Cognitive science is the project of understanding the mind by modelling its workings. Its development is one of the most remarkable and fascinating intellectual achievements of the modern era. Mind as Machine is a masterful history of cognitive science, told by one of its most eminent practitioners.

Review of: Margaret A. Boden, Mind as Machine: A History of Cognitive Science, 2 vols, Oxford: Oxford University Press, 2006, xlvii+1631, cloth $225, ISBN 0-19-924144-9. - Mind as Machine is Margaret Boden’s opus magnum. For one thing, it comes in two massive volumes of nearly 1700 pages, ... But it is not just the opus magnum in simple terms of size, but also a truly crowning achievement of half a century’s career in cognitive science.

History of Cognitive Neuroscience documents the major neuroscientific experiments and theories over the last century and a half in the domain of cognitive neuroscience, and evaluates the cogency of the conclusions that have been drawn from them. Provides a companion work to the highly acclaimed Philosophical Foundations of Neuroscience – combining scientific detail with philosophical insights Views the evolution of brain science through the lens of its principal figures and experiments Addresses philosophical criticism of Bennett and (...) Hacker?s previous book Accompanied by more than 100 illustrations. (shrink)

An examination of the fundamental role cybernetics played in the birth of cognitive science and the light this sheds on current controversies. The conceptual history of cognitive science remains for the most part unwritten. In this groundbreaking book, Jean-Pierre Dupuy—one of the principal architects of cognitive science in France—provides an important chapter: the legacy of cybernetics. Contrary to popular belief, Dupuy argues, cybernetics represented not the anthropomorphization of the machine but the mechanization of (...) the human. The founding fathers of cybernetics—some of the greatest minds of the twentieth century, including John von Neumann, Norbert Wiener, Warren McCulloch, and Walter Pitts—intended to construct a materialist and mechanistic science of mental behavior that would make it possible at last to resolve the ancient philosophical problem of mind and matter. The importance of cybernetics to cognitive science, Dupuy argues, lies not in its daring conception of the human mind in terms of the functioning of a machine but in the way the strengths and weaknesses of the cybernetics approach can illuminate controversies that rage today—between cognitivists and connectionists, eliminative materialists and Wittgensteinians, functionalists and anti-reductionists. Dupuy brings to life the intellectual excitement that attended the birth of cognitive science sixty years ago. He separates the promise of cybernetic ideas from the disappointment that followed as cybernetics was rejected and consigned to intellectual oblivion. The mechanization of the mind has reemerged today as an all-encompassing paradigm in the convergence of nanotechnology, biotechnology, information technology, and cognitive science. The tensions, contradictions, paradoxes, and confusions Dupuy discerns in cybernetics offer a cautionary tale for future developments in cognitive science. (shrink)

It's an exciting story, no doubt, and I hate to be a spoilsport. But there are a few problems. One is that virtually every reference to me or to co-workers around the world, and to the areas in which we work, is fanciful, sometimes even bringing to mind Pauli 's famous observation "not even wrong." I'll review what seems to be a fair sample.

The first full-scale history of cognitive science, this work addresses a central issue: What is the nature of knowledge?

The conceptual history of cognitive science remains for the most part unwritten. In this groundbreaking book, Jean-Pierre Dupuy--one of the principal architects of cognitive science in France--provides an important chapter: the legacy of cybernetics. Contrary to popular belief, Dupuy argues, cybernetics represented not the anthropomorphization of the machine but the mechanization of the human. The founding fathers of cybernetics--some of the greatest minds of the twentieth century, including John von Neumann, Norbert Wiener, Warren McCulloch, and (...) Walter Pitts--intended to construct a materialist and mechanistic science of mental behavior that would make it possible at last to resolve the ancient philosophical problem of mind and matter. The importance of cybernetics to cognitive science, Dupuy argues, lies not in its daring conception of the human mind in terms of the functioning of a machine but in the way the strengths and weaknesses of the cybernetics approach can illuminate controversies that rage today--between cognitivists and connectionists, eliminative materialists and Wittgensteinians, functionalists and anti-reductionists. Dupuy brings to life the intellectual excitement that attended the birth of cognitive science sixty years ago. He separates the promise of cybernetic ideas from the disappointment that followed as cybernetics was rejected and consigned to intellectual oblivion. The mechanization of the mind has reemerged today as an all-encompassing paradigm in the convergence of nanotechnology, biotechnology, information technology, and cognitive science. The tensions, contradictions, paradoxes, and confusions Dupuy discerns in cybernetics offer a cautionary tale for future developments in cognitive science. (shrink)

