Authors
Aaron Sloman
University of Birmingham
Abstract
he design-based approach is a methodology for investigating mechanisms capable of generating mental phenomena, whether introspectively or externally observed, and whether they occur in humans, other animals or robots. The study of designs satisfying requirements for autonomous agency can provide new deep theoretical insights at the information processing level of description of mental mechanisms. Designs for working systems (whether on paper or implemented on computers) can systematically explicate old explanatory concepts and generate new concepts that allow new and richer interpretations of human phenomena. To illustrate this, some aspects of human grief are analysed in terms of a particular information processing architecture being explored in our research group. We do not claim that this architecture is part of the causal structure of the human mind; rather, it represents an early stage in the iterative search for a deeper and more general architecture, capable of explaining more phenomena. However even the current early design provides an interpretative ground for some familiar phenomena, including characteristic features of certain emotional episodes, particularly the phenomenon of perturbance (a partial or total loss of control of attention). The paper attempts to expound and illustrate the design-based approach to cognitive science and philosophy, to demonstrate the potential effectiveness of the approach in generating interpretative possibilities, and to provide first steps towards an information processing account of `perturbant', emotional episodes.
Keywords Architecture  Emotion  Grief  Mind  Psychology  Science
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DOI 10.1353/ppp.1996.0022
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Virtual Machines and Consciousness.Aaron Sloman & Ronald L. Chrisley - 2003 - Journal of Consciousness Studies 10 (4-5):133-172.
Is Metabolism Necessary?M. A. Boden - 1999 - British Journal for the Philosophy of Science 50 (2):231-248.
An Alternative to Working on Machine Consciousness.Aaron Sloman - 2010 - International Journal of Machine Consciousness 2 (1):1-18.

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