What is the relation between the material, conventional symbol structures that we encounter in the spoken and written word, and human thought? A common assumption, that structures a wide variety of otherwise competing views, is that the way in which 10 these material, conventional symbol-structures do their work is by being translated into some kind of content-matching inner code. One alternative to this view is the tempting but thoroughly elusive idea that we somehow think in some natural language (such as English). In the present treatment I explore a third option, which I shall call the ‘‘complementarity’ ’ view of language. According to this third view the actual symbol 15 structures of a given language add cognitive value by complementing (without being replicated by) the more basic modes of operation and representation endemic to the biological brain. The ‘‘cognitive bonus’ ’ that language brings is, on this model, not to be cashed out either via the ultimately mysterious notion of ‘‘thinking in a given natural language’ ’ or via some process of exhaustive translation into another inner code. Instead, 20 we should try to think in terms of a kind of coordination dynamics in which the forms and structures of a language qua material symbol system play a key and irreducible role. Understanding language as a complementary cognitive resource is, I argue, an important part of the much larger project (sometimes glossed in terms of the ‘‘extended mind’’) of understanding human cognition as essentially and multiply hybrid: as involving 25 a complex interplay between internal biological resources and external non-biological resources.

We present an approach to learn inverse kinematics of redundant systems without prior- or expert-knowledge. The method allows for an iterative bootstrapping and refinement of the inverse kinematics estimate. The essential novelty lies in a path based sampling approach: we generate trainig data along pathes, which result from execution of the currently learned estimate along a desired path towards a goal. The information structure thereby induced enables an efficient detection and resolution of inconsistent samples solely from directly observable data. We derive and illustrate the exploration and learning process with a low-dimensional kinematic example that provides direct insight into the bootstrapping process. We further show that the method scales for high dimensional problems, such as the Honda humanoid robot or hyperredundant planar arms with up to 50 degrees of freedom.

Are minds really dynamical or are they really symbolic? Because minds are bundles of computations, and because computation is always a matter of interpretation of one system by another, minds are necessarily symbolic. Because minds, along with everything else in the universe, are physical, and insofar as the laws of physics are dynamical, minds are necessarily dynamical systems. Thus, the short answer to the opening question is “yes.” It makes sense to ask further whether some of the computations that constitute a human mind are constrained by functional, algorithmic, or implementational factors to be essentially of the discrete symbolic variety (even if they supervene on an apparently continuous dynamical substrate). I suggest that here too the answer is “yes” and discuss the need for such discrete, symbolic cognitive computations in communication-related tasks.

Computational models have played a central role in the debate over language learnability. This article discusses how they have been used in different “stances”, from generative views to more recently introduced explanatory frameworks based on embodiment, cognitive development and cultural evolution. By digging into the details of certain specific models, we show how they organize, transform and rephrase defining questions about what makes language learning possible for children. Finally, we present a tentative synthesis to recast the debate using the notion of learning bias.

Abstract — Neural networks have been proposed as an ideal cognitive modeling methodology to deal with the symbol grounding problem. More recently, such neural network approaches have been incorporated in studies based on cognitive agents and robots. In this paper we present a new model of symbol grounding transfer in cognitive robots. Language learning simulations demonstrate that robots are able to acquire new action concepts via linguistic instructions. This is achieved by autonomously transferring the grounding from directly grounded action names to new higher-order composite actions. The robot’s neural network controller permits such a grounding transfer. The implications for such a modeling approach in cognitive science and autonomous robotics are discussed.

Traditionally, in robotics, artificial intelligence, and neuroscience, there has been a focus on the study of the control or the neural system itself. Recently there has been an increasing interest into the notion of embodiment in all disciplines dealing with intelligent behavior, including psychology, philosophy, and linguistics. In this paper, we explore the far-reaching and often surprising implications of this concept. While embodiment has often been used in its trivial meaning, i.e. „intelligence requires a body“, there are deeper and more important consequences, concerned with connecting brain, body, and environment, or more generally with the relation between physical and information (neural, control) processes. It turns out that, for example, robots designed by exploiting embodiment are frequently simpler, more robust and adaptive than those based on the classical control paradigm. Often, morphology and materials can take over some of the functions normally attributed to control, a phenomenon called “morphological computation”. It can be shown that through the embodied interaction with the environment, in particular through sensory-motor coordination, information structure is induced in the sensory data, thus facilitating perception and learning. A number of case studies are presented to illustrate the concept of embodiment. We conclude with some speculations about potential lessons for robotics.

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Cognitive science is, more than anything else, a pursuit of cognitive mechanisms. To make headway towards a mechanistic account of any particular cognitive phenomenon, a researcher must choose among the many architectures available to guide and constrain the account. It is thus fitting that this volume on contemporary debates in cognitive science includes two issues of architecture, each articulated in the 1980s but still unresolved: • Just how modular is the mind? (section 1) – a debate initially pitting encapsulated mechanisms (Fodorian modules that feed their ultimate outputs to a nonmodular central cognition) against highly interactive ones (e.g., connectionist networks that continuously feed streams of output to one another). • Does the mind process language-like representations according to formal rules? (this section) – a debate initially pitting symbolic architectures (such as Chomsky’s generative grammar or Fodor’s language of thought) against less language-like architectures (such as connectionist or dynamical ones). Our project here is to consider the second issue within the broader context of where cognitive science has been and where it is headed. The notion that cognition in general—not just language

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Thirty years ago, grounded cognition had roots in philosophy, perception, cognitive linguistics, psycholinguistics, cognitive psychology, and cognitive neuropsychology. During the next 20 years, grounded cognition continued developing in these areas, and it also took new forms in robotics, cognitive ecology, cognitive neuroscience, and developmental psychology. In the past 10 years, research on grounded cognition has grown rapidly, especially in cognitive neuroscience, social neuroscience, cognitive psychology, social psychology, and developmental psychology. Currently, grounded cognition appears to be achieving increased acceptance throughout cognitive science, shifting from relatively minor status to increasing importance. Nevertheless, researchers wonder whether grounded mechanisms lie at the heart of the cognitive system or are peripheral to classic symbolic mechanisms. Although grounded cognition is currently dominated by demonstration experiments in the absence of well-developed theories, the area is likely to become increasingly theory driven over the next 30 years. Another likely development is the increased incorporation of grounding mechanisms into cognitive architectures and into accounts of classic cognitive phenomena. As this incorporation occurs, much functionality of these architectures and