How Do Living Systems Create Meaning?

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.

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

Cognitive science is a network of interrelated scientific disciplines engaged in researching human cognition and its brain mechanisms. The birth of cognitive science has been the result of numerous integrated processes. Cognitive science is made up of experimental psychology cognition, philosophy consciousness, neuroscience, cognitive anthropology, linguistics, computer science and artificial intelligence. In recent years, a number of other research areas have been added to the body of cognitive science. Among researchers there have been discussions about whether cognitive science is a separate research area or it consists of a series of specialized areas. In fact, the point at issue is whether cognitive science is still a multidisciplinary project or already an interdisciplinary one. P. Thagard believes that cognitive science has reached the level of interdisciplinarity and explains the advances in this area through the metaphor of “trading zones”. The success elements of cognitive science are: fruitful unification of scientific interests of cognitive science founders; organizational structure of the scientific community – universities, where a special interdisciplinary intellectual environment has been created; a large number of joint research projects supported by governments and business; integrated use of scientific methods and fundamental ideas. D. Sperber and J. Miller prefer to talk not about a unified cognitive science but cognitive sciences, i.e., the commonwealth of sciences working together on the study of a single object - human cognition, however, the extent of their interactive communication is still small. Thus, we should speak about multidisciplinarity rather than genuine interdisciplinarity of the joint research of separate sciences.

Computer science (CS) is defined in wikipedia as a branch of human knowledge “relating to computation, ranging from abstract analysis of algorithms and formal grammars, to subjects like programming languages, software, and computer hardware” (Computer Science, 2005). Computer science emerged as a distinct field in the 1940s and 1950s with the development of the first electronic digital computers. To limit computer science to just computer use or its knowledge bodies, however, is reductive; CS is embedded in a complex, unquantifiable cultural context, including socio-economic and gendering practice. Computer hardware and software are designed to complement and supplement human activity and processes such as warfare, industrial applications, information management, including education, the Internet, a knowledge commons, and most recently biotechnology. Although CS is typically considered neutral and scientific, its episteme and practice is androcentric or male centered, often to the exclusion of females (Herbst, 2002). Female attributes have not typically been associated with computer science or computers. Although there is general agreement that women are as intellectually capable as men in CS, the fact remains that women today do not have equal participation in CS majors, CS engineering, programming, software design, Web site construction, or computer repair. (Jepson & Perl, 2002). In the technetronic 21st century, when computers are becoming standard for education and in symbolic analytic jobs, women’s enrollment in CS has declined, and many women do not feel confident using computers for more than e-mail transmissions, e-commerce, and social interaction in forums or newsgroups. Women who do not have knowledge or confidence in their abilities to work in CS not only have unrealized potentials in CS but also are left out of employment activities. Reasons for gendering in CS are complex and debated. Socialization, overt and tacit discrimination, and epistemological plurality are three dominant explanations. CS industries, educators, cognitive scientists, parents, and women professionals in CS are some of the groups currently working to attain gender equity in CS.

This paper discusses a three‐level model that synthesizes and unifies existing learning theories to model the roles of artificial intelligence (AI) in promoting learning processes. The model, drawn from developmental psychology, computational biology, instructional design, cognitive science, complexity and sociocultural theory, includes a causal learning mechanism that explains how learning occurs and works across micro, meso and macro levels. The model also explains how information gained through learning is aggregated, or brought together, as well as dissipated, or released and used within and across the levels. Fourteen roles for AI in education are proposed, aligned with the model's features: four roles at the individual or micro level, four roles at the meso level of teams and knowledge communities and six roles at the macro level of cultural historical activity. Implications for research and practice, evaluation criteria and a discussion of limitations are included. Armed with the proposed model, AI developers can focus their work with learning designers, researchers and practitioners to leverage the proposed roles to improve individual learning, team performance and building knowledge communities. Practitioner notes What is already known about this topic Numerous learning theories exist with significant cross‐over of concepts, duplication and redundancy in terms and structure that offer partial explanations of learning. Frameworks concerning learning have been offered from several disciplines such as psychology, biology and computer science but have rarely been integrated or unified. Rethinking learning theory for the age of artificial intelligence (AI) is needed to incorporate computational resources and capabilities into both theory and educational practices. What this paper adds A three‐level theory (ie, micro, meso and macro) of learning that synthesizes and unifies existing theories is proposed to enhance computational modelling and further develop the roles of AI in education. A causal model of learning is defined, drawing from developmental psychology, computational biology, instructional design, cognitive science and sociocultural theory, which explains how learning occurs and works across the levels. The model explains how information gained through learning is aggregated, or brought together, as well as dissipated, or released and used within and across the levels. Fourteen roles for AI in education are aligned with the model's features: four roles at the individual or micro level, four roles at the meso level of teams and knowledge communities and six roles at the macro level of cultural historical activity. Implications for practice and policy Researchers may benefit from referring to the new theory to situate their work as part of a larger context of the evolution and complexity of individual and organizational learning and learning systems. Mechanisms newly discovered and explained by future researchers may be better understood as contributions to a common framework unifying the scientific understanding of learning theory.