Cognitive science is a cross-disciplinary enterprise devoted to understanding the nature of the mind. In recent years, investigators in philosophy, psychology, the neurosciences, artificial intelligence, and a host of other disciplines have come to appreciate how much they can learn from one another about the various dimensions of cognition. The result has been the emergence of one of the most exciting and fruitful areas of inter-disciplinary research in the history of science. This volume of original essays (...) surveys foundational, theoretical, and philosophical issues across the discipline, and introduces the foundations of cognitive science, the principal areas of research, and the major research programs. With a focus on broad philosophical themes rather than detailed technical issues, the volume will be valuable not only to cognitive scientists and philosophers of cognitive science, but also to those in other disciplines looking for an authoritative and up-to-date introduction to the field. (shrink)

In recent decades cognitive science has revolutionised our understanding of the workings of the human mind. Philosophy has made a major contribution to cognitive science and has itself been hugely influenced by its development. This dynamic book explores the philosophical significance of cognitive science and examines the central debates that have enlivened its history. In a wide-ranging and comprehensive account of the topic, philosopher M.J. Cain discusses the historical origins of cognitive (...) class='Hi'>science and its philosophical underpinnings; the nature and role of representations in cognition; the architecture of the mind and the modularity thesis; the nature of concepts; knowledge of language and its acquisition; perception; and the relationship between the brain and cognition. Cain draws upon an extensive knowledge of empirical developments and their philosophical interpretation. He argues that although the field has generated some challenging new views in recent years, many of the core ideas that initiated its birth are still to be taken seriously. Clearly written and incisively argued, The Philosophy of Cognitive Science will appeal to any student or researcher interested in the workings of the mind. (shrink)

_Abstract_: We imagine the future of cognitive science by first considering its past, which shows remarkable transformation from a field that, although interdisciplinary, was initially marked by a narrow set of assumptions concerning its subject matter. In the last decades, multiple alternative frameworks with radically different ontological and epistemic commitments (e.g., situated cognition, embodied cognition, extended mind) found broad support. We address the question of how to understand these changes, noting as logical alternatives that (1) newer approaches are (...) not properly _cognitive_; (2) that newer approaches are cognitive but not _science_; and (3) that cognitive science has become pluralistic. We endorse the third position and venture to guess that the future of cognitive science is also pluralistic. We are left, however, with the question of what this _means. _After noting the polysemous nature of the term “pluralism”, we attempt to add clarity by distinguishing three forms: ontological, epistemic, and ethical. We then consider what each form might imply for the future of cognitive science. _Keywords_: Cognitive Science; Pluralism; Relativism; History of Science; Philosophy of Science _Il futuro della scienza cognitiva è pluralista, ma che vuol dire?_ _Riassunto_: Pensiamo al futuro della scienza cognitiva in primo luogo considerando il suo passato, il quale è notevolmente cambiato rispetto al presente. Per quanto si proponesse come un campo di studi interdisciplinari, la scienza cognitiva delle origini era caratterizzata da un insieme ristretto di assunzioni riguardanti il proprio oggetto. Negli ultimi decenni hanno trovato supporto diverse cornici teoriche in reciproca competizione e con impegni ontologici ed epistemologici radicalmente differenti (si pensi, per esempio, alla cognizione situata, alla cognizione incarnata, alla mente estesa). Proveremo a dare risposta alla domanda su come intendere questi cambiamenti, prendendo atto che ci troviamo di fronte a una serie di posizioni che sono logicamente alternative fra loro: (1) gli approcci più recenti non sono propriamente _cognitivi_; (2) oppure che gli approcci più recenti sono cognitivi, ma non _scientifici_; (3) la scienza cognitiva è diventata pluralista. Noi crediamo che la terza posizione sia corretta e scommettiamo su un futuro della scienza cognitiva che sia anche pluralista. Resta aperto, tuttavia, il problema di cosa questo _significhi_. Dopo aver preso atto della natura polisemica del termine “pluralismo”, cercheremo di far chiarezza distinguendo tre forme di pluralismo: ontologico, epistemico, ed etico. Considereremo quindi ciò che ciascuna può comportare per il futuro della scienza cognitiva. _Parole chiave_: Scienza cognitiva; Pluralismo; Relativismo; Storia della scienza; Filosofia della scienza. (shrink)

