Current debates regarding the possible cognitive implications of ideas from adaptive behavior research and dynamical systems theory would benefit greatly from a careful study of simple model agents that exhibit minimally cognitive behavior. This paper sketches one such agent, and presents the results of preliminary experiments on the evolution of dynamical neural networks for visually-guided orientation, object discrimination and accurate pointing with a simple manipulator to objects appearing in its field of view. 1 Introduction Many of the key ideas emphasized in adaptive behavior research are beginning to have a significant impact on cognitive science. For example, adaptive behavior research in general, and the dynamical perspective on adaptive behavior that is often taken in such research in particular, have begun to significantly influence the growing debates concerning the nature and necessity of notions of representation and computation in explaining cognitive behavio...

We describe a new kind of image representation in terms of local multi–modal Primitives. These Primitives are motivated by processing of the human visual system as well as by functional considerations. We discuss analogies of our representation to human vision and concentrate specifically on the implications of the necessity of communication of information in a complex multi-modal system.

A new algorithmic framework called denoising source separation (DSS) is introduced. The main benefit of this framework is that it allows for easy development of new source separation algorithms which are optimised for specific problems. In this framework, source separation algorithms are constucted around denoising procedures. The resulting algorithms can range from almost blind to highly specialised source separation algorithms. Both simple linear and more complex nonlinear or adaptive denoising schemes are considered. Some existing independent component analysis algorithms are reinterpreted within DSS framework and new, robust blind source separation algorithms are suggested. Although DSS algorithms need not be explicitly based on objective functions, there is often an implicit objective function that is optimised. The exact relation between the denoising procedure and the objective function is derived and a useful approximation of the objective function is presented. In the experimental section, various DSS schemes are applied extensively to artificial data, to real magnetoencephalograms and to simulated CDMA mobile network signals. Finally, various extensions to the proposed DSS algorithms are considered. These include nonlinear observation mappings, hierarchical models and overcomplete, nonorthogonal feature spaces. With these extensions, DSS appears to have relevance to many existing models of neural information processing.

At a time of increased social usage of net and collaborative applications a robust and detailed theory of social presence could contribute to our understanding of social behavior in mediated environments, allow researchers to predict and measure differences among media interfaces, and guide the design of new social environments and interfaces. A broader theory of social presence can guide more valid and reliable measures. The article reviews, classifies, and critiques existing theories and measures of social presence. A set of criteria and scope conditions is proposed to help remedy limitations in past theories and measures and to provide a contribution to a more robust theory and measure of social presence. Keywords: Human-computer interaction, computer-mediated communication, nonverbal communication, new media, communication technology, virtual reality

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The 30th anniversary of The Journal of Portfolio Management is a milestone in the rich intellectual history of modern finance, firmly establishing the relevance of quantitative models and scientific inquiry in the practice of financial management. One of the most enduring ideas from this intellectual history is the Efficient Markets Hypothesis (EMH), a deceptively simple notion that has become a lightning rod for its disciples and the proponents of behavioral economics and finance. In its purest form, the EMH obviates active portfolio management, calling into question the very motivation for portfolio research. It is only fitting that we revisit this groundbreaking idea after three very successful decades of this Journal. In this article, I review the current state of the controversy surrounding the EMH and propose a new perspective that reconciles the two opposing schools of thought. The proposed reconciliation, which I call the Adaptive Markets Hypothesis (AMH), is based on an evolutionary approach to economic interactions, as well as some recent research in the cognitive neurosciences that has been transforming and revitalizing the intersection of psychology and economics. Although some of these ideas have not yet been fully articulated within a rigorous quantitative framework, long time students of the EMH and seasoned practitioners will no doubt recognize immediately the possibilities generated by this new perspective. Only time will tell whether its potential will be fulfilled. I begin with a brief review of the classic version of the EMH, and then summarize the most significant criticisms leveled against it by psychologists and behavioral economists. I argue that the sources of this controversy can

Hierarchy of approximations involved in simplification of microscopic theories, from sub-cellural to the whole brain level, is presented. A new approximation to neural dynamics is described, leading to a Platonic-like model of mind based on psychological spaces. Objects and events in these spaces correspond to quasistable states of brain dynamics and may be interpreted from psychological point of view. Platonic model bridges the gap between neurosciences and psychological sciences. Static and dynamic versions of this model are outlined and Feature Space Mapping, a neurofuzzy realization of the static version of Platonic model, described. Categorization experiments with human subjects are analyzed from the neurodynamical and Platonic model points of view.

Memory evolved to supply useful, timely information to the organism’s decision-making systems. Therefore, decision rules, multiple memory systems, and the search engines that link them should have coevolved to mesh in a coadapted, functionally interlocking way. This adaptationist perspective suggested the scope hypothesis: When a generalization is retrieved from semantic memory, episodic memories that are inconsistent with it should be retrieved in tandem to place boundary conditions on the scope of the generalization. Using a priming paradigm and a decision task involving person memory, the authors tested and confirmed this hypothesis. The results support the view that priming is an evolved adaptation. They further show that dissociations between memory systems are not—and should not be—absolute: Independence exists for some tasks but not others. Memory is a gift of nature, the ability of living organisms to retain and to utilize acquired information or knowledge.... Owners of biological memory systems are capable of behaving more appropriately at a later time because of their experiences at an earlier time, a feat not possible for organisms without memory. (Tulving, 1995a, p. 751) If there is one proposition on which all psychologists seem to

Classical cognitive science assumes that intelligentlybehaving systems must be symbol processors that are implemented in physical systems such as brains or digital computers. By contrast, connectionists suppose that symbol manipulating systems could be approximations of neural networks dynamics. Both classicists and connectionists argue that symbolic computation and subsymbolic dynamics are incompatible, though on different grounds. While classicists saythat connectionist architectures and symbol processors are either incompatible or the former are mere implementations of the latter, connectionists replythat neural networks might be incompatible with symbol processors because the latter cannot be implementations of the former. In this contribution, the notions of “incompatibility ” and “implementation ” will be criticized to show that they must be revised in the context of the dynamical system approach to cognitive science. Examples for implementations of symbol processors that are incompatible with respect to contextual topologies will be discussed. 1.

Understanding complex behavior requires a multidisplinary effort from the neurosciences, psychology, behavioral biology, and computer science. This paper gives an overview of the current state of theoretical thinking in the field. The focus is on a behavior--oriented approach to cognition, i.e., not so much on the mental representations themselves, but on the behaviors that do require these representations. It is the intention of the paper to support the exchange between the different disciplines involved. Examples of different types of models and explanations are discussed, but no comprehensive review of all relevant work is attempted. In the second part, I collect a number of elements that in my view are essential to a future theory of cognitive behavior. Keywords: Cognition, Perception and Action, Brain Theory, Computational Theory, Artificial Life, Virtual Reality Mallot: Behavior--Oriented Approaches to Cognition Page 2 1 Introduction 1.1 Perception, Action, and Cognition The ...