The book presents the case that cognitive science should turn its attention to developing theories of human cognition that cover the full range of human perceptual, cognitive, and action phenomena. Cognitive science has now produced a massive number of high-quality regularities with many microtheories that reveal important mechanisms. The need for integration is pressing and will continue to increase. Equally important, cognitive science now has the theoretical concepts and tools to support serious attempts at unified theories. The argument is made (...) entirely by presenting an exemplar unified theory of cognition both to show what a real unified theory would be like and to provide convincing evidence that such theories are feasible. The exemplar is SOAR, a cognitive architecture, which is realized as a software system. After a detailed discussion of the architecture and its properties, with its relation to the constraints on cognition in the real world and to existing ideas in cognitive science, SOAR is used as theory for a wide range of cognitive phenomena: immediate responses ; discrete motor skills ; memory and learning ; problem solving ; language ; and development. The treatments vary in depth and adequacy, but they clearly reveal a single, highly specific, operational theory that works over the entire range of human cognition, SOAR is presented as an exemplar unified theory, not as the sole candidate. Cognitive science is not ready yet for a single theory – there must be multiple attempts. But cognitive science must begin to work toward such unified theories. (shrink)

Because Müiller fails to distinguish between two senses of the term “autonomy,” there is a danger that his results will be misinterpreted by both linguists and neuroscientists. Although he may very well have been successful in refuting one sense of autonomy, he may actually have helped to provide an explanation for the correctness of autonomy in its other sense.

This chapter contains sections titled: Highlights of Past Literature Current Work Future Work.

Neuroclassicism is the view that cognition is explained by “classical” computing mechanisms in the nervous system that exhibit a clear demarcation between processing machinery and read–write memory. The psychologist C. R. Gallistel has mounted a sophisticated defense of neuroclassicism by drawing from ethology and computability theory to argue that animal brains necessarily contain read–write memory mechanisms. This argument threatens to undermine the “connectionist” orthodoxy in contemporary neuroscience, which does not seem to recognize any such mechanisms. In this paper I argue (...) that the neuroclassicist critique rests on a misunderstanding of how computability theory constrains theorizing about natural computing mechanisms. (shrink)

The Miyashita experiments are very interesting and the results should be examined from a viewpoint of attractor dynamics. Amit's target article shows a path toward realistic modeling by artificial neural networks (ANN), but it is not necessarily the only one. I introduce another model that can explain a substantial part of the empirical observations and makes an interesting prediction. This model consists of such units that have nonmonotonic input-output characteristics with local inhibition neurons.

I sketch an explanatory framework that fits a variety of contemporary research programs in cognitive science. I then investigate the scope and the implications of this framework. The framework emphasizes (a) the explanatory role played by the semantic content of cognitive representations, and (b) the important mechanistic, non-intentional dimension of cognitive explanations. I show how both of these features are present simultaneously in certain varieties of cognitive explanation. I also consider the explanatory role played by grounded representational content, that is, (...) content evaluated by appeal to its truth, falsity, accuracy, inaccuracy and other relational properties. (shrink)

The paper presents a paradoxical feature of computational systems that suggests that computationalism cannot explain symbol grounding. If the mind is a digital computer, as computationalism claims, then it can be computing either over meaningful symbols or over meaningless symbols. If it is computing over meaningful symbols its functioning presupposes the existence of meaningful symbols in the system, i.e. it implies semantic nativism. If the mind is computing over meaningless symbols, no intentional cognitive processes are available prior to symbol grounding. (...) In this case, no symbol grounding could take place since any grounding presupposes intentional cognitive processes. So, whether computing in the mind is over meaningless or over meaningful symbols, computationalism implies semantic nativism. (shrink)

Between the later views of Wittgenstein and those of connectionism 1 on the subject of the mastery of language there is an impressively large number of similarities. The task of establishing this claim is carried out in the second section of this paper.

Every immediate memory is unique; it is therefore unlikely to consist of an attractor or even a combination of attractors. In the present state of knowledge about the chemistry of synaptic transmission, there is no reason to look beyond neurons that directly receive sensory afferents for the afterdischarges that correspond to active memories.

