Technical Narrators and the Possibilities of Cognitive Assemblages in Kazuo Ishiguro’s <i>Klara and the Sun</i>

Cognitive Hearing Science or Auditory Cognitive Science is an emerging field of interdisciplinary research concerning the interactions between hearing and cognition. It follows a trend over the last half century for interdisciplinary fields to develop, beginning with Neuroscience, then Cognitive Science, then Cognitive Neuroscience, and then Cognitive Vision Science. A common theme is that an interdisciplinary approach is necessary to understand complex human behaviors, to develop technologies incorporating knowledge of these behaviors, and to find solutions for individuals with impairments that undermine typical behaviors. Accordingly, researchers in traditional academic disciplines, such as Psychology, Physiology, Linguistics, Philosophy, Anthropology, and Sociology benefit from collaborations with each other, and with researchers in Computer Science and Engineering working on the design of technologies, and with health professionals working with individuals who have impairments. The factors that triggered the emergence of Cognitive Hearing Science include the maturation of the component disciplines of Hearing Science and Cognitive Science, new opportunities to use complex digital signal-processing to design technologies suited to performance in challenging everyday environments, and increasing social imperatives to help people whose communication problems span hearing and cognition. Cognitive Hearing Science is illustrated in research on three general topics: (1) language processing in challenging listening conditions; (2) use of auditory communication technologies or the visual modality to boost performance; (3) changes in performance with development, aging, and rehabilitative training. Future directions for modeling and the translation of research into practice are suggested.

Philosophical Posthumanism

Exploration of a new integrative intellectual enterprise: the cognitive social sciences. Research in the cognitive sciences has advanced significantly in recent decades. Computational cognitive modeling has profoundly changed the ways in which we understand cognition. Empirical research has progressed as well, offering new insights into many psychological phenomena. This book investigates the possibility of exploiting the successes of the cognitive sciences to establish a better foundation for the social sciences, including the disciplines of sociology, anthropology, economics, and political science. The result may be a new, powerful, integrative intellectual enterprise: the cognitive social sciences. The book treats a range of topics selected to capture issues that arise across the social sciences, covering computational, empirical, and theoretical approaches. The chapters, by leading scholars in both the cognitive and the social sciences, explore the relationship between cognition and society, including such issues as methodologies of studying cultural differences; the psychological basis of politics (for instance, the role of emotion and the psychology of moral choices); cognitive dimensions of religion; cognitive approaches to economics; meta-theoretical questions on the possibility of the unification of social and cognitive sciences. Combining depth and breadth, the book encourages fruitful interdisciplinary interaction across many fields.

The new Atheism based in cognitive science questions all three elements: 1) I, believe in, and 3) God. Belief is certainly an object of study in cognitive science, and the results of research do not closely match popular notions of faith. With respect to God, cognitive scientists can legitimately ask: If a god did exist, how would its own cognitive functions operate, and what material basis would enable them? The scientific discipline that has studied religion in modern societies most intensively is sociology, and the original scope of cognitive science left out sociology, and indeed much of social cognition in general. This chapter anticipates the currently progressing union of cognitive science with cognitive social science, and employ a broader definition of the field that includes social cognition, communication, and the ways that humans use information pragmatically during interaction with others. Keywords: cognitive science; communication; new atheism; social cognition

Cognitive and educational sciences share a fundamental interest in learning. While theoretical bases and practical interests seem to match well at first sight, it is well worth to take a closer look. In the different disciplines involved in the Cognitive Sciences there is a large number of approaches to study learning, but closer glance reveals a limited number of ways to conceptualize learning. In its short history, the field has seen some paradigm shifts, which changed the way we look at human cognition and thus at learning. It is worth noting that each paradigm also transports an implicit notion of knowledge. In the field of education we find these paradigms as different learning theories, which in turn determine approaches to teaching and learning. Dealing with knowledge is central to educational practice. Furthermore, educational research indicates that our personal concept of knowledge qualitatively influences the way students learn. Thus there is a deep, but the intricate relationship between our concepts learning and knowledge. If the interaction between the two fields is to go beyond the (often unsatisfying) attempt to find direct-to-classroom recipes, explicit concepts of learning and knowledge must be the starting point for any successful interdisciplinary collaboration. Key words: cognitive sciences, education, knowledge, learning.

