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On the subsymbolic nature of a PDP architecture that uses a nonmonotonic activation function

Michael R. W. Dawson & C. Darren Piercey

Minds and Machines 11 (2):197-218 (2001)

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Interpreting the internal structure of a connectionist model of the balance scale task.Michael R. W. Dawson & Corinne Zimmerman - 2003 - Brain and Mind 4 (2):129-149.details One new tradition that has emerged from early research on autonomous robots is embodied cognitive science. This paper describes the relationship between embodied cognitive science and a related tradition, synthetic psychology. It is argued that while both are synthetic, embodied cognitive science is antirepresentational while synthetic psychology still appeals to representations. It is further argued that modern connectionism offers a medium for conducting synthetic psychology, provided that researchers analyze the internal representations that their networks develop. The paper then provides a (...) Download Export citation Bookmark 3 citations
On the biological plausibility of grandmother cells: Implications for neural network theories in psychology and neuroscience.Jeffrey S. Bowers - 2009 - Psychological Review 116 (1):220-251.details A fundamental claim associated with parallel distributed processing theories of cognition is that knowledge is coded in a distributed manner in mind and brain. This approach rejects the claim that knowledge is coded in a localist fashion, with words, objects, and simple concepts, that is, coded with their own dedicated representations. One of the putative advantages of this approach is that the theories are biologically plausible. Indeed, advocates of the PDP approach often highlight the close parallels between distributed representations learned (...) Download Export citation Bookmark 26 citations
What the <0.70, 1.17, 0.99, 1.07> is a Symbol?Istvan S. N. Berkeley - 2008 - Minds and Machines 18 (1):93-105.details The notion of a ‘symbol’ plays an important role in the disciplines of Philosophy, Psychology, Computer Science, and Cognitive Science. However, there is comparatively little agreement on how this notion is to be understood, either between disciplines, or even within particular disciplines. This paper does not attempt to defend some putatively ‘correct’ version of the concept of a ‘symbol.’ Rather, some terminological conventions are suggested, some constraints are proposed and a taxonomy of the kinds of issue that give rise to (...) Download Export citation Bookmark 1 citation
Moving the goal posts: A reply to Dawson and Piercey. [REVIEW]Istvan S. N. Berkeley - 2006 - Minds and Machines 16 (4):471-478.details Berkeley [Minds Machines 10 (2000) 1] described a methodology that showed the subsymbolic nature of an artificial neural network system that had been trained on a logic problem, originally described by Bechtel and Abrahamsen [Connectionism and the mind. Blackwells, Cambridge, MA, 1991]. It was also claimed in the conclusion of this paper that the evidence was suggestive that the network might, in fact, count as a symbolic system. Dawson and Piercey [Minds Machines 11 (2001) 197] took issue with this latter (...) Download Export citation Bookmark 3 citations
Content and Its vehicles in connectionist systems.Nicholas Shea - 2007 - Mind and Language 22 (3):246–269.details This paper advocates explicitness about the type of entity to be considered as content- bearing in connectionist systems; it makes a positive proposal about how vehicles of content should be individuated; and it deploys that proposal to argue in favour of representation in connectionist systems. The proposal is that the vehicles of content in some connectionist systems are clusters in the state space of a hidden layer. Attributing content to such vehicles is required to vindicate the standard explanation for some (...) Download Export citation Bookmark 26 citations
How Do Artificial Neural Networks Classify Musical Triads? A Case Study in Eluding Bonini's Paradox.Arturo Perez, Helen L. Ma, Stephanie Zawaduk & Michael R. W. Dawson - 2023 - Cognitive Science 47 (1):e13233.details How might artificial neural networks (ANNs) inform cognitive science? Often cognitive scientists use ANNs but do not examine their internal structures. In this paper, we use ANNs to explore how cognition might represent musical properties. We train ANNs to classify musical chords, and we interpret network structure to determine what representations ANNs discover and use. We find connection weights between input units and hidden units can be described using Fourier phase spaces, a representation studied in musical set theory. We find (...) Download Export citation Bookmark

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