5,110 research outputs found
A Connectionist Theory of Phenomenal Experience
When cognitive scientists apply computational theory to the problem of phenomenal consciousness, as
many of them have been doing recently, there are two fundamentally distinct approaches available. Either
consciousness is to be explained in terms of the nature of the representational vehicles the brain deploys; or
it is to be explained in terms of the computational processes defined over these vehicles. We call versions of
these two approaches vehicle and process theories of consciousness, respectively. However, while there may
be space for vehicle theories of consciousness in cognitive science, they are relatively rare. This is because
of the influence exerted, on the one hand, by a large body of research which purports to show that the
explicit representation of information in the brain and conscious experience are dissociable, and on the
other, by the classical computational theory of mind â the theory that takes human cognition to be a species
of symbol manipulation. But two recent developments in cognitive science combine to suggest that a
reappraisal of this situation is in order. First, a number of theorists have recently been highly critical of the
experimental methodologies employed in the dissociation studies â so critical, in fact, itâs no longer
reasonable to assume that the dissociability of conscious experience and explicit representation has been
adequately demonstrated. Second, classicism, as a theory of human cognition, is no longer as dominant in
cognitive science as it once was. It now has a lively competitor in the form of connectionism; and
connectionism, unlike classicism, does have the computational resources to support a robust vehicle theory
of consciousness. In this paper we develop and defend this connectionist vehicle theory of consciousness. It
takes the form of the following simple empirical hypothesis: phenomenal experience consists in the explicit
representation of information in neurally realized PDP networks. This hypothesis leads us to re-assess some
common wisdom about consciousness, but, we will argue, in fruitful and ultimately plausible ways
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Neurons and symbols: a manifesto
We discuss the purpose of neural-symbolic integration including its principles, mechanisms and applications. We outline a cognitive computational model for neural-symbolic integration, position the model in the broader context of multi-agent systems, machine learning and automated reasoning, and list some of the challenges for the area of
neural-symbolic computation to achieve the promise of effective integration of robust learning and expressive reasoning under uncertainty
Empiricism without Magic: Transformational Abstraction in Deep Convolutional Neural Networks
In artificial intelligence, recent research has demonstrated the remarkable potential of Deep Convolutional Neural Networks (DCNNs), which seem to exceed state-of-the-art performance in new domains weekly, especially on the sorts of very difficult perceptual discrimination tasks that skeptics thought would remain beyond the reach of artificial intelligence. However, it has proven difficult to explain why DCNNs perform so well. In philosophy of mind, empiricists have long suggested that complex cognition is based on information derived from sensory experience, often appealing to a faculty of abstraction. Rationalists have frequently complained, however, that empiricists never adequately explained how this faculty of abstraction actually works. In this paper, I tie these two questions together, to the mutual benefit of both disciplines. I argue that the architectural features that distinguish DCNNs from earlier neural networks allow them to implement a form of hierarchical processing that I call âtransformational abstractionâ. Transformational abstraction iteratively converts sensory-based representations of category exemplars into new formats that are increasingly tolerant to ânuisance variationâ in input. Reflecting upon the way that DCNNs leverage a combination of linear and non-linear processing to efficiently accomplish this feat allows us to understand how the brain is capable of bi-directional travel between exemplars and abstractions, addressing longstanding problems in empiricist philosophy of mind. I end by considering the prospects for future research on DCNNs, arguing that rather than simply implementing 80s connectionism with more brute-force computation, transformational abstraction counts as a qualitatively distinct form of processing ripe with philosophical and psychological significance, because it is significantly better suited to depict the generic mechanism responsible for this important kind of psychological processing in the brain
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