Within contemporary science, it is common practice to compare data points to the average, i.e., to the statistical mean. Because this practice is so familiar, it might at first appear not to be the sort of thing that requires explanation. But recent research in cognitive science and in the history of science gives us reason to adopt the opposite perspective. Cognitive science research on the ways people ordinarily make sense of the world suggests (...) that, instead of using a purely statistical notion of the average, people tend to use a value-laden notion of the normal. Similarly, historical research on the scientific practices used in other time periods indicates that, prior to the nineteenth century, scientists tended to make use of a notion of the normal that was overtly value-laden. These findings give us reason to rethink certain familiar facts about scientific practice. In particular, they suggest that the fact that scientists so often make use of the statistical average should be seen as a highly surprising fact, the sort of thing that calls out for explanation. (shrink)

[from the publisher's website] Questions about the existence and attributes of God form the subject matter of natural theology, which seeks to gain knowledge of the divine by relying on reason and experience of the world. Arguments in natural theology rely largely on intuitions and inferences that seem natural to us, occurring spontaneously—at the sight of a beautiful landscape, perhaps, or in wonderment at the complexity of the cosmos—even to a nonphilosopher. In this book, Helen De Cruz and Johan De (...) Smedt examine the cognitive origins of arguments in natural theology. They find that although natural theological arguments can be very sophisticated, they are rooted in everyday intuitions about purpose, causation, agency, and morality. Using evidence and theories from disciplines including the cognitive science of religion, evolutionary ethics, evolutionary aesthetics, and the cognitive science of testimony, they show that these intuitions emerge early in development and are a stable part of human cognition. -/- De Cruz and De Smedt analyze the cognitive underpinnings of five well-known arguments for the existence of God: the argument from design, the cosmological argument, the moral argument, the argument from beauty, and the argument from miracles. Finally, they consider whether the cognitive origins of these natural theological arguments should affect their rationality. (shrink)

This chapter describes the conceptual foundations of cognitive science during its establishment as a science in the 20th century. It is organized around the core ideas of individual agency as its basic explanans and information-processing as its basic explanandum. The latter consists of a package of ideas that provide a mathematico-engineering framework for the philosophical theory of materialism.

The paper argues that it is the rise of Artificial Intelligence as a concrete possibility (the idea of a thinking machine) that favoured the growth of interest in Cognitive Science within the academic community. This puts the history and the definition of AI in a different light, as it can be seen and understood as "a continuation of Philosophy by other means" (Landi, 2020) and not merely as a technology or a sum of technologies.

This article critically examines the views that psychology first came into existence as a discipline ca. 1879, that philosophy and psychology were estranged in the ensuing decades, that psychology finally became scientific through the influence of logical empiricism, and that it should now disappear in favor of cognitive science and neuroscience. It argues that psychology had a natural philosophical phase (from antiquity) that waxed in the seventeenth and eighteenth centuries, that this psychology transformed into experimental psychology ca. 1900, (...) that philosophers and psychologists collaboratively discussed the subject matter and methods of psychology in the first two decades of the twentieth century, that the neobehaviorists were not substantively influenced by the Vienna Circle, that the study of perception and cognition in psychology did not disappear in the behaviorist period and so did not reemerge as a result of artificial intelligence, linguistics, and the computer analogy, that although some psychologists adopted the language-of-thought approach of traditional cognitive science, many did not, and that psychology will not go away because it contributes independently of cognitive science and neuroscience. (shrink)

The Relevance of Cognitive Science to Teaching, Proceedings of the 6th International History, Philosophy & Science Teaching Conference (IHPST), Denver, Colorado, November 7-10, 2001. (PDF).

Neuroethology is a branch of biology that studies the neural basis of naturally occurring animal behavior. This science, particularly a recent program called computational neuroethology, has a similar structure to the interdisciplinary endeavor of cognitive science. I argue that it would be fruitful to conceive of cognitive science as the computational neuroethology of humans. However, there are important differences between the two sciences, including the fact that neuroethology is much more comparative in its perspective. Neuroethology (...) is a biological science and as such, evolution is a central notion. Its target organisms are studied in the context of their evolutionary history. The central goal of this paper is to argue that cognitive science can and ought to be more comparative in its approach to cognitive phenomena in humans. I show how the domain of cognitive phenomena can be divided up into four different classes, individuated by the relative phylogenetic uniqueness of the behavior. I then describe how comparative evidence can enrich our understanding in each of these different arenas. (shrink)

Review of THEO C. MEYERING, Historical Roots of Cognitive Science : The Rise of a Cognitive Theory of Perception from Antiquity to the Nineteenth Century. Boston: Kluwer, xix + 250 pp. $69.00. Examines the author's interpretation of Aristotelian theories of perceptual cognition, early modern theories, and Helmholtz's theory.