This article addresses the ability of Parallel Distributed Processing (PDP) networks to generate stagewise cognitive development in accordance with Piaget's theory of cognitive epigenesis. We carried out a replication study of the simulation experiments by McClelland (1989) and McClelland and Jenkins (1991) in which a PDP network learns to solve balance scale problems. In objective tests motivated from catastrophe theory, a mathematical theory of transitions in epigenetical systems, no evidence for stage transitions in network performance was found. It is concluded (...) that PDP networks lack the ability to recover the positive outcomes of analogous catastrophe analyses of real cognitive developmental data. In an attempt to further characterize the learning behaviour of PDP networks, we carried out a second simulation study using the discrimination‐shift paradigm. The results thus obtained indicate that PDP learning is compatible with the learning of stimulus‐response relationships, not with the acquisition of mediating rules such as conceived in (neo‐)Piagetian theory. In closing, we speculate about the feasibility of simulating stagewise development with alternative network architectures. (shrink)

This paper presents a skill learning model CLARION. Different from existing models of mostly high-level skill learning that use a top-down approach (that is, turning declarative knowledge into procedural knowledge through practice), we adopt a bottom-up approach toward low-level skill learning, where procedural knowledge develops first and declarative knowledge develops later. Our model is formed by integrating connectionist, reinforcement, and symbolic learning methods to perform on-line reactive learning. It adopts a two-level dual-representation framework (Sun, 1995), with a combination of localist (...) and distributed representation. We compare the model with human data in a minefield navigation task, demonstrating some match between the model and human data in several respects. (shrink)

The current renewal of connectionist techniques using networks of neuron-like units has started to have an influence on cognitive modelling. However, compared with classical artificial intelligence methods, the position of connectionism is still not clear. In this article artificial intelligence and connectionism are systematically compared as cognitive models so as to bring out the advantages and shortcomings of each. The problem of structured representations appears to be particularly important, suggesting likely research directions.

There is currently a debate over whether cognitive architecture is classical or connectionist in nature. One finds the following three comparisons between classical architecture and connectionist architecture made in the pro-connectionist literature in this debate: (1) connectionist architecture is neurally plausible and classical architecture is not; (2) connectionist architecture is far better suited to model pattern recognition capacities than is classical architecture; and (3) connectionist architecture is far better suited to model the acquisition of pattern recognition capacities by learning than (...) is classical architecture. If true, (1)–(3) would yield a compelling case against the view that cognitive architecture is classical, and would offer some reason to think that cognitive architecture may be connectionist. We first present the case for (1)–(3) in the very words of connectionist enthusiasts. We then argue that the currently available evidence fails to support any of (1)–(3). (shrink)

There is currently a debate over whether cognitive architecture is classical or connectionist in nature. One finds the following three comparisons between classical architecture and connectionist architecture made in the pro-connectionist literature in this debate: (1) connectionist architecture is neurally plausible and classical architecture is not; (2) connectionist architecture is far better suited to model pattern recognition capacities than is classical architecture; and (3) connectionist architecture is far better suited to model the acquisition of pattern recognition capacities by learning than (...) is classical architecture. If true, (1)-(3) would yield a compelling case against the view that cognitive architecture is classical, and would offer some reason to think that cognitive architecture may be connectionist. We first present the case for (1)-(3) in the very words of connectionist enthusiasts. We then argue that the currently available evidence fails to support any of (1)-(3). (shrink)

I consider the role of cognitive modeling in cognitive science. Modeling, and the computers that enable it, are central to the field, but the role of modeling is often misunderstood. Models are not intended to capture fully the processes they attempt to elucidate. Rather, they are explorations of ideas about the nature of cognitive processes. In these explorations, simplification is essential—through simplification, the implications of the central ideas become more transparent. This is not to say that simplification has no downsides; (...) it does, and these are discussed. I then consider several contemporary frameworks for cognitive modeling, stressing the idea that each framework is useful in its own particular ways. Increases in computer power (by a factor of about 4 million) since 1958 have enabled new modeling paradigms to emerge, but these also depend on new ways of thinking. Will new paradigms emerge again with the next 1,000‐fold increase? (shrink)

the _algorithmic_, and the _implementational_; Zenon Pylyshyn (1984) calls them the _semantic_, the _syntactic_, and the _physical_; and textbooks in cognitive psychology sometimes call them the levels of _content_, _form_, and _medium_ (e.g. Glass, Holyoak, and Santa 1979).

