The computational paradigm, which has dominated psychology and artificial intelligence since the cognitive revolution, has been a source of intense debate. Recently, several cognitive scientists have argued against this paradigm, not by objecting to computation, but rather by objecting to the notion of representation. Our analysis of these objections reveals that it is not the notion of representation per se that is causing the problem, but rather specific properties of representations as they are used in various psychological theories. Our analysis (...) suggests that all theorists accept the idea that cognitive processing involves internal information-carrying states that mediate cognitive processing. These mediating states are a superordinate category of representations. We discuss five properties that can be added to mediating states and examine their importance in various cognitive models. Finally, three methodological lessons are drawn from our analysis and discussion. (shrink)

Introductory Chapter to the _Cambridge Handbook of Situated Cognition_ (CUP, 2009).

What is creativity? One new idea may be creative, whereas another is merely new: What's the difference? And how is creativity possible? These questions about human creativity can be answered, at least in outline, using computational concepts. There are two broad types of creativity, improbabilist and impossibilist. Improbabilist creativity involves novel combinations of familiar ideas. A deeper type involves METCS: the mapping, exploration, and transformation of conceptual spaces. It is impossibilist, in that ideas may be generated which – with respect (...) to the particular conceptual space concerned – could not have been generated before. The more clearly conceptual spaces can be defined, the better we can identify creative ideas. Defining conceptual spaces is done by musicologists, literary critics, and historians of art and science. Humanist studies, rich in intuitive subtleties, can be complemented by the comparative rigour of a computational approach. Computational modelling can help to define a space, and to show how it may be mapped, explored, and transformed. Impossibilist creativity can be thought of in “classical” Al terms, whereas connectionism illuminates improbabilist creativity. Most Al models of creativity can only explore spaces, not transform them, because they have no self-reflexive maps enabling them to change their own rules. A few, however, can do so. A scientific understanding of creativity does not destroy our wonder at it, nor does it make creative ideas predictable. Demystification does not imply dehumanization. (shrink)

In this reply to James H. Fetzer’s “Minds and Machines: Limits to Simulations of Thought and Action”, I argue that computationalism should not be the view that (human) cognition is computation, but that it should be the view that cognition (simpliciter) is computable. It follows that computationalism can be true even if (human) cognition is not the result of computations in the brain. I also argue that, if semiotic systems are systems that interpret signs, then both humans and computers are (...) semiotic systems. Finally, I suggest that minds can be considered as virtual machines implemented in certain semiotic systems, primarily the brain, but also AI computers. In doing so, I take issue with Fetzer’s arguments to the contrary. (shrink)

Velmans’ paper raises three problems concerning mental causation: (1) How can consciousness affect the physical, given that the physical world appears causally closed? 10 (2) How can one be in conscious control of processes of which one is not consciously aware? (3) Conscious experiences appear to come too late to causally affect the processes to which they most obviously relate. In an appendix Velmans gives his reasons for refusing to resolve these problems through adopting the position (which he labels ‘physicalism’) (...) that ‘consciousness is nothing more than a state of the brain’. The rest of the paper, then, is an attempt to solve these problems without embracing a reductionist physicalism. Velmans’ solution to the first problem is ‘ontological monism combined with epistemological dualism’: First-person and third-person accounts are two different ways of knowing the same facts. This kind of reply is not new; it is, for example, a twist on the position expressed in Davidson (1970). True, there are substantial differences: For one, Davidson reconciles the tension between descriptions of events in mentalistic and physicalist language, not between firstand third-person descriptions of states; for another, Davidson actually provides an argument for his position, although to do so he assumes that there are no psycho-physical (or indeed, psycho-psycho) laws, something which I suspect Velmans would be reluctant to do. Nevertheless, they have in common the idea that the causal efficacy of the mental is not at odds with the causal closure of physics, since a mind-involving causal story is just another way of talking about the same facts that a purely physical causal story talks about. This ‘dual-aspect’ approach is a popular tactic for resolving the mind--body problem, but it has some well-known problems, and it is unfortunate Velmans doesn’t reply to these standard objections. For example, a frequently discussed issue in connection with theories of mental causation is the problem of overdetermination (see, e.g., Unger, 1977; Peacocke, 1979). (shrink)

The ecological approach to perception-action is unlike the standard approach in several respects. It takes the animal-in-its-environment as the proper scale for the theory and analysis of perception-action, it eschews symbol based accounts of perception-action, it promotes self-organization as the theory-constitutive metaphor for perception-action, and it employs self-referring, non-predicative definitions in explaining perception-action. The present article details the complexity issues confronted by the ecological approach in terms suggested by Rosen and introduces non-well-founded set theory as a potentially useful tool for (...) expressing them. The issues and the tool are brought to focus in the concept of affordance that is the basis for explanation of prospective control of action in the ecological approach. (shrink)