The growth of multidisciplinarity in the Cognitive Science Society

Definitions of cognitive science typically emphasize the field’s interdisciplinary roots. As a case study of the field of cognitive science, we analyzed the activities of the Cognitive Science Society with a particular emphasis on the interdisciplinary nature of the field, now and since its inception. Analyses of departmental affiliations, training backgrounds, research methodology, and paper citations suggest that the journal Cognitive Science and the Annual Meeting of the Cognitive Science Society are dominated by cognitive psychology and computer science, rather than being an equal division among the constituent disciplines of cognitive science. However, at many levels, a growing percentage of work was found to involve a conjunction of multiple disciplines, such that approximately 30-50% of recent work in the Cognitive Science Society is interdisciplinary. The goal of this symposium is to discuss the factors (psychological, historical, sociological, and others) that have lead to the current state of affairs, and what may be done to further increase interdisciplinarity in cognitive science.

Summary form only given. A fundamental challenge for almost all scientific disciplines is to explain how natural intelligence is generated by physiological organs and what the logical model of the brain is beyond its neural architectures. According to cognitive informatics and abstract intelligence, the exploration of the brain is a complicated recursive problem where contemporary denotational mathematics is needed to efficiently deal with it. Cognitive psychology and medical science were used to explain that the brain works in a certain way based on empirical observations on related activities in usually overlapped brain areas. However, the lack of precise models and rigorous causality in brain studies has dissatisfied the formal expectations of researchers in computational intelligence and mathematics, because a computer, the logical counterpart of the brain, might not be explained in such a vague and empirical approach without the support of formal models and rigorous means. In order to fonnally explain the architectures and functions of the brain, as well as their intricate relations and interactions, systematic models of t he brain are s ought for revealing the principles and mechanisms of the brain at the neural, physiological, cognitive, and logical (abstract) levels. Cognitive and brain informatics investigate into the brain via not only inductive syntheses through these four cognitive levels from the bottom up in order to form theories based on empirical observations, but also deductive analyses from the top down in order to explain various functional and behavioral instances according to the abstract intelligence theory. This keynote lecture presents systematic models of the brain from the facets of cognitive informatics, abstract intelligence, brain Informatics, neuroinformatics, and cognitive psychology. A logical model of the brain is introduced that maps the cognitive functions of the brain onto its neural and physiological architectures. This work leads to a coherent abstract intelligence theory based on both denotational mathematical models and cognitive psychology observations, which rigorously explains the underpinning principles and mechanisms of the brain. On the basis of the abstract intelligence theories and the logical models of the brain, a comprehensive set of cognitive behaviors as identified in the Layered Reference Model of the Brain (LRMB) such as perception, inference and learning can be rigorously explained and simulated.The logical model of the brain and the abstract intelligence theory of natural intelligence will enable the development of cognitive computers that perceive, think and learn. The functional and theoretical difference between cognitive computers and classic computers are that the latter are data processors based on Boolean algebra and its logical counterparts; while the former are knowledge processors based on contemporary denotational mathematics. A wide range of applications of cognitive computers have been developing in ICIC and my laboratory such as, inter alia, cognitive robots, cognitive learning engines, cognitive Internet, cognitive agents, cognitive search engines, cognitive translators, cognitive control systems, and cognitive automobiles.

The Mindset of Cognitive Science.

