Philosophy of Connectionism, Misc

Organizational learning can be described as a transfer of individuals’ cognitive mental models to shared mental models. Employees, seeking the same colleagues for advice, are structurally equivalent, and the aim of the paper is to study if the concept can act as a conduit for organizational learning. It is argued that the mimicking of colleagues’ advice seeking structures will induce structural equivalence and transfer the accuracy of individuals’ cognitive mental models to shared mental models. Taking a dyadic level of analysis (...) authors revisit a classical case and present novel data analyses.The empirical results indicate that the mimicking of advice seeking structures can alter cognitive accuracy. It is discussed the findings’ implications for organization learning theory and practice, addressed the study’s limitations, and suggested avenues for future research. (shrink)

Fundamental assumptions behind qualitative modelling are critically considered and some inherent problems in that modelling approach are outlined. The problems outlined are due to the assumption that a sufficient set of symbols representing the fundamental features of the physical world exists. That assumption causes serious problems when modelling continuous systems. An alternative for intelligent system building for cases not suitable for qualitative modelling is proposed. The proposed alternative combines neural networks and quantitative modelling.

To date, almost every historical examination of Warren McCulloch and Walter Pitts’s, “A Logical Calculus of the Ideas Immanent in Nervous Activity” has focused its attention on one dimension of their paper, namely, the attempt to relate neuronal action potentials to formulae in (an extension of) Boolean logic.[1] The implicit justification for this focus begins with the observation that this constitutes the most substantial conceptual innovation of the paper. Earlier work in theoretical neurophysiology had provided mathematical descriptions of neural networks (...) using the continuous mathematics of differential equations.[2] The use of mathematical logic was entirely novel. Further, it is this feature of the paper that had the greatest impact on subsequent developments in artificial intelligence, automata theory, cognitive science, computation theory, and connectionism. What the standard perspective undervalues, however, is the highly significant role of closed loops of neurons, both in “A Logical Calculus,” and in McCulloch’s and Pitts’s theorizing about the central nervous system. The principal aim of the present paper, therefore, is to provide a more balanced account of the intellectual context and content of “A Logical Calculus.”. (shrink)

Newell proposed that cognitive theories be developed in an effort to satisfy multiple criteria and to avoid theoretical myopia. He provided two overlapping lists of 13 criteria that the human cognitive architecture would have to satisfy in order to be functional. We have distilled these into 12 criteria: flexible behavior, real-time performance, adaptive behavior, vast knowledge base, dynamic behavior, knowledge integration, natural language, learning, development, evolution, and brain realization. There would be greater theoretical progress if we evaluated theories by a (...) broad set of criteria such as these and attended to the weaknesses such evaluations revealed. To illustrate how theories can be evaluated we apply these criteria to both classical connectionism and the ACT-R theory. The strengths of classical connectionism on this test derive from its intense effort in addressing empirical phenomena in such domains as language and cognitive development. Its weaknesses derive from its failure to acknowledge a symbolic level to thought. In contrast, ACT-R includes both symbolic and subsymbolic components. The strengths of the ACT-R theory derive from its tight integration of the symbolic component with the subsymbolic component. Its weaknesses largely derive from its failure, as yet, to adequately engage in intensive analyses of issues related to certain criteria on Newell's list. Key Words: cognitive architecture; connectionism; hybrid systems; language; learning; symbolic systems. (shrink)

A new, dramatically updated edition of the classic resource on the constantly evolving fields of brain theory and neural networks.

A comprehensive introduction to the Language of Though Hypothesis (LOTH) accessible to general audiences. LOTH is an empirical thesis about thought and thinking. For their explication, it postulates a physically realized system of representations that have a combinatorial syntax (and semantics) such that operations on representations are causally sensitive only to the syntactic properties of representations. According to LOTH, thought is, roughly, the tokening of a representation that has a syntactic (constituent) structure with an appropriate semantics. Thinking thus consists in (...) syntactic operations defined over representations. Most of the arguments for LOTH derive their strength from their ability to explain certain empirical phenomena like productivity, systematicity of thought and thinking. (shrink)

The reemergence of connectionism² has profoundly altered the philosophy of mind. Paul Churchland has argued that it should equally transform the philosophy of science. He proposes that connectionism offers radical and useful new ways of understanding theories and explanations.