Cognitive science is, of course, not really a new discipline, but a recognition of a fundamental set of common concerns shared by the disciplines of psychology, computer science, linguistics, economics, epistemology, and the social sciences generally. All of these disciplines are concerned with information processing systems, and all of them are concerned with systems that are adaptive—that are what they are from being ground between the nether millstone of their physiology or hardware, as the case may be, (...) and the upper millstone of a complex environment in which they exist. Systems that are adaptive may equally well be described as “artificial,” for as environments change, they can be expected to change too, as though they were deliberately designed to fit those environments (as indeed they sometimes are).The task of empirical science is to discover and verify invariants in the phenomena under study. The artificiality of information processing systems creates a subtle problem in defining empirical invariants in such systems. For observed regularities are very likely invariant only within a limited range of variation in their environments, and any accurate statement of the laws of such systems must contain reference to their relativity to environmental features. It is a common experience in experimental psychology, for example, to discover that we are studying sociology—the effects of the past histories of our subjects—when we think we are studying physiology—the effects of properties of the human nervous system. Similarly, business cycle economists are only now becoming aware of the extent to which the parameters of the system they are studying are dependent on the experiences of a population with economic events over the previous generation.In artificial sciences, the descriptive and the normative are never far apart. Thus, in economics, the “principle of rationality” is sometimes asserted as a descriptive invariant, sometimes as advice to decision makers. Similarly, in psychology, the processes of adaptation (learning) have always been a central topic, at one time a topic that dominated the whole field of research. Linguistics, too, has suffered its confusions between descriptive and normative attitudes towards its subject. But we must avoid the error, in studying information processing systems, of thinking that the adaptive processes themselves must be invariant; and we must be prepared to face the complexities of regression in the possibility that they themselves may be subject to improvement and adaptation.It might have been necessary a decade ago to argue for the commonality of the information processes that are employed by such disparate systems as computers and human nervous systems. The evidence for that commonality is now overwhelming, and the remaining questions about the boundaries of cognitive science have more to do with whether there also exist nontrivial commonalities with information processing in genetic systems than with whether men and machines both think. (shrink)

Cognitive science is, of course, not really a new discipline, but a recognition of a fundamental set of common concerns shared by the disciplines of psychology, computer science, linguistics, economics, epistemology, and the social sciences generally. All of these disciplines are concerned with information processing systems, and all of them are concerned with systems that are adaptive—that are what they are from being ground between the nether millstone of their physiology or hardware, as the case may be, (...) and the upper millstone of a complex environment in which they exist. Systems that are adaptive may equally well be described as “artificial,” for as environments change, they can be expected to change too, as though they were deliberately designed to fit those environments (as indeed they sometimes are).The task of empirical science is to discover and verify invariants in the phenomena under study. The artificiality of information processing systems creates a subtle problem in defining empirical invariants in such systems. For observed regularities are very likely invariant only within a limited range of variation in their environments, and any accurate statement of the laws of such systems must contain reference to their relativity to environmental features. It is a common experience in experimental psychology, for example, to discover that we are studying sociology—the effects of the past histories of our subjects—when we think we are studying physiology—the effects of properties of the human nervous system. Similarly, business cycle economists are only now becoming aware of the extent to which the parameters of the system they are studying are dependent on the experiences of a population with economic events over the previous generation.In artificial sciences, the descriptive and the normative are never far apart. Thus, in economics, the “principle of rationality” is sometimes asserted as a descriptive invariant, sometimes as advice to decision makers. Similarly, in psychology, the processes of adaptation (learning) have always been a central topic, at one time a topic that dominated the whole field of research. Linguistics, too, has suffered its confusions between descriptive and normative attitudes towards its subject. But we must avoid the error, in studying information processing systems, of thinking that the adaptive processes themselves must be invariant; and we must be prepared to face the complexities of regression in the possibility that they themselves may be subject to improvement and adaptation.It might have been necessary a decade ago to argue for the commonality of the information processes that are employed by such disparate systems as computers and human nervous systems. The evidence for that commonality is now overwhelming, and the remaining questions about the boundaries of cognitive science have more to do with whether there also exist nontrivial commonalities with information processing in genetic systems than with whether men and machines both think. (shrink)

