Online research in philosophy

In their critique of connectionist models Fodor and Pylyshyn (1988) dismiss such models as not being cognitive or psychological. Evaluating Fodor and Pylyshyn's critique requires examining what is required in characterizating models as 'cognitive'. The present discussion examines the various senses of this term. It argues the answer to the title question seems to vary with these different senses. Indeed, by one sense of the term, neither representa-tionalism nor connectionism is cognitive. General ramifications of such an appraisal are discussed and alternative avenues for cognitive research are suggested.

In this paper I critically examine the line of reasoning that has recently appeared in the literature that connects connectionism with eliminativism. This line of reasoning has it that if connectionist models turn out accurately to characterize our cognition, then beliefs, desires and the other intentional entities of commonsense psychology will be eliminated from our theoretical ontology. In complete contrast I argue (1) that not only is this line of reasoning mistaken about the eliminativist tendencies of connectionist models, but (2) that these models have the potential to provide a more robust vindication of commonsense psychology than classical computational models.

This paper presents considerations in favour of the view that traditional (classical) architectures can be seen as emergent features of connectionist networks with distributed representation. A recent paper by William Bechtel (1988) which argues for a similar conclusion is unsatisfactory in that it fails to consider whether the compositional syntax and semantics attributed to mental representations by classical models can emerge within a connectionist network. The compatibility of the two paradigms hinges largely, I suggest, on how this question is answered. Focusing on the issue of syntax, I argue that while such structure is lacking in connectionist models with local representation, it can be accommodated within networks where representation is distributed. I discuss an important paper by Smolenski (1988) which attempts to show how connectionists can incorporate the relevant syntactic structure, suggesting that some criticisms levelled against that paper by Fodor & Pylyshyn (1988) are wanting. I then go on to indicate a strategy by which a compositional syntax and semantics can be defined for the sort of network that Smolenski describes. I conclude that since the connectionist can respect the central tenets of classicism, the two approaches are compatible with one another.

This paper surveys applications of logical methods in the cognitive sciences. Special attention is paid to non-monotonic logics and complexity theory. We argue that these particular tools have been useful in clarifying the debate between symbolic and connectionist models of cognition.

Does connectionism spell doom for folk psychology? I examine the proposal that cognitive representational states such as beliefs can play no role if connectionist models - - interpreted as radical new cognitive theories -- take hold and replace other cognitive theories. Though I accept that connectionist theories are radical theories that shed light on cognition, I reject the conclusion that neural networks do not represent. Indeed, I argue that neural networks may actually give us a better working notion of cognitive representational states such as beliefs, and in so doing give us a better understanding of how these states might be instantiated in neural wetware.

This commentary examines one aspect of the target article – the comparison of ACT-R with connectionist models. It argues that conceptions of connectionist models should be broadened to cover the whole spectrum of work in this area, especially the so-called hybrid models. Doing so may change drastically ratings of connectionist models, and consequently shed more light on the developing field of cognitive architectures.

If the arguments of chapter 1 are correct, associationist connectionist models (such as ultralocal ones) yield the clearest alternatives to the LOT hypothesis. While it may be that such models cannot provide a general account of cognition, they may account for important aspects of cognition, such as low-level perception (e.g., with the interactive activation model of reading) or the mechanisms which distinguish experts from novices at a given skill (e.g., with dependency-network models). Since these models stand a fighting chance of being applicable to some aspects of cognition, it is important from a philosophical standpoint that we have appropriate tools for understanding such models. In particular, we want to have a theory of the semantic content of representations in certain connectionist models. In this chapter, I want to consider the prospects for applying a specific sort of "fine-grained" theory of content to such models.

The employment of a particular class of computer programs known as "connectionist networks" to model mental processes is a widespread approach to research in cognitive science these days. Little has been written, however, on the precise connection that is thought to hold between such programs and actual in vivo cognitive processes such that the former can be said to "model" the latter in a scientific sense. What is more, this relation can be shown to be problematic. In this paper I give a brief overview of the use of connectionist models in cognitive science, and then explore some of the statements connectionists have made about the nature of the "modeling relation" thought to hold between them and cognitive processes. Finally I show that these accounts are inadequate and that more work is necessary if connectionist networks are to be seriously regarded as scientific models of cognitive processes.

Green offers us two options: either connectionist models are literal models of brain activity or they are mere instruments, with little or no ontological significance. According to Green, only the first option renders connectionist models genuinely explanatory. I think there is a third possibility. Connectionist models are not literal models of brain activity, but neither are they mere instruments. They are abstract, IDEALISED models of the brain that are capable of providing genuine explanations of cognitive phenomena.

Connectionist models of cognition are all the rage these days. They are said to provide better explanations than traditional symbolic computational models in a wide array of cognitive areas, from perception to memory to language to reasoning to motor action. But what does it actually mean to say that they "explain" cognition at all? In what sense do the dozens of nodes and hundreds of connections in a typical connectionist network explain anything? It is the purpose of this paper to explore this question in light of traditional accounts of what it is to be an explanation. We start with an impossibly brief review of some historically important theories of explanation. We then discuss several currently-popular approaches to the question of how connectionist models explain cognition. Third, we describe a theory of causation by philosopher Stephen Yablo that solves some of the problems on which we think many accounts of connectionist explanation founder. Finally, we apply Yablo's theory to these accounts, and show how several important issues surrounding them seem to disappear into thin air in its presence.