In a case study of the growth of cognitive science, we analyzed the activities of the Cognitive Science Society with a particular emphasis on the multidisciplinary nature of the field. Analyses of departmental affiliations, training back‐grounds, research methodology, and paper citations suggest that the journal Cognitive Science and the Annual Meeting of the Cognitive Science Society are dominated by cognitive psychology and computer science, rather than being an equal division among the constituent disciplines of cognitive science. However, at many levels, a growing percentage of work was found to involve a conjunction of multiple disciplines, such that approximately 30–50% of recent work in the Cognitive Science Society is multidisciplinary. In a questionnaire study of cognitive scientists involved in collaborative research, multidisciplinarity was found to shape the research process and affect the factors associated with successful research.

In cognitive science, we are currently witness to a fundamental paradigm shift [for review see, e. g., Varela et al. (1991) and Bechtel and Abrahamsen (1991)]. In the early days of artificial intelligence and cognitive psychology, cognitive processes were assumed to be based on algorithmic computations controlled by formalizable rules which act upon quasi-propositional knowledge about the external world. In recent years, however, more and more cognitive scientists have converted to the idea that functions like perception, problemsolving, or memory emerge from complex interactions in highly distributed neuronal networks which, unlike conventional information-processing systems, are shaped by learning and experience-dependent plasticity. In such networks, information processing does not follow explicit rules, but is based on the self-organization of patterns of activity. This paradigm shift is motivated by the apparent inability of classical models to account for many facets of cognitive processes and, in addition, by their lack of biological plausibility. In the present contribution, we describe this paradigm shift as it occurs in the neurobiology of perception, with particular reference to the visual system. In addition to demonstrating crucial differences, we elaborate basic conceptual assumptions which are common to both the classical neurobiological view on perception and to the more recent connectionist framework.

Works endorsing transhumanist ideas often appeal to various scientific disciplines, including cognitive science. A key transhumanist concept, that of cognitive enhancement, is closely linked with research conducted in such fields of cognitive science as artificial intelligence, cognitive robotics and the evolution of cognitive systems. This provokes the following questions: Do the research results of cognitive science really support the transhumanist vision of the future of mankind? What methodological differences and similarities are there between transhumanism and cognitive science? Is the conception of mind (cognition, intelligence) emerging from contemporary research in cognitive science compatible with the notion of mind adopted by the transhumanists? This paper provides answers to these questions. In the first part, I present the main transhumanist ideas associated with research in cognitive science. In the second, I discuss the main currents (paradigms) of research in cognitive science, focusing on the problems that have to do with transhumanism.

Knowledge Engineering is generally known as the field that is responsible for the analysis and design of expert systems and is thus concerned with representing and implementing the expertise of a chosen application domain in a computer system. Research on cognition or cognitive science, on the other hand, is performed as a basic science, mostly within the disciplines of artificial intelligence, psychology and linguistics. It investigates the mental states and processes of humans by modelling them with a computer system and combining analytic and empirical viewpoints. Early on, knowledge acquisition was known as the activity of making explicit the human knowledge that is relevant for performing a task, so that it can be represented and become operational in an expert system. Knowledge acquisition and the field of knowledge engineering are consequently closely related to human cognition, which is studied in cognitive science. The specific relationship between knowledge engineering and cognitive science has changed over the years and therefore needs to be reconsidered in future expert system developments.

This article provides an overview of the main tendencies and ideas in the embodied mind paradigm in the expanding field of modern cognitive science. The focus is not on the biological and neurological aspects of cognitive science, rather the article demonstrates how basic concepts and theories from cognitive science have influenced linguistics, sociology, the understanding of art and creativity, film and film perception, as well as our understanding of historical film narratives and mediated memories. Although these areas of humanities and social science may seem unrelated, this article demonstrates how the embodied mind paradigm has actually forged links between separate scientific disciplines. Cognitive science and the embodied mind theory have created a stronger interdisciplinary connection between cognitive understanding in social science and humanities. Metaphors and image schema, the way our brain relies on narrative structures, the dynamic ability of the brain to blend old and new schemas, and the unparalleled creativity of the brain are all part of the approaches of the cognitive social science and humanities to social interaction, communication and creativity described here. The article also discusses the relationship between the more universal dimensions of the human mind and the question of cultural and social variations. The argument here is that a cognitive and more universal theory of human beings is not the same as determinism. On the contrary, when we understand our universal commonalities and the basic functions of our embodied mind we will also be better placed to understand cultural and social differences and variations.

