Computation In Cognitive Science Research Articles

Symbol and Substrate: A Methodological Approach to Computation in Cognitive Science

Symbol and Substrate: A Methodological Approach to Computation in Cognitive Science

Morphological, Natural, Analog, and Other Unconventional Forms of Computing for Cognition and Intelligence

According to the currently dominant view, cognitive science is a study of mind and intelligence focused on computational models of knowledge in humans. It is described in terms of symbol manipulation over formal language. This approach is connected with a variety of unsolvable problems, as pointed out by Thagard. In this paper, I argue that the main reason for the inadequacy of the traditional view of cognition is that it detaches the body of a human as the cognizing agent from the higher-level abstract knowledge generation. It neglects the dynamical aspects of cognitive processes, emotions, consciousness, and social aspects of cognition. It is also uninterested in other cognizing agents such as other living beings or intelligent machines. Contrary to the traditional computationalism in cognitive science, the morphological computation approach offers a framework that connects low-level with high-level approaches to cognition, capable of meeting challenges listed by Thagard. To establish this connection, morphological computation generalizes the idea of computation from symbol manipulation to natural/physical computation and the idea of cognition from the exclusively human capacity to the capacity of all goal-directed adaptive self-reflective systems, living organisms as well as robots. Cognition is modeled as a layered process, where at the lowest level, systems acquire data from the environment, which in combination with the already stored data in the morphology of an agent, presents the basis for further structuring and self-organization of data into information and knowledge.

The Explanatory Role of Computation in Cognitive Science

Which notion of computation (if any) is essential for explaining cognition? Five answers to this question are discussed in the paper. (1) The classicist answer: symbolic (digital) computation is required for explaining cognition; (2) The broad digital computationalist answer: digital computation broadly construed is required for explaining cognition; (3) The connectionist answer: sub-symbolic computation is required for explaining cognition; (4) The computational neuroscientist answer: neural computation (that, strictly, is neither digital nor analogue) is required for explaining cognition; (5) The extreme dynamicist answer: computation is not required for explaining cognition. The first four answers are only accurate to a first approximation. But the devil is in the details. The last answer cashes in on the parenthetical if any in the question above. The classicist argues that cognition is symbolic computation. But digital computationalism need not be equated with classicism. Indeed, computationalism can, in principle, range from digital (and analogue) computationalism through (the weaker thesis of) generic computationalism to (the even weaker thesis of) digital (or analogue) pancomputationalism. Connectionism, which has traditionally been criticised by classicists for being non-computational, can be plausibly construed as being either analogue or digital computationalism (depending on the type of connectionist networks used). Computational neuroscience invokes the notion of neural computation that may (possibly) be interpreted as a sui generis type of computation. The extreme dynamicist argues that the time has come for a post-computational cognitive science. This paper is an attempt to shed some light on this debate by examining various conceptions and misconceptions of (particularly digital) computation.

Defending the Semantic Conception of Computation in Cognitive Science

Defending the Semantic Conception of Computation in Cognitive Science

Computation in cognitive science: it is not all about Turing-equivalent computation

It is sometimes suggested that the history of computation in cognitive science is one in which the formal apparatus of Turing-equivalent computation, or effective computability, was exported from mathematical logic to ever wider areas of cognitive science and its environs. This paper, however, indicates some respects in which this suggestion is inaccurate. Computability theory has not been focused exclusively on Turing-equivalent computation. Many essential features of Turing-equivalent computation are not captured in definitions of computation as (digital) symbol manipulation. Turing-equivalent computation did not play the role in McCulloch and Pitts’s early cybernetic work that is sometimes attributed to it. Finally, various segments of the neuroscientific community invoke a notion of computation that differs from the Turing-equivalent notion.