This volume is unique in its focus on cognitive informatics (CI), a flourishing discipline that cuts across several academic and professional sectors. The chapters in this volume focus on motivating examples drawn from the application of methods and theories from CI to challenges pertaining to the practice of critical-care medicine. Informatics is a discipline concerned with the basic and applied science of information, the practices involved in information processing, and the engineering of information systems. Cognitive Informatics is the multidisciplinary study of cognition, information and computational sciences that investigates all facets of human computing, including design and computer-mediated intelligent action [1]. The basic scientific discipline of CI is strongly grounded in the methods and theories of cognitive science. As an applied discipline, it also draws on the methods and theories from human factors and human-computer interaction. The healthcare domain has provided significant challenges and a fertile test bed for theories from these disciplines. CI provides a framework for the analysis and modeling of complex human performance in technology-mediated settings and contributes to the design and development of better information systems.

The resurgence of interest in the cognitive sciences since the early 1970s has been accompanied by a dramatic acceleration in the development of theoretical models. Cognitive scientists develop models in order to characterize the formal properties of the processes that they are studying. Efforts to model cognition led to a recent focus on “neural networks,” computer-based computational systems that simulate the characteristics of actual neural brain processes. Neural networks are designed by means of computational modeling, which is a direct result of advances in the fields of mathematical psychology and computer science. Computational models are created with the hope that they will provide a formal mathematical basis for understanding cognitive and behavioral processes. These models seek to specify the parameters underlying information processing and therefore are of considerable interest to investigators of attentional phenomena. Although at times these models seem to do little more than provide a mathematical description of processes that are typically characterized less formally, they may be useful for identifying the constraints and parameters underlying attentional operations.KeywordsStatistical Learning TheoryOrient ResponseManual TrackingCompetitive LearningAdaptive Resonance TheoryThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The present paper examines the historical choice points the led twentieth-century cognitive science to its current commitment to correspondence approaches to reality and truth. Such a “correspondence”-driven approach to reality and truth stands in contrast to coherence-driven approaches, which were prominent in the 1800s and early 1900s. Coherence approaches refused to begin the conversation regarding reality with the assumption that the important thing about it was its independence of observers because the reality-observer split inherent in correspondence-driven views often led to objective-subjective divides, which, within scientific theorizing, tended to render the latter causally unnecessary and in need of ontological justification. The present paper fleshes out the differences between coherence- and correspondence-driven approaches to reality and truth, proposes an explanation of why cognitive science came to favor correspondence approaches, describes problems that have arisen in cognitive science because of its commitment to correspondence theorizing, and proposes an alternative framework (i.e., Wild Systems Theory— WST ) that is inspired by a coherence approach to reality and truth, yet is entirely consistent with science.

