulties which are not resolvable. We analyse the merits and limits of a representation-based methodology to modeling cognition by treating Jackendoff's Consciousness and the Computational Mind as a good case study. We note the effects this choice of methodology has on the view of consciousness he proposes, as well as a more detailed consideration of the computational mind. The fundamental difficulty we identify is the conflict between the desire for modular processors which map directly onto representations and the need for dynamically interacting control. Our analysis of this approach to modeling cognition is primarily directed at separating merits from problems and inconsistencies by a critique internal to this approach; we also step outside the framework to note the issues it ignores.

Vagueness theorists tend to think that evolutionary theory dissolves the riddle "Which came first, the chicken or the egg?". After all, 'chicken' is vague. The idea is that Charles Darwin demonstrated that the chicken was preceded by borderline chickens and so it is simply indeterminate as to where the pre-chickens end and the chickens begin.

The past three decades have witnessed a remarkable growth of research interest in the mind. This trend has been acclaimed as the ‘cognitive revolution’ in psychology. At the heart of this revolution lies the claim that the mind is a computational system. The purpose of this paper is both to elucidate this claim and to evaluate its implications for cognitive psychology. The nature and scope of cognitive psychology and cognitive science are outlined, the principal assumptions underlying the information processing approach to cognition are summarised and the nature of artificial intelligence and its relationship to cognitive science are explored. The ‘computational metaphor’ of mind is examined and both the theoretical and methodological issues which it raises for cognitive psychology are considered. Finally, the nature and significance of ‘connectionism’—the latest paradigm in cognitive science—are briefly reviewed.

The computational program for theoretical neuroscience initiated by Marr and Poggio (1977) calls for a study of biological information processing on several distinct levels of abstraction. At each of these levels — computational (defining the problems and considering possible solutions), algorithmic (specifying the sequence of operations leading to a solution) and implementational — significant progress has been made in the understanding of cognition. In the past three decades, computational principles have been discovered that are common to a wide range of functions in perception (vision, hearing, olfaction) and action (motor control). More recently, these principles have been applied to the analysis of cognitive tasks that require dealing with structured information, such as visual scene understanding and analogical reasoning. Insofar as language relies on cognition-general principles and mechanisms, it should be possible to capitalize on the recent advances in the computational study of cognition by extending its methods to linguistics.

influence. One of the principal characteristics that distinguishes Cognitive Science from more traditional studies of cognition within Psychology, is the extent to which it has been influenced by both the ideas and the techniques of computing. It may come as a surprise to the outsider, then, to discover that there is no unanimity within the discipline on either (a) the nature (and in some cases the desireabilty) of the influence and (b) what computing is –- or at least on its.

An asymmetry between the demands at the computational and algorithmic levels of description furnishes the illusion that the abstract profile at the computational level can be multiply realized, and that something is actually being shared at the algorithmic one. A disembodied rendering of the situation lays the stress upon the different ways in which an algorithm can be implemented. However, from an embodied approach, things look rather different. The relevant pairing, I shall argue, is not between implementation and algorithm, but rather between algorithm and computation. The autonomy of psychology is a result of the failure to appreciate this point.

In this comment on Joshua Greene's essay, The Secret Joke of Kant's Soul, I argue that a notable weakness of Greene's approach to moral psychology is its neglect of computational theory. A central problem moral cognition must solve is to recognize (i.e., compute representations of) the deontic status of human acts and omissions. How do people actually do this? What is the theory which explains their practice?

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.

This book is a definitive reference source for the growing, increasingly more important, and interdisciplinary field of computational cognitive modeling, that is, computational psychology. It combines breadth of coverage with definitive statements by leading scientists in this field. Research in computational cognitive modeling explores the essence of cognition through developing detailed, process-based understanding by specifying computational mechanisms, structures, and processes. Computational models provide both conceptual clarity and precision at the same time. This book substantiates this approach through overviews and many examples.