Computation, External Factors, and Cognitive Explanations

Computational properties, it is standardly assumed, are to be sharply distinguished from semantic properties. Specifically, while it is standardly assumed that the semantic properties of a cognitive system are externally or non-individualistically individuated, computational properties are supposed to be individualistic and internal. Yet some philosophers (e.g., Tyler Burge) argue that content impacts computation, and further, that environmental factors impact computation. Oron Shagrir has recently argued for these theses in a novel way, and gave them novel interpretations. In this paper I present a conception of computation in cognitive science that takes Shagrir's conception as its starting point, but further develops it in various directions and strengthens it. I argue that the explanatory role of computational properties emerges from the idea that syntactical properties and the relevant external factors presented by cognitive systems compose wide computational properties. I also elaborate upon the notion of content that is in play, and argue that it is contents of the kind that are ascribed by transparent interpretations of content ascriptions that impact computation. This fact enables the thesis that external factors impact computation to rebuff the challenge which concerns the claim that psychology must be individualistic.

How do connectionist networks compute?

Although connectionism is advocated by its proponents as an alternative to the classical computational theory of mind, doubts persist about its computational credentials. Our aim is to dispel these doubts by explaining how connectionist networks compute. We first develop a generic account of computation-no easy task, because computation, like almost every other foundational concept in cognitive science, has resisted canonical definition. We opt for a characterisation that does justice to the explanatory role of computation in cognitive science. Next we examine what might be regarded as the "conventional" account of connectionist computation. We show why this account is inadequate and hence fosters the suspicion that connectionist networks are not genuinely computational. Lastly, we turn to the principal task of the paper: the development of a more robust portrait of connectionist computation. The basis of this portrait is an explanation of the representational capacities of connection weights, supported by an analysis of the weight configurations of a series of simulated neural networks.

From Cartesian towards non-Cartesian pragmatics

This paper focuses on the philosophical and methodological problems of modern cognitive science and linguistic pragmatics in the context of their interdependence and a growing convergence of goals and methods. First of all, it introduces the distinction between a Cartesian and non-Cartesian view of doing science. The traditional Cartesian paradigm associated with (1) the idea of nativism, (2) Noam Chomsky's approach to linguistics and (3) computationalism in cognitive science has to face strong opposition from non-Cartesian trends both in cognitive science and linguistic pragmatics. The Cartesian autonomous agent (speaker-hearer) is replaced by a pancontextualized cognitive agent dependent on a number of internal (mental) factors, such as pertaining to the cognitive-affective-conative system, and external ones, e.g., socio-cultural, interactional, and relational. Such an agent has to participate in ‘joint actions’, ‘collective cognition’, ‘dynamic cognition’ and be aware of a plethora of emergent phenomena in social interaction. The integrative approaches in linguistic pragmatics reveal different degrees of integration, theoretical justification, empirical corroboration and methodological advances. It has been noted that progress in such a ‘holistic pragmatics’ seems to depend on developments in cognitive science and vice versa. Finally, it has been suggested that the construction of a holistic approach to linguistic pragmatics should satisfy a number of ‘minimal requirements’ that so far have not been met. The discussion is closely related to important philosophical problems, such as (1) rationalism vs. empiricism; (2) the reductionism vs. holism dichotomies; and the debates on (3) the unity vs. diversity of science and (4) the role of context in contemporary research.

Hybrid computation in cognitive science: Neural networks and symbols

AbstractThe human brain seems to be a hybrid computer, partly a highly efficient, highly evolved, massively parallel ‘intuitive’ and ‘perceptual’ processor, and, partly a slow and inaccurate serial symbol manipulator. Neural network models seem compatible with the massively parallel hardware, and are probably the best current model for the parallel part of the hybrid. Traditional symbol‐based artificial intelligence has usually based its models on the presumed properties of the symbol manipulator. Potentially, the strengths and weaknesses of the two parts of the hybrid complement each other nicely, and ultimately, a computer could make good use of both strategies simultaneously. However, the coupling between them in humans is still uneasy, perhaps because this novel combination is biologically recent. A brief discussion of human ‘computation’ suggests the virtues and problems inherent in a hybrid.