From the Publisher: Biomedicine has become one of the best-modeled domains from several perspectives - artificial intelligence, psychology, and the social sciences; yet few studies have combined these points of view. In this book, the interdisciplinary strengths of cognitive science offer fresh insights into biomedical problem solving. Cognitive Science in Medicine presents current research that focuses on issues and results in applying techniques from cognitive science to problems in biomedicine. It includes material by researchers who have worked in both areas and is unique in linking models of physician knowledge with models of physician behavior. David Evans discusses issues of cognitive science in medicine in his introduction; and in a chapter with Cindy Gadd and Harry Pople, deals with the problem of managing coherence and context in medical problem-solving discourse. Vimla Patel, Evans, and Guy Groen provide experimental data that illuminates the role of biomedical knowledge in clinical reasoning; and Patel, Evans, and David Kaufman offer a cognitive science framework for analysis of clinical interviews. Other contributors and subjects include Clark Glymour on the empirical and representational issues in cognitive and medical science; Alan Lesgold on multilevel models of expertise; Arthur Elstein, James Dodd, and Gerald B. Holzman on the analysis of estrogen replacement decisions among residents; Kenneth R. Hammond, Elizabeth Frederick, Nichole Robillard, and Doreen Victor on the features of the student-teacher dialog in medicine; Naomi Rodolitz and William J. Clancey on tutoring for strategic knowledge; Paul J. Feltovich, Rand J. Spiro, and Richard L. Coulson on the foundationsof misunderstanding in established medical knowledge; John K. Vries, Evans, and Peretz Shoval on the development of semantic networks for medical information retrieval; and John Bruer, with a preface on the implications of cognitive-scientific studies for medical education. David A. Evans is Assistant Professor of Linguistics and Computer Science at Carnegie-Mellon University and Vimla L. Patel is Associate Professor of Medicine and Educational Psychology at McGill University. A Bradford Book.

526 BOOK REVIEWS learned how to integrate satisfaction into love (91, 113, 119, 124, 136, 186, 192, 198; cp. 37). Indeed, it is Aquinas's gradual integration of satisfaction as a motive for the Incarnation subordinate to love (166) that enables Aquinas aptly to locate satisfaction within the Christian life (cp. 47, 136, 142, 166) and accounts for Cessario's subtitle. Third, I am not clear on Ccssario's (or my own!) stand on how we ought sort out the respects in which Thomas's and our perspectives merge (cp. xiv). In his conclusion Cessario is cautious, suggesting that "if the term ' satisfaction ' cannot be restored to current usage, then certainly the substance of Saint Thomas's understanding of satisfaction can and should be." (256). But, given Cessario's own persuasive display of the way satisfactio pervades the very texture of Aquinas's theology, I am not sure we can have the "substance" without the term-or vice-versa. Clearly Cessario thinks the merging is substantive. But he himself teaches us that supporting this claim involves supporting claims for exegetical, liturgical ecclesial, and other practices. Those of us who think that satisfactio is a description rather than "the guiding model or key notion" (xx, my emphasis) for thinking Christ crucified will need to see such support worked out in relation to the competing sotcriologies Cessario mentions (267) as well as ecumenical discussions of justification which were understandably outside the scope of this book (151). But a single book can only accomplish a single thing-and this book clearly succeeds in showing us how Thomas can be brought to bear on contemporary theological issues. All students of Aquinas and/or soteriology ought to read this text. Loyola College in Maryland Baltimore, Maryland JAMES J. BUCKLEY How We Know. Edited by MICHAEr, SHAFTO. San 1!1rancisco: Harper & Row. 1985. Pp. xv and 171. $14.95. How We Know is a collection of six papers delivered in 1984 at the twentieth Nobel Conference, held at Gustavus Adolphus College in St. Peter, Minnesota. Two words that came to my mind as I worked through the papers were "fascinating" and "frightening". The Conference dealt with cognitive science and a number of the authors have come up with interesting, provocative, and often orig'inal observations about how the human mind operates when it hies to remember and to understand. In his Introduction Michael Shafto, who is a Scientific Officer in the Personnel and Training Research Programs of the United States Office of Naval Research, notes that cognitive science as the work of a coalition of thinkers is barely a decade old, and he reminds the reader that the methods BOOK REVIEWS 59.7 of cognitive science are those of experimental psychology, physiology, and computer science. Though the Confermce was supposedly aimed at nonspecialists , the reader unfamiliar with cognitive science might find some of the papers a bit difficult. Certainly the backgrounds of the authors are very impressive. The first four papers in the collection are by Gerald Edelman, Brenda Milner, Roger C. Schank, and Herbert A. Simon respectively. Edelman is the Director and Scientific Chairman of the Neurosciences Research Program at Rockefeller University; Milner is Professor of Psychology in the Department of Neurology at McGill University: Schank is Professor and Chairman of the Computer Science Department at Yale; and Simon is Professor of Computer Science and Psychology at Carnegie-Mellon University . Calling his theory of the hum:m brain "Neural Darwinism," Edelman claims that the brain is Darwinian. Taking Darwin's theory of natural selections and applying it to the brain, Edelman argues that during ontogeny and behavior those groups of neurons are chosen that are adaptive for the organism. Milner explores the neurophysiological basis of that special moment when a new experience affects the brain. Insisting that memory is not merely a passive reproi!ucer but rather actively selective, Milner, relying on her studies of patients. with loss of memory, suggests that the frontal lobes of the brain hnve a special role in memory. Interested in the interface of mcmory anil percr-ption, Schank notes that, because human memory is dynamic and the memory of the computer is not, the -programming of the intelligent functions of...