Reviewed by: The analogical mind: Perspectives from cognitive science ed. by Dedre Gentner, Keith J. Holyoak, and Boicho N. Kokinov Rick DeNoble The analogical mind: Perspectives from cognitive science. Ed. by Dedre Gentner, Keith J. Holyoak, and Boicho N. Kokinov. Cambridge, MA: MIT Press, 2001. Pp. 541. ISBN 0262571390. $28.35. This book grew out of a 1998 summer workshop at the New Bulgarian University in Sofia, Bulgaria [End Page 456] called ‘Advances in Analogy Research: Integration of Theory and Data from the Cognitive, Computational, and Neural Sciences’. While the contributors come from a variety of disciplines, they share an interest in a common approach to cognitive studies. The book consists of an introduction by the volume editors, followed by thirteen chapters divided into three sections: ‘Computational and theoretical approaches’, ‘Arenas of analogical thought’, and ‘Developmental approaches’. An epilogue concludes the volume. The introduction provides a brief history of the development of the study of analogy within cognitive science, a very brief statement of the current framework for analogy studies, and an outline of the remainder of the book’s contents. Section 1 consists of four chapters based on actual running computational models of analogical thinking. These include: ‘Exploring analogy in the large’ (23–50) by Kenneth D. Forbus; ‘Integrating meaning and reasoning in analogy making: The AMBR model’ (54–124) by Boicho N. Kokinov and Alexander A. Petrov; ‘The STAR-2 model for mapping hierarchically structured analogs’ (125–60) by William H. Wilson, Graeme S. Halford, Brett Gray, and Steven Phillips; and ‘Toward an understanding of analogy within a biological symbol system’ (161–95) by Keith J. Holyoak and John E. Hummel. Section 2 contains seven chapters, which deal with the role analogy plays within a variety of complex cognitive processes or tasks. The first three deal especially with analogical processes in language. The chapters in Section 2 include: ‘Metaphor is like analogy’ (199–254) by Dedre Gentner, Brian Bowdle, Phillip Wolff, and Consuelo Boronet; ‘Conceptual blending and analogy’ (255–86) by Gilles Fauconnier; ‘Setting limits on analogy: Why conceptual combination is not structural alignment’ (287–312) by Mark T. Keane and Fintan Costello; ‘The analogical paradox: Why analogy is so easy in naturalistic settings yet so difficult in the psychological laboratory’ (313–34) by Kevin Dunbar; ‘Emotional analogies and analogical inference’ (335–62) by Paul Thagard and Cameron Shelley; ‘Analogy and analogical comparison in choice’ (363–400) by Arthur B. Markman and C. Page Moreau; and ‘Semantic alignments in mathematical word problems’ (401–33) by Miriam Basok. The last section contains two chapters: ‘Analogical reasoning in children’ (437–70) by Usha Goswami, and ‘Can an ape reason analogically? Comprehension and production of analogical problems by Sarah, a chimpanzee (Pan troglodytes)’ (471–97) by David L. Oden, Roger K. R. Thompson, and David Premack. The epilogue is written by Douglas Hofstadter, who claims in a supposedly unorthodox view that, rather than being a ‘blip in the broad blue sky’, analogy is actually ‘the very blue that fills the whole sky of cognition—analogy is everything, or very nearly so, in my view’ (499). The essays in the book go a long way toward making this claim the standard view of cognitive science. Rick DeNoble Boulder, CO Copyright © 2006 Linguistic Society of America