Online research in philosophy

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.

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.

To begin, I introduce an analysis of interlevel relations that allows us to offer an initial characterization of the debate about the way classical and connectionist models relate. Subsequently, I examine a compatibility thesis and a conditional claim on this issue.With respect to the compatibility thesis, I argue that, even if classical and connectionist models are not necessarily incompatible, the emergence of the latter seems to undermine the best arguments for the Language of Thought Hypothesis, which is essential to the former.

In 1982, Feldman and Ballard published "Connectionist models and their properties" in Cognitive Science , helping to focus attention on a family of similarly inspired research strategies just then under way, by giving the family a name: "connectionism." Now, seven years later, the connectionist nation has swelled to include such subfamilies as "PDP" and "neural net models." Since the ideological foes of connectionism are keen to wipe it out in one fell swoop aimed at its "essence", it is worth noting the diversity of not only the models but also the aspirations of the modelers. There is no good reason to suppose that they all pledge allegiance to any one principle..

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.

The commentators have raised some interesting issues but none question the viability of a localist approach to connectionist modelling. Once localist models are properly defined they can be seen to exhibit many properties relevant to the modelling of both psychological and brain function. They can be used to implement exemplar models, prototype models and models of sequence memory and they form a foundation upon which symbolic models can be constructed. Localist models are insensitive to interference and have learning rules that are biologically plausible. They have more explanatory value than their distributed counterparts and they relate transparently to a number of classic mathematical models of behaviour.

This paper explores the question of whether connectionist models of cognition should be considered to be scientific theories of the cognitive domain. It is argued that in traditional scientific theories, there is a fairly close connection between the theoretical (unobservable) entities postulated and the empirical observations accounted for. In connectionist models, however, hundreds of theoretical terms are postulated -- viz., nodes and connections -- that are far removed from the observable phenomena. As a result, many of the features of any given connectionist model are relatively optional. This leads to the question of what, exactly, is learned about a cognitive domain modelled by a connectionist network.

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.

Over the last decade, fully distributed models have become dominant in connectionist psychological modelling, whereas the virtues of localist models have been underestimated. This target article illustrates some of the benefits of localist modelling. Localist models are characterized by the presence of localist representations rather than the absence of distributed representations. A generalized localist model is proposed that exhibits many of the properties of fully distributed models. It can be applied to a number of problems that are difficult for fully distributed models, and its applicability can be extended through comparisons with a number of classic mathematical models of behaviour. There are reasons why localist models have been underused, though these often misconstrue the localist position. In particular, many conclusions about connectionist representation, based on neuroscientific observation, can be called into question. There are still some problems inherent in the application of fully distributed systems and some inadequacies in proposed solutions to these problems. In the domain of psychological modelling, localist modelling is to be preferred. Key Words: choice; competition; connectionist modelling; consolidation; distributed; localist; neural networks; reaction-time.

Holistically ignited Hebbian models are fundamentally different from the serially organized connectionist implementations of language. This may be important for the recovery of language after injury, because connectionist models have provided useful insights into recovery of some cognitive functions. I ask whether cell assembly modelling can make an important contribution and whether the apparent incompatibility with successful connectionist modelling is a problem.