Imagery and meaning,the cognitive science connection

Taking the integrated viewpoints of causal theory of reference, cognitive science and the notion of correspondence principles from physics, this paper addresses the problems of creativity, the nature of visual imagery and the manner in which science progresses.     

Representation and cognitive science

‘Representation’ is a concept which occurs both in cognitive science and philosophy. It has common features in both settings in that it concerns the explanation of behaviour in terms of the way the subject categorizes and systematizes responses to its environment. The prevailing model sees representations as causally structured entities correlated on the one hand with elements in a natural language and on the other with clearly identifiable items in the world. This leads to an analysis of representation and cognition in terms of formal symbols and their relations. But human perception and cognition use multiple informational constraints and deal with unsystematic and messy input in a way best explained by Parallel Distributed Processing models. This undermines the claim that a formal representational theory of mind is ‘the only game in town’. In particular it suggests a radically different model of brain function and its relation to epistemology from that found in current representational theories.     

Non-representationalist cognitive science and realism

Embodied and extended cognition is a relatively new paradigm within cognitive science that challenges the basic tenet of classical cognitive science, viz. cognition consists in building and manipulating internal representations. Some of the pioneers of embodied cognitive science have claimed that this new way of conceptualizing cognition puts pressure on epistemological and ontological realism. In this paper I will argue that such anti-realist conclusions do not follow from the basic assumptions of radical embodied cognitive science. Furthermore I will show that one can develop a form of realism that reflects rather than just accommodates the core principles of non-representationalist embodied cognitive science.     

A Bayesian marriage of science and politics

Conclusion Some might say that there is a sense in which the very consideration of whether science is equated with rationality is obscene. For far too long science has been dealt with by philosophers within the confines of a protectionist, if not an apologist, policy. Putnam has provided a service by exposing some of the weaker links of the scientism mythology. I have tried to bolster that critique by indicating how, along Bayesian lines, value and holistic considerations could be used to help positively transform science.Richard Rudner would have undoubtedly agreed with very little of what I have said — he seldon did. But he would have been there with open-mind and open-heart. Forqua mentor andqua friend he was, indeed, the philosopher par excellence.     

Mind,consciousness, and cognition: Phenomenology vs. cognitive science

I would like to thank Steven Crowell from Rice University (Philosophy Department) for helpful comments and suggestions about my discussion of Husserl and other phenomenologists, and Edouard Philippe, also from Rice University (Electrical Engineering Department), for offering me the opportunity to formulate my views on cognitive science and artificial intelligence.     

Emergence and reduction in dynamical cognitive science

This paper examines the widespread intuition that the dynamical approach to cognitive science is importantly related to emergentism about the mind. The explanatory practices adopted by dynamical cognitive science rule out some conceptions of emergence; covering law explanations require a deducibility relationship between explanans and explanandum, whereas canonical theories of emergence require the absence of such deducibility. A response to this problem - one which would save the intuition that dynamics and emergence are related - is to reconstrue the concept of emergence as a relationship between laws. I call this “nomological emergence” and comment on the extent to which dynamicists would find it acceptable. Alternatively, dynamical cognitive science might be viewed as fitting better with the kind of “functional reductionism” which has recently been developed by authors such as Jaegwon Kim. Which of these two alternatives is preferable remains an open question pending the further development of dynamical cognitive science, particularly in its “non-classical” forms.     

Integrating predictive frameworks and cognitive models of face perception

The idea of a “predictive brain”—that is, the interpretation of internal and external information based on prior expectations—has been elaborated intensely over the past decade. Several domains in cognitive neuroscience have embraced this idea, including studies in perception, motor control, language, and affective, social, and clinical neuroscience. Despite the various studies that have used face stimuli to address questions related to predictive processing, there has been surprisingly little connection between this work and established cognitive models of face recognition. Here we suggest that the predictive framework can serve as an important complement of established cognitive face models. Conversely, the link to cognitive face models has the potential to shed light on issues that remain open in predictive frameworks.     

Neural predictive mechanisms and their role in cognitive incrementalism

Many neuroscientists view prediction as one of the core brain functions, especially on account of its support of fast movements in complex environments. This leads to the natural question whether predictive knowledge forms the cornerstone of our common-sense understanding of the world. However, there is little consensus as to the exact nature of predictive information and processes, or of the neural mechanisms that realize them. This paper compares procedural versus declarative notions of prediction, examines how the brain appears to carry out predictive functions, and discusses to what degree, and at what level, these neural mechanisms support cognitive incrementalism: the notion that high-level cognition stems from sensorimotor behavior.     

Three case studies in the Bayesian analysis of cognitive models

Bayesian statistical inference offers a principled and comprehensive approach for relating psychological models to data. This article presents Bayesian analyses of three influential psychological models: multidimensional scaling models of stimulus representation, the generalized context model of category learning, and a signal detection theory model of decision making. In each case, the model is recast as a probabilistic graphical model and is evaluated in relation to a previously considered data set. In each case, it is shown that Bayesian inference is able to provide answers to important theoretical and empirical questions easily and coherently. The generality of the Bayesian approach and its potential for the understanding of models and data in psychology are discussed.