The introduction of connectionist or parallel distributed processing (PDP) systems to model cognitive functions has raised the question of the possible relations between these models and traditional information processing models which employ rules to manipulate representations. After presenting a brief account of PDP models and two ways in which they are commonly interpreted by those seeking to use them to explain cognitive functions, I present two ways one might relate these models to traditional information processing models and so not totally (...) repudiate the tradition of modelling cognition through systems of rules and representations. The proposal that seems most promising is that PDP-type structures might provide the underlying framework in which a rule and representation model might be implemented. To show how one might pursue such a strategy, I discuss recent research by Barsalou on the instability of concepts and show how that might be accounted for in a system whose microstructure had a PDP architecture. I also outline how adopting a multi-leveled view of the mind, where on one level the mind employed a PDP-type system and at another level constituted a rule processing system, would allow researchers to relocate some problems which seemed difficult to explain at one level, such as the capacity for concept learning, to another level where it could be handled in a straightforward manner. (shrink)

The notion that the mind is a physical symbol system (Newell) with a determinate functional architecture (Pylyshyn) provides a compelling conception of the relation of cognitive inquiry to neuroscience inquiry: cognitive inquiry explores the activity within the symbol system while neuroscience explains how the symbol system is realized in the brain. However, the view the the mind is a physical symbol system is being challenged today by researchers in artificial intelligence who propose that the mind is a connectionist system and (...) not simply a rule processing system. I describe this challenge and offer evidence that indicates the challenge may be well motivated. I then turn to the question of how such changes in the conception of the activity of the mind will affect our understanding of the relation of neuroscience to cognitive inquiry and sketch a framework in which the cognitive system consists of several levels and in which both neuroscience and cognitive science can make contributions at several of these levels. (shrink)

Contemporary epistemology has assumed that knowledge is represented in sentences or propositions. However, a variety of extensions and alternatives to this view have been proposed in other areas of investigation. We review some of these proposals, focusing on (1) Ryle's notion of knowing how and Hanson's and Kuhn's accounts of theory-laden perception in science; (2) extensions of simple propositional representations in cognitive models and artificial intelligence; (3) the debate concerning imagistic versus propositional representations in cognitive psychology; (4) recent treatments of (...) concepts and categorization which reject the notion of necessary and sufficient conditions; and (5) parallel distributed processing (connectionist) models of cognition. This last development is especially promising in providing a flexible, powerful means of representing information nonpropositionally, and carrying out at least simple forms of inference without rules. Central to several of the proposals is the notion that much of human cognition might consist in pattern recognition rather than manipulation of rules and propositions. (shrink)

Since the emergence of what Fodor and Pylyshyn (1988) call 'new connectionism', there can be little doubt that connectionist research has become a significant topic for discussion in the Philosophy of Cognitive Science and the Philosophy of Mind. In addition to the numerous papers on the topic in philosophical journals, almost every recent book in these areas contain at least a brief reference to, or discussion of, the issues raised by connectionist research (see Sterelny 1990, Searle, 1992, and O Nualláin, (...) 1995, for example). Other texts have focused almost exclusively upon connectionist issues (see Clark, 1993, Bechtel and Abrahamsen, 1991 and Lloyd, 1989, for example). Regrettably the discussions of connectionism found in the philosophical literature suffer from a number of deficiencies. My purpose in this paper is to highlight one particular problem and attempt to take a few steps to remedy the situation. (shrink)

Connectionism is a style of modeling based upon networks of interconnected simple processing devices. This style of modeling goes by a number of other names too. Connectionist models are also sometimes referred to as 'Parallel Distributed Processing' (or PDP for short) models or networks.1 Connectionist systems are also sometimes referred to as 'neural networks' (abbreviated to NNs) or 'artificial neural networks' (abbreviated to ANNs). Although there may be some rhetorical appeal to this neural nomenclature, it is in fact misleading as (...) connectionist networks are commonly significantly dissimilar to neurological systems. For this reason, I will avoid using this terminology, other than in direct quotations. Instead, I will follow the practice I have adopted above and use 'connectionist' as my primary term for systems of this kind. (shrink)

According to the standard (recent) history of connectionism (see for example the accounts offered by Hecht-Nielsen (1990: pp. 14-19) and Dreyfus and Dreyfus (1988), or Papert's (1988: pp. 3-4) somewhat whimsical description), in the early days of Classical Computational Theory of Mind (CCTM) based AI research, there was also another allegedly distinct approach, one based upon network models. The work on network models seems to fall broadly within the scope of the term 'connectionist' (see Aizawa 1992), although the term had (...) yet to be coined at the time. These two approaches were "two daughter sciences" according to Papert (1988: p. 3). The fundamental difference between these two 'daughters', lay (according to Dreyfus and Dreyfus (1988: p. 16)) in what they took to be the paradigm of intelligence. Whereas the early connectionists took learning to be fundamental, the traditional school concentrated upon problem solving. (shrink)

I sketch a theory of cognitive representation from recent "connectionist" cognitive science. I then argue that (i) this theory is reducible to neuroscientific theories, yet (ii) its kinds are multiply realized at a neurobiological level. This argument demonstrates that multiple realizability alone is no barrier to the reducibility of psychological theories. I conclude that the multiple realizability argument, the most influential argument against psychophysical reductionism, should be abandoned.

There is a distinction between locality and modularity. These two terms have often been used interchangeably in the target article and commentary. Using this distinction we argue in favor of a modularity. In addition we also argue that both PDP-type networks and box-and-arrow models have their own strengths and pitfalls.