Experts from diverse fields, including artificial life, cognitive science, economics, developmental and evolutionary biology, and the arts, discuss modularity. Modularity—the attempt to understand systems as integrations of partially independent and interacting units—is today a dominant theme in the life sciences, cognitive science, and computer science. The concept goes back at least implicitly to the Scientific (or Copernican) Revolution, and can be found behind later theories of phrenology, physiology, and genetics; moreover, art, engineering, and mathematics rely on modular design principles. This collection broadens the scientific discussion of modularity by bringing together experts from a variety of disciplines, including artificial life, cognitive science, economics, evolutionary computation, developmental and evolutionary biology, linguistics, mathematics, morphology, paleontology, physics, theoretical chemistry, philosophy, and the arts. The contributors debate and compare the uses of modularity, discussing the different disciplinary contexts of "modular thinking" in general (including hierarchical organization, near-decomposability, quasi-independence, and recursion) or of more specialized concepts (including character complex, gene family, encapsulation, and mosaic evolution); what modules are, why and how they develop and evolve, and the implication for the research agenda in the disciplines involved; and how to bring about useful cross-disciplinary knowledge transfer on the topic. The book includes a foreword by the late Herbert A. Simon addressing the role of near-decomposability in understanding complex systems.

I want to begin by emphasizing that the definite article in my title should not be taken to imply that cognitive analyses are unique—there is no single view that could be called the view of cognitive science on scientific controversies or, I suspect, of anything else of interest (Giere 1992). In fact, one should be suspicious even of the claim that there are cognitive scientists, despite a number of reported sightings and self-reports. Very few people have been trained in a cognitive science program—most were trained as philosophers, psychologists, linguists, computer scientists, or some other field and then developed an interest and competence at cognitive science. There is even a serious and often heated debate about whether cognitive science(s) is singular or plural.

Effects of class size on alternative educational outcomes across disciplines

The present essay aims at integrating different concepts of meaning developed in semiotics, biology, and cognitive science, in a way that permits the formulation of issues involving evolution and development. The concept of sign in semiotics, just like the notion of representation in cognitive science, have either been used too broadly, or outright rejected. My earlier work on the notions of iconicity and pictoriality has forced me to spell out the taken-forgranted meaning of the sign concept, both in the Saussurean and the Peircean tradition. My work with the evolution and development of semiotic resources such as language, gesture, and pictures has proved the need of having recourse to a more specified concept of sign. To define the sign, I take as point of departure the notion of semiotic function (by Piaget), and the notion of appresentation (by Husserl). In the first part of this essay, I compare cognitive science and semiotics, in particular as far as the parallel concepts of representation and sign are concerned. The second part is concerned with what is probably the most important attempt to integrate cognitive science and semiotics that has been presented so far, The Symbolic Species, by Terrence Deacon. I criticize Deacon’s use of notions such as iconicity, indexicality, and symbolicity. I choose to separate the sign concept from the notions of iconicity, indexicality, and symbolicity, which only in combination with the sign give rise to icons, indices, and symbols, but which, beyond that, have other, more elemental, uses in the world of perception. In the third part, I discuss some ideas about meaning in biosemiotics, which I show not to involve signs in the sense characterised earlier in the essay. Instead, they use meaning in the general sense of selection and organisation, which is a more elementary sense of meaning. Although I admit that there is a possible interpretation of Peirce, which could be taken to correspond to Uexküll’s idea of functional circle, and to meaning as function described by Emmeche and Hoffmeyer, I claim that this is a different sense of meaning than the one embodied in the sign concept. Finally, I suggest that more thresholds of meaning than proposed, for instance by Kull, are necessary to accommodate the differences between meaning (in the broad sense) and sign (as specified in the Piaget–Husserl tradition).

Women and computing