Credences are similar to levels of confidence, represented as a value on the [0,1] interval. This chapter sheds light on questions about credence, including its relationship to full belief, with an eye toward the empirical relevance of credence. First, I’ll provide a brief epistemological history of credence and lay out some of the main theories of the nature of credence. Then, I’ll provide an overview of the main views on how credences relate to full beliefs. Finally, I’ll turn to (...) the empirical, and suggest some ways that cognitive science does and could answer questions about credence, such as what credences one has, how credences relate to beliefs, and the extent to which we can control our credences. (shrink)

Núñez et al (2019) argue (1) that the field of Cognitive Science has failed, in that it has not arrived at a cohesive theory, and (2) that this is contrary to the intentions of the founders. Their survey of publication and citation patterns bears out the lack of a cohesive theory and also provides corroboration for (3) the concern that the field is becoming unbalanced, with psychology overweighted (Gentner, 2010). I will argue against points (1) and (2), but (...) agree with point (3). My central claim is that cognitive science was never meant to have one unified theoretical framework, nor should it have. (shrink)

This article examines how experimental psychology experienced a revolution as cognitive science replaced behaviorism in the mid-20th century. This transition in the scientific account of human nature involved making normal what had once been normative: borrowing ideas of democratic thinking from political culture and conceptions of good thinking from philosophy of science to describe humans as active, creatively thinking beings, rather than as organisms that simply respond to environmental conditions. Reflexive social and intellectual practices were central to (...) this process as cognitive scientists used anti-positivist philosophy of science simultaneously to justify their own work as valid and also as a model of human thinking. In the process, the normative philosophy of science or ‘good academic thinking’ that cognitive scientists used to reshape the discipline of psychology and characterize themselves became, at the same time, the descriptive model of human nature. (shrink)

Purpose: This paper is a brief introduction to enactive cognitive science: a description of some of the main research concerns; some examples of how such concerns have been realized in actual research; some of its research methods and proposed explanatory mechanisms and models; some of the potential as both a theoretical and applied science; and several of the major open research questions. Findings: Enactive cognitive science is an approach to the study of mind that seeks (...) to explain how the structures and mechanisms of autonomous cognitive systems can arise and participate in the generation and maintenance of viable perceiver-dependent worlds -- rather than more conventional cognitivist efforts, such as the attempt to explain cognition in terms of the ``recovery'' of (pre-given, timeless) features of The (objectively-existing and accessible) World. As such, enactive cognitive science is resonant with radical constructivism. Research implications: As with other scientific efforts conducted within a constructivist orientation, enactive cognitive science is broadly ``conventional'' in its scientific methodology. That is, there is a strong emphasis on testable hypotheses, empirical observation, supportable mechanisms and models, rigorous experimental methods, acceptable criteria of validation, and the like. Nonetheless, this approach to cognitive science does also raise a number of specific questions about the scope of amenable phenomena (e.g., meaning, consciousness, etc.) -- and it also raises questions of whether such a perspective requires an expansion of what is typically considered within the purview of scientific method (e.g., the role of the observer/scientist). (shrink)

Hume was a cognitive scientist of religion avant la lettre. His Natural History of Religion (1757 [2007]) locates the origins of religion in human nature. This paper explores similarities between some of his ideas and the cognitive science of religion, the multidisciplinary study of the psychological origins of religious beliefs. It also considers Hume’s distinction between two questions about religion: its foundation in reason (the domain of natural theology and philosophy of religion) and its origin in (...) human nature (the domain of cognitive science of religion). (shrink)

Cognitive science is an interdisciplinary research endeavor focusing on human cognitive phenomena such as memory, language use, and reasoning. It emerged in the second half of the 20th century and is charting new directions at the beginning of the 21st century. This chapter begins by identifying the disciplines that contribute to cognitive science and reviewing the history of the interdisciplinary engagements that characterize it. The second section examines the role that mechanistic explanation plays in (...) cognitive science, while the third focuses on the importance of mental representations in specifically cognitive explanations. The fourth section considers the interdisciplinary nature of cognitive science and explores how multiple disciplines can contribute to explanations that exceed what any single discipline might accomplish. The conclusion sketches some recent developments in cognitive science and their implications for philosophers. (shrink)