Roughly speaking, computationalism says that cognition is computation, or that cognitive phenomena are explained by the agent‘s computations. The cognitive processes and behavior of agents are the explanandum. The computations performed by the agents‘ cognitive systems are the proposed explanans. Since the cognitive systems of biological organisms are their nervous 1 systems (plus or minus a bit), we may say that according to computationalism, the cognitive processes and behavior of organisms are explained by neural computations. Some people might prefer to (...) say that cognitive systems are ―realized‖ by nervous systems, and thus that—according to computationalism—cognitive computations are ―realized‖ by neural processes. In this paper, nothing hinges on the nature of the relation between cognitive systems and nervous systems, or between computations and neural processes. For present purposes, if a neural process realizes a computation, then that neural process is a computation. Thus, I will couch much of my discussion in terms of nervous systems and neural computation.1 Before proceeding, we should dispense with a possible red herring. Contrary to a common assumption, computationalism does not stand in opposition to connectionism. Connectionism, in the most general and common sense of the term, is the claim that cognitive phenomena are explained (at some level and at least in part) by the processes of neural networks. This is a truism, supported by most neuroscientific evidence. Everybody ought to be a connectionist in this general sense. The relevant question is, are neural processes computations? More precisely, are the neural processes to be found in the nervous systems of organisms computations? Computationalists say ―yes‖, anti-computationalists say ―no‖. This paper investigates whether any of the arguments on offer against computationalism have a chance at knocking it off.2 Ever since Warren McCulloch and Walter Pitts (1943) first proposed it, computationalism has been subjected to a wide range of objections.. (shrink)

The nature of cognition is being re-considered. Instead of emphasizing formal operations on abstract symbols, the new approach foregrounds the fact that cognition is, rather, a situated activity, and suggests that thinking beings ought therefore be considered first and foremost as acting beings. The essay reviews recent work in Embodied Cognition, provides a concise guide to its principles, attitudes and goals, and identifies the physical grounding project as its central research focus.

Vera & Simon (1993a) have argued that the theories and methods known as situated action or situativity theory are compatible with the assumptions and methodology of the physical symbol systems hypothesis and do not require a new approach to the study of cognition. When the central criterion of computational universality is added to the loose definition of a symbol system which Vera and Simon provide, it becomes apparent that there are important incompatibilities between the two approaches such that situativity theory (...) cannot be subsumed within the symbol systems approach. Symbol systems and situativity theoretic approaches are, and should be seen to be, competing approaches to the study of cognition. (shrink)

Embodied, embedded, enactive, and extended theorists do not typically focus on the ontological frameworks in which they develop their theories. One exception is 4E theories that embrace New Mechanism. In this paper, we endorse the New Mechanist’s general turn to ontology, but argue that their ontology is not the best on the market for 4E theories. Instead, we advocate for a different ontology: causal powers realism. Causal powers realism posits that psychological manifestations are the product of mental powers, and that (...) mental powers are empirically-discoverable features of individuals that account for the causal work those individuals do. We contend that causal powers realism provides a unifying framework for the central commitments of 4E theories, as well as additional resources for theorizing in a 4E framework. And while New Mechanism offers some of these resources as well, we argue that causal powers realism is ultimately the better of the two. (shrink)

Novel computational representations, such as simulation models of complex systems and video games for scientific discovery, are dramatically changing the way discoveries emerge in science and engineering. The cognitive roles played by such computational representations in discovery are not well understood. We present a theoretical analysis of the cognitive roles such representations play, based on an ethnographic study of the building of computational models in a systems biology laboratory. Specifically, we focus on a case of model-building by an engineer that (...) led to a remarkable discovery in basic bioscience. Accounting for such discoveries requires a distributed cognition analysis, as DC focuses on the roles played by external representations in cognitive processes. However, DC analyses by and large have not examined scientific discovery, and they mostly focus on memory offloading, particularly how the use of existing external representations changes the nature of cognitive tasks. In contrast, we study discovery processes and argue that discoveries emerge from the processes of building the computational representation. The building process integrates manipulations in imagination and in the representation, creating a coupled cognitive system of model and modeler, where the model is incorporated into the modeler's imagination. This account extends DC significantly, and we present some of the theoretical and application implications of this extended account. (shrink)

This piece is a commentary on a precis of Maggie Boden's book "The creative mind" published in Behavioral and Brain Sciences.

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)

We begin by distinguishing computationalism from a number of other theses that are sometimes conflated with it. We also distinguish between several important kinds of computation: computation in a generic sense, digital computation, and analog computation. Then, we defend a weak version of computationalism—neural processes are computations in the generic sense. After that, we reject on empirical grounds the common assimilation of neural computation to either analog or digital computation, concluding that neural computation is sui generis. Analog computation requires continuous (...) signals; digital computation requires strings of digits. But current neuroscientific evidence indicates that typical neural signals, such as spike trains, are graded like continuous signals but are constituted by discrete functional elements (spikes); thus, typical neural signals are neither continuous signals nor strings of digits. It follows that neural computation is sui generis. Finally, we highlight three important consequences of a proper understanding of neural computation for the theory of cognition. First, understanding neural computation requires a specially designed mathematical theory (or theories) rather than the mathematical theories of analog or digital computation. Second, several popular views about neural computation turn out to be incorrect. Third, computational theories of cognition that rely on non-neural notions of computation ought to be replaced or reinterpreted in terms of neural computation. (shrink)