A competence model describes the abstract structure of a solution to some problem. or class of problems, facing the would-be intelligent system. Competence models can be quite derailed, specifying far more than merely the function to be computed. But for all that, they are pitched at some level of abstraction from the details of any particular algorithm or processing strategy which may be said to realize the competence. Indeed, it is the point and virtue of such models to specify some (...) equivalence class of algorithms/processing strategies so that the common properties highlighted by the chosen class may feature in psychologically interesting accounts. A question arises concerning the type of relation a theorist might expect to hold between such a competence model and a psychologically real processing strategy. Classical work in cognitive science expects the actual processing to depend on explicit or tacit knowledge of the competence theory. Connectionist work, for reasons to be explained, represents a departure from this norm. But the precise way in which a connectionist approach may disturb the satisfying classical symmetry of competence and processing has yet to be properly specified. A standard ?Newtonian? connectionist account, due to Paul Smolensky, is discussed and contrasted with a somewhat different ?rogue? account. A standard connectionist understanding has it that a classical competence theory describes an idealized subset of a network's behaviour. But the network's behaviour is not to be explained by its embodying explicit or tacit knowledge of the information laid out in the competence theory. A rogue model, by contrast, posits either two systems, or two aspects of a single system, such that one system does indeed embody the knowledge laid out in the competence theory. (shrink)

This is an amended version of material that first appeared in A. Clark, Microcognition: Philosophy, Cognitive Science, and Parallel Distributed Processing (MIT Press, Cambridge, MA, 1989), Ch. 1, 2, and 6. It appears in German translation in Metzinger,T (Ed) DAS LEIB-SEELE-PROBLEM IN DER ZWEITEN HELFTE DES 20 JAHRHUNDERTS (Frankfurt am Main: Suhrkamp. 1999).

It is not widely realised that Turing was probably the first person to consider building computing machines out of simple, neuron-like elements connected together into networks in a largely random manner. Turing called his networks unorganised machines. By the application of what he described as appropriate interference, mimicking education an unorganised machine can be trained to perform any task that a Turing machine can carry out, provided the number of neurons is sufficient. Turing proposed simulating both the behaviour of the (...) network and the training process by means of a computer program. We outline Turing's connectionist project of 1948. (shrink)

In this paper, I define tacit knowledge as a kind of causal-explanatory structure, mirroring the derivational structure in the theory that is tacitly known. On this definition, tacit knowledge does not have to be explicitly represented. I then take the notion of a modular theory, and project the idea of modularity to several different levels of description: in particular, to the processing level and the neurophysiological level. The fundamental description of a connectionist network lies at a level between the processing (...) level and the physiological level. At this level, connectionism involves a characteristic departure from modularity, and a correlative absence of syntactic structure. This is linked to the fact that tacit knowledge descriptions of networks are only approximately true. A consequence is that strict causal systematicity in cognitive processes poses a problem for the connectionist programme. (shrink)

In this article, we highlight three questions: (1) Does human cognition rely on structured internal representations? (2) How should theories, models and data relate? (3) In what ways might embodiment, action and dynamics matter for understanding the mind and the brain?

Critical notice of Andy Clark's "Associative Engines".

A possible relation between Derrida's deconstruction of metaphysics and connectionism is explored by considering diffeacuterance in neural nets terms. First diffeacuterance, as the crossing of Saussurian difference and Freudian deferral, is modeled and then the fuller 'sheaf of diffeacuterance is taken up. The metaphysically conceived brain has two versions: in the traditional computational version the brain processes information like a computer and in the connectionist version the brain computes input vector to output vector transformations non-symbolically. The 'deconstructed brain' neither processes (...) information nor computes functions but is spontaneously economical. (shrink)

In the late 1980s, there were many who heralded the emergence of connectionism as a new paradigm – one which would eventually displace the classically symbolic methods then dominant in AI and Cognitive Science. At present, there remain influential connectionists who continue to defend connectionism as a more realistic paradigm for modeling cognition, at all levels of abstraction, than the classical methods of AI. Not infrequently, one encounters arguments along these lines: given what we know about neurophysiology, it is just (...) not plausible to suppose that our brains are digital computers. Thus, they could not support a classical architecture. I argue here for a middle ground between connectionism and classicism. I assume, for argument's sake, that some form(s) of connectionism can provide reasonably approximate models – at least for lower-level cognitive processes. Given this assumption, I argue on theoretical and empirical grounds that most human mental skills must reside in separate connectionist modules or sub-networks. Ultimately, it is argued that the basic tenets of connectionism, in conjunction with the fact that humans often employ novel combinations of skill modules in rule following and problem solving, lead to the plausible conclusion that, in certain domains, high level cognition requires some form of classical architecture. During the course of argument, it emerges that only an architecture with classical structure could support the novel patterns of information flow and interaction that would exist among the relevant set of modules. Such a classical architecture might very well reside in the abstract levels of a hybrid system whose lower-level modules are purely connectionist. (shrink)