Human behavior and thought often exhibit a familiar pattern of within group similarity and between group difference. Many of these patterns are attributed to cultural differences. For much of the history of its investigation into behavior and thought, however, cognitive science has been disproportionately focused on uncovering and explaining the more universal features of human minds—or the universal features of minds in general. -/- This entry charts out the ways in which this has changed over recent decades. (...) It sketches the motivation behind the cultural turn in cognitive science, and situates some of its central findings with respect to the questions that animate it and the debates that it has inspired. Woven throughout the entry are examples of how the cognitive science of culture, and especially its elevated concern with different forms of diversity and variation, continues to influence and be influenced by philosophers. -/- One cluster of philosophical work falls within the traditional subject matter of philosophy of science, in this case of the cognitive and social sciences. Philosophers have analyzed and assessed the methods and evidence central to the scientific study of cognition and culture, and have offered conceptual scrutiny, clarification, and synthesis. Research in a second vein sees philosophers themselves contributing more directly to cognitive scientific projects, (co)constructing theories, helping build computational models, even gathering empirical data. A third kind of work is naturalistic philosophy or philosophy of nature, wherein philosophers seek to use results from the cognitive science of culture to inform or transform debates over long-standing philosophical questions, including questions about the nature of philosophy and philosophical methodology itself. (shrink)

This book consists of an edited collection of original essays of the highest academic quality by seasoned experts in their fields of cognitive science. The essays are interdisciplinary, drawing from many of the fields known collectively as “the cognitive sciences.” Topics discussed represent a significant cross-section of the most current and interesting issues in cognitive science. Specific topics include matters regarding machine learning and cognitive architecture, the nature of cognitive content, the relationship of (...) information to cognition, the role of language and communication in cognition, the nature of embodied cognition, selective topics in visual cognition, brain connectivity, computation and simulation, social and technological issues within the cognitive sciences, and significant issues in the history of neuroscience. This book will be of interest to both professional researchers and newer students and graduate students in the fields of cognitive science—including computer science, linguistics, philosophy, psychology and neuroscience. The essays are in English and are designed to be as free as possible of technical jargon and therefore accessible to young scholars and to scholars who are new to the cognitive neurosciences. In addition to several entries by single authors, the book contains several interesting roundtables where researchers contribute answers to a central question presented to those in the focus group on one of the core areas listed above. This exciting approach provides a variety of perspectives from across disciplines on topics of current concern in the cognitive sciences. (shrink)

This paper reviews 30 years of progress in U.S. cognitive science research related to education and training, as seen from the perspective of a research manager who was personally involved in many of these developments.

For all of recorded history prior to the second half of the twentieth century, there has been but one realm in which the cognitive processes of reasoning and problem solving, learning and discovery, language and mathematics took place. The realm of human intellect no longer has an exclusive claim on these cognitive processes--artificial intelligence represents a parallel claim. Wagman compares the two realms, focusing on each of the major components of cognition: logic, reasoning, problem-solving, language, memory, learning, (...) and discovery. He identifies consonant and disparate modes of cognition, and develops a general theory of human and artificial intelligence. (shrink)

Much has been written on witchcraft by historians, theologians, philosophers, and anthropologists, but nothing by scientists. This book aims to reappraise witchcraft by applying to it the advances in cognitive sciences. The book is divided into four parts. Part One: Deep History deals with human emotions and drives to deepen the phenomenology of evil witchcraft agency and its female feature. Part Two: Historical Times focuses on the natural control of malefice that engendered rare state and church repressions. Surprisingly, (...) Islamic lands remained immune from stakes. Modern urbanizations are considered a blow to the persistence of rural civilizations that embedded witchcraft in their vision. Part Three: In the Laboratory applies neurosciences to specific case studies such as the personalization of misfortune, the millenary stereotype witch=woman, the reality of evil, and the contagious meme of treasure hunts. Part Four: Millennials wonders whether intentional malefic hatred is a closed chapter in the history of humanity. An Anatomy of Witchcraft is ideal reading for students and scholars. Given its interdisciplinary nature, the book will be of interest to scholars from many fields including evolutionary psychology, anthropology, women's history, and cognitive sciences. (shrink)

There have been few attempts, so far, to document the history of artificial intelligence. It is argued that the “historical sociology of scientific knowledge” can provide a broad historiographical approach for the history of AI, particularly as it has proved fruitful within the history of science in recent years. The article shows how the sociology of knowledge can inform and enrich four types of project within the history of AI; organizational history; AI viewed as (...) technology; AI viewed as cognitive science and historical biography. In the latter area the historical treatments of Darwin and Turing are compared to warn against the pitfalls of “rational reconstructions” of the past. (shrink)

We discuss the development of cognitive neuroscience in terms of the tension between the greater sophistication in cognitive concepts and methods of the cognitive sciences and the increasing power of more standard biological approaches to understanding brain structure and function. There have been major technological developments in brain imaging and advances in simulation, but there have also been shifts in emphasis, with topics such as thinking, consciousness, and social cognition becoming fashionable within the brain sciences. The discipline (...) has great promise in terms of applications to mental health and education, provided it does not abandon the cognitive perspective and succumb to reductionism. (shrink)