Systems based on symbolic knowledge have performed extremely well in processing reason, yet, remain beset with problems of brittleness in many domains. Connectionist approaches do similarly well in emulating interactive domains, however, have struggled when modelling higher brain functions. Neither of these dichotomous approaches, however, have provided many inroads into the area of human reasoning that psychology and sociology refer to as the process of practice. This paper argues that the absence of a model for the process of practise in (...) current approaches is a significant contributor to brittleness. This paper will investigate how the process of practise relates to deeper forms of contextual representations of knowledge. While researchers and developers of knowledge based systems have often incorporated the notion of context they treat context as a static entity, neglecting many connectionists’ work in learning hidden and dynamic contexts. This paper argues that the omission of these higher forms of context is one of the fundamental problems in the application and interpretation of symbolic knowledge. Finally, these ideas for modelling context will lead to the reinterpretation of situation cognition which makes a significant step towards a philosophy of knowledge that could lead to the modelling of the process of practice. (shrink)

Cognitive science's basic premises are under attack. In particular, its focus on internal cognitive processes is a target. Intelligence is increasingly interpreted, not as a matter of reclusive thought, but as successful agent-environment interaction. The critics claim that a major reorientation of the field is necessary. However, this will only occur when there is a distinct alternative conceptual framework to replace the old one. Whether or not a serious alternative is provided is not clear. Among the critics there is some (...) consensus, however, that this role could be fulfilled by the concept of a 'behavioral system'. This integrates agent and environment into one encompassing general system. We will discuss two contexts in which the behavioral systems idea is being developed. Autonomous Agents Research is the enterprise of building behavior-based robots. Dynamical Systems Theory provides a mathematical framework well suited for describing the interactions between complex systems. We will conclude that both enterprises provide important contributions to the behavioral systems idea. But neither turns it into a full conceptual alternative which will initiate a major paradigm switch in cognitive science. The concept will need a lot of fleshing out before it can assume that role. (shrink)

The medium‐independence of computational descriptions has shaped common conceptions of computational explanation. So long as our goal is to explain how a system successfully carries out its computations, then we only need to describe the abstract series of operations that achieve the desired input–output mapping, however they may be implemented. It is argued that this abstract conception of computational explanation cannot be applied to so‐called real‐time computing systems, in which meeting temporal deadlines imposed by the systems with which a device (...) interfaces are constitutive of the computing tasks that a device performs. Instead, real‐time computing reveals the need for alternative conceptions of computational explanation, as well as computational implementation, that eschew medium‐independence. (shrink)

This chapter contains sections titled: The Scientific Legitimacy of Mentalism? Cognitive Architecture and Massive Modularity Embodied, Situated, and Extended Cognition Concepts Mindreading Conclusion and Future Directions References.

The literatures on bounded and ecological rationality are built on adaptationism—and its associated modular, cognitivist and computational paradigm—that does not address or explain the evolutionary origins of rationality. We argue that the adaptive mechanisms of evolution are not sufficient for explaining human rationality, and we posit that human rationality presents exaptive origins, where exaptations are traits evolved for other functions or no function at all, and later co-opted for new uses. We propose an embodied reconceptualization of rationality—embodied rationality—based on the (...) reuse of the perception-action system, where many neural processes involved in the control of the sensory-motor system, salient in ancestral environments have been later co-opted to create—by tinkering—high-level reasoning processes, employed in civilized niches. (shrink)

I review the historical context for modeling skilled performance in games. Using Newell's concept of time bands for explaining cognitive behavior, I categorize the current papers in terms of time scales, type of data, and analysis methodologies. I discuss strengths and weaknesses of these approaches for describing skill acquisition and why the study of digital games can address the challenges of replication and generalizability. Cognitive science needs to pay closer attention to population representativeness to enhance generalizability of findings, and to (...) the social band of explanation, in order to explain why so few individuals reach expert levels of performance. (shrink)

PDP networks that use nonmonotonic activation functions often produce hidden unit regularities that permit the internal structure of these networks to be interpreted (Berkeley et al., 1995; McCaughan, 1997; Dawson, 1998). In particular, when the responses of hidden units to a set of patterns are graphed using jittered density plots, these plots organize themselves into a set of discrete stripes or bands. In some cases, each band is associated with a local interpretation. On the basis of these observations, Berkeley (2000) (...) has suggested that these bands are both subsymbolic and symbolic in nature, and has used the analysis of one network to support the claim that there are fewer differences between symbols and subsymbols than one might expect. We suggest below that this conclusion is premature. First, in many cases the local interpretation of each band is difficult to relate to the interpretation of a network's response; a more appropriate relationship only emerges when a band associated with one hidden unit is considered in the context of other bands associated with other hidden units (i.e., interpretations of distributed representations are more useful than interpretations of local representations). Second, the content that a band designates to an external observer (i.e., the interpretation assigned to a band by the researcher) can be quite different from the content that a band designates to the output units of the network itself.. We use two different network simulations – including the one described by Berkeley (2000) – to illustrate these points. We conclude that current evidence involving interpretations of nonmonotonic PDP networks actually illustrates the differences between symbolic and subsymbolic processing. (shrink)