Levels of explanation and cognitive architectures* by

Some controversies in cognitive science, such as arguments about whether classical or distributed connectionist architectures best model the human cognitive system, reenact long-standing debates in the philosophy of science. For millennia philosophers have pondered whether mentality can submit to scientific explanation generally and to physical explanation particularly. Recently, positive answers have gained popularity. The question remains, though, as to the analytical level at which mentality is best explained. Is there a level of analysis that is peculiarly appropriate for the explanation of either consciousness or mental contents? Are human consciousness, cognition, and conduct best understood in terms of talk about neurons and networks or schemas and scripts or intentions and inferences? If our best accounts make no appeal to our hopes or beliefs or desires, how do we square those views with our conception of ourselves as rational beings? Moreover, can models of physical processes explain our mental lives? Does mentality require a special level of rational or cognitive explanation or is it best understood in terms of overall brain functioning or neuronal or molecular or even quantum activities--or any of a dozen levels of physical explanation in between? Also, regardless of how they compare with explanations cast at physical levels, what is the status of psychological explanations that appeal fundamentally to mental contents? As a means for beginning to address such questions, proposals about cognitive architecture concern which kind of explanation best characterizes primitive psychological activities. Although, technically, approaches to modeling those activities are unlimited, two 1 strategies have enjoyed most of the attention. The prominence of the classical account and the distributed connectionist (or parallel distributed processing (PDP)) account, notwithstanding, nothing bars the development of additional proposals. Classicism employs rules that apply to symbolic representations to explain cognitive processing..
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