The Edinburgh History of Distributed Cognition (Series Editor(s): Miranda Anderson, Douglas Cairns) -/- Questions the barriers between the humanities and the cognitive sciences. -/- Cognitive science is finding increasing evidence that cognition is distributed across brain, body and world. This series calls for a reappraisal of historical concepts of cognition in light of these findings. It engages with recent debates about the various strong or weak models of distributed cognition and brings them into discourse with research (...) in the humanities. Together, the books in this series give a wide-ranging examination of the parallels (and divergences) from these models in cultural, philosophical and scientific works, from antiquity to the mid-20th century. -/- Key Features: * Opens up our reading of Western European works in the fields of history of ideas, history of science, material culture and literary studies by bringing recent insights in cognitive science to bear on the distributed nature of cognition * Explores how sociocultural and environmental contexts lead to the manifestation of particular forms of cognitive paradigms or to their suppression * Traces the interconnections between and particular divergences across Western European history among the various concepts of distributed cognition -/- . (shrink)

Organic metaphors appear as early as §2 of the Phenomenology and throughout Hegel’s major works. The culmination of the dialectic is the moment where Life understands itself. Hegel even identifies the Notion with the “principle of all life”. Yet despite Hegel’s emphasis on the notion of Life, there is no general agreement about the significance of his notion of organism. Some commentators emphasize Hegel’s organicism only in connection with the notion of organic unities in Hegel’s social philosophy. Still others acknowledge (...) its basic position in Hegel’s metaphysics, but regard it as refuted by the development of mechanistic science. Some even regard Hegel’s organicism as broadly consistent with non-organic science, leaving the status of Hegel’s notion of organism in conceptual limbo. (shrink)

Within the debate on the inevitability versus contingency of science for which Hacking’s writings have provided the basic terminology, the devising of counterfactual histories of science is widely assumed by champions of the contingency thesis to be an effective way to challenge the inevitability thesis. However, relatively little attention has been devoted to the problem of how to defend counterfactual history of science against the criticism that it is too speculative an endeavor to be worth bothering (...) with—the same critique traditionally levelled against the use of counterfactuals in general history. In this paper, we review the defense of counterfactuals put forward by their advocates within general history. According to such defense—which emphasizes the essential role of counterfactuals within explanations—good counterfactual scenarios need to exhibit the right kind of plausibility, characterized as continuity between said scenarios and what historians know about the world. As our discussion shows, the same requirement needs to be satisfied by good counterfactual histories of science. However, as we mention in the concluding part of the paper, there is at least one concern raised by counterfactual history of science as used to support the contingency thesis for which the defense based on the plausibility of the counterfactual scenarios does not seem to offer easy solutions. (shrink)

Cognitive Science, Literature, and the Arts is the first student-friendly introduction to the uses of cognitive science in the study of literature, written specifically for the non-scientist. Patrick Colm Hogan guides the reader through all of the major theories of cognitive science, focusing on those areas that are most important to fostering a new understanding of the production and reception of literature. This accessible volume provides a strong foundation of the basic principles of (...) class='Hi'>cognitive science, and allows us to begin to understand how the brain works and makes us feel as we read. (shrink)

The history of science can be better understood against the background of a history of knowledge comprising not only theoretical but also intuitive and practical knowledge. This widening of scope necessitates a more concise definition of the concept of knowledge, relating its cognitive to its material and social dimensions. The history of knowledge comprises the history of institutions in which knowledge is produced and transmitted. This is an essential but hitherto neglected aspect of cultural (...) evolution. Taking this aspect into account one is led to the concept of extended evolution, which integrates the perspectives of niche construction and complex regulative networks. The paper illustrates this concept using four examples: the emergence of language, the Neolithic revolution, the invention of writing and the origin of mechanics. (shrink)

The greater part of this book is a careful analysis and defense of H. von Helmholtz's theory of perception. But this analysis is also meant to justify a more basic thesis, which can be seen as the central point of the work as a whole. This central thesis is the assertion of the need to return to a plausible form of epistemological realism after the long and misguided history of mind-dominated philosophy--that is to say, of Cartesian rationalism, subjective and (...) objective idealism, phenomenology, and linguistic philosophy. The function of the long subtitle is to suggest a link between the naive realism of Aristotelian epistemology and what Meyering calls Helmholtz's "hypothetical realism.". (shrink)

This chapter takes a naturalized approach to the philosophy of science using evidence from cognitive psychology and from the history of science. It first describes the problem of the theory ladenness of perception. Then it provides a general top-down/bottom-up framework from cognitive psychology that is used to organize and evaluate the evidence for theory ladenness throughout the process of carrying out science (perception, attention, thinking, experimenting, memory, and communication). The chapter highlights both the facilitatory (...) and inhibitory role of theory in the scientific process. Both the historical record and the evidence from cognitive psychology suggest that the top-down factors have their greatest impact when the bottom-up information is weak or degraded. There is a short discussion of the normative implications of the material in the chapter for carrying out science. (shrink)

Accession Number: ATLA0001788504; Hosting Book Page Citation: p 265-277.; Physical Description: diag ; Language(s): English; Issued by ATLA: 20130825; Publication Type: Essay.

In lieu of an abstract, here is a brief excerpt of the content:The Philosophic Foundations of Mimetic Theory and Cognitive Science(Including Artificial Intelligence)Jean-Pierre Dupuy (bio)In the mid 1970s I discovered at the same time cognitive science and mimetic theory. Being a philosopher with a scientific background, I immediately brought them together and tried to reconceptualize the latter in terms of the former. In a sense, I haven't stopped doing that in the last 45 years. That is (...) why I feel fully at home with the goals pursued by the organizers of this conference.I do not believe it is feasible or even desirable to operate the rapprochement between the two disciplines at a global level. The history of cognitive science is exceedingly involved, and the part played by artificial intelligence in it compared to the part played by the neurosciences has varied greatly over time.1 Only a piecemeal approach makes sense. I present here four short case studies that cover the whole period, from the beginnings in cybernetics to today's artificial intelligence (AI).In order to better situate these studies within a broader frame, I propose the diagram in Figure 1. To comment on it would require more time than available for my whole presentation. Let me just say the following. [End Page 1]It is essential to distinguish between two kinds of AI. The first to bear this name was launched by economist Herbert Simon and computer scientist Allan Newell at a Dartmouth conference in 1956. It was based on the premise that thinking is computing, with this computation bearing on sentences of a "language of thought" implementable in a digital computer. It turns out that this AI(1) was progressively substituted from the 1980s onward by an AI(2), which, to some extent, was a return to a fundamental result obtained by cybernetics in 1943. Neurophysiologist Warren McCulloch and mathematician Walter Pitts had constructed a mathematical model of a network of elementary calculators called "neurons" and explored its properties. The capacity to "think" was one of these. It was not situated at the elementary level, as was the case in AI(1), but it emerged at the collective level from the interactions between the neurons.This model was heavily criticized by the supporters of AI(1),2 and its first implementations, in the notorious "Perceptron" in particular (1957), were pitiful failures. However, the emergence of "second-order cybernetics" under the guidance of cybernetician Heinz von Foerster and the theory of complex, self-organizing systems that it helped develop, in cooperation and rivalry with similar efforts in the physical sciences, enabled the neural network model to come to a second life under different names: formal neurons, deep learning, big data, and the like. Together, they constitute AI(2).The following network of influences wouldn't have been possible without its stem, that is, the boxes containing the names of Kurt Gödel and Alan Turing. The former revolutionized logic by showing that reasoning about arithmetic and arithmetic are one and the same, insofar as it is possible to code in a 1-to-1 fashion propositions about integers (for instance, "7 is a prime number") by means of integers. The latter demonstrated that some mechanisms can self-transcend inasmuch as they generate an ensemble of outputs that cannot be defined mechanistically. Later, John von Neumann, who is credited along with Alan Turing with having invented the digital computer, would use this logical possibility to define complexity: A mechanism is complex if it is capable of generating one more complex than itself. The gate was open for the theories of complex, self-organizing systems to thrive and, by way of consequence, for AI(2) to emerge.fascination with modelsHerbert Simon (1916–2001), winner of the Nobel Prize in economics for his work on what he called "bounded rationality," is generally considered to be one [End Page 2] Click for larger view View full resolutionFigure 1.of the founders of artificial intelligence. Some cognitive scientists still rely on the computer program that he devised with Alan Newell to simulate the creative processes of human thought—a program aptly named "General Problem Solver." The title of one of his principal works, The Sciences of the... (shrink)

This paper considers the past and future of Psychology within Cognitive Science. In the history section, I focus on three questions: (a) how has the position of Psychology evolved within Cognitive Science, relative to the other disciplines that make up Cognitive Science; (b) how have particular Cognitive Science areas within Psychology waxed or waned; and (c) what have we gained