In the present paper I analyse the modularity thesis and, more specifically, the thesis of domain-specificity of processing. I argue that this thesis is not trivial only under the assumption of a variety of processes which differ from each other at the implementation level; otherwise, the variety of cognitive processes can only be explained as emergent on the basic mechanism of associative activation in that it operates on domain-specific representations, which is something that no one would deny. But that assumption (...) is untenable: there are no other processes than associative activation (and inhibition) at the implementation level. Any claim to the contrary is the result of a conceptual confusion between two senses of “associative”: a behavioural one, relative to which there are cognitive processes that exceed the ability to code elementary spatio-temporal contingencies, and one that lies instead at the implementation level. Since the assumption of a plurality of processes at the implementation level is untenable, the only viable interpretation of modularism (as far as domain-specificity is concerned) is a trivial one. By this I do not mean that the thesis is devoid of any content. However, its content is scarcely debatable, and far less thrilling than the debate has suggested so far. (shrink)

The systematicity argument only challenges connectionism if systematicity is a general property of cognition. I examine this thesis in terms of properties of concepts. First, I propose that Evans's Generality Constraint only applies to attributions of belief. Then I defend a variety of conceptual pluralism, arguing that concepts share two fundamental properties related to centrality and belief-attribution, and contending that there are two kinds of concepts that differ in their compositional properties. Finally, I rely on Dual Systems Theory and on (...) differences between animal and human cognition to suggest a scenario of two processing systems that work on different kinds of concepts, with only one of them supporting full systematicity. I sketch a non-classical systematicity argument that rules out classicism as the basis of one of those systems given that it would wrongly entail that both systems are fully systematic. (shrink)

The article criticises the attempt to establish connectionism as an alternative theory of human cognitive architecture through the introduction of thesymbolic/subsymbolic distinction (Smolensky, 1988). The reasons for the introduction of this distinction are discussed and found to be unconvincing. It is shown that thebrittleness problem has been solved for a large class ofsymbolic learning systems, e.g. the class oftop-down induction of decision-trees (TDIDT) learning systems. Also, the process of articulating expert knowledge in rules seems quite practical for many important domains, (...) including common sense knowledge.The article discusses several experimental comparisons betweenTDIDT systems and artificial neural networks using the error backpropagation algorithm (ANNs usingBP). The properties of one of theTDIDT systemsID3 (Quinlan, 1986a) are examined in detail. It is argued that the differences in performance betweenANNs usingBP andTDIDT systems reflect slightly different inductive biases but are not systematic; these differences do not support the view that symbolic and subsymbolic systems are fundamentally incompatible. It is concluded, that thesymbolic/subsymbolic distinction is spurious. It cannot establish connectionism as an alternative cognitive architecture. (shrink)

Cognitive scientists must understand not just what the mind does, but how it does what it does. In this paper, I consider four aspects of cognitive architecture: how the mind develops, the extent to which it is or is not modular, the extent to which it is or is not optimal, and the extent to which it should or should not be considered a symbol‐manipulating device (as opposed to, say, an eliminative connectionist network). In each case, I argue that insights (...) from developmental and evolutionary biology can lead to substantive and important compromises in historically vexed debates. (shrink)

In this paper I describe basic features of traditional (British) emergentism and Popper’s emergentist theory of consciousness and compare them to the contemporary versions of emergentism present in connectionist approach in cognitive sciences. I argue that despite their similarities, the traditional form, as well as Popper’s theory belong to strong causal emergentism and yield radically different ontological consequences compared to the weaker, contemporary version present in cognitive science. Strong causal emergentism denies the causal closure of the physical domain and introduces (...) genuine new mental causal powers and genuine downward causation, while weak emergentism provides new insights in understanding the mechanisms and explanation that is compatible with physicalism. (shrink)

Artificial intelligent (AI) systems that perform image classification tasks are being used to great success in many application contexts. However, many of these systems are opaque, even to experts. This lack of understanding can be problematic for ethical, legal, or practical reasons. The research field Explainable AI (XAI) has therefore developed several approaches to explain image classifiers. The hope is to bring about understanding, e.g., regarding why certain images are classified as belonging to a particular target class. Most of these (...) approaches use visual explanations. Drawing on Elgin’s work (True enough. MIT Press, Cambridge, 2017), I argue that analyzing what those explanations exemplify can help to assess their suitability for producing understanding. More specifically, I suggest to distinguish between two forms of examples according to their suitability for producing understanding. I call these forms samples and exemplars, respectively. Samples are prone to misinterpretation and thus carry the risk of leading to misunderstanding. Exemplars, by contrast, are intentionally designed or chosen to meet contextual requirements and to mitigate the risk of misinterpretation. They are thus preferable for bringing about understanding. By reviewing several XAI approaches directed at image classifiers, I show that most of them explain with samples. If my analysis is correct, it will be beneficial if such explainability methods use explanations that qualify as exemplars. (shrink)

We suggest a common ground for alternative proposals In different domains of cognitive science which have previously seemed to have little in common. The underlying common theme is associated with a redefinition of the basic unit of analysis in each domain of thought. Our framework suggests a definition of unity which is based not on inherent properties of the elements constituting the unit, but rather on dynamic patterns of correlation across the elements. We introduce a set of features that characterize (...) the new dynamic units, and distinguish them from traditional units of analysis. A conceptual connection is made with the identification of elementary units in modern physics theories, as well as with concepts and structures in the study of complex dynamical systems and connectionism. The paper analyses the evolution of the concept of unit in different domains of thought in cognitive science, and examines the proposed framework of “dynamic unity” with regard to various theoretical problems within each domain. The particular cognitive science issues discussed in the paper include: (1) the binding problem in the brain; (2) the mental unit of conceptual organization; (3) defining ‘wordhood’ in linguistics; and (4) identifying the unit of cognition in the natural environment. (shrink)

The Representational Theory of the Mind allows for psychological explanations couched in terms of the contents of propositional attitudes. Propositional attitudes themselves are taken to be relations to mental representations. These representations (partially) determine the contents of the attitudes in which they figure. Thus, Representationalism owes an explanation of the contents of mental representations. This essay constitutes an atomistic theory of the content of formally or syntactically simple mental representation, proposing that the content of such a representation is determined by (...) the intersection of the representation's correlational and control properties. The theory is distinguished from standard information-based accounts of mental content in allowing that the relevant correlations be contingent while insisting on an efferent aspect to mental content. The theory on offer allows for a natural explanation of misrepresentation, finds a niche for the notion of narrow content, welcomes radical first person fallibility with respect to questions of content, admits of mental ambiguity and recognizes that the future of a psychological agent is a factor in determining the content of the agent's present psychological states. (shrink)

The question, ‘Is cognition linguistic?' divides recent cognitive theories into two antagonistic groups. Sententialists claim that we think in some language, while advocates of non linguistic views of cognition deny this claim. The Introspective Argument for Sententialism is one of the most appealing arguments for sententialism. In substance, it claims that the introspective fact of inner speech provides strong evidence that our thoughts are linguistic. This article challenges this argument. I claim that the Introspective Argument for Sententialism confuses the content (...) of our thoughts with their vehicles: while sententialism is a thesis about the vehicles of our thoughts, inner speech sentences are the content of auditory or articulatory images. The rebuttal of the introspective argument for sententialism is shown to have a general significance in cognitive science: Introspection does not tell us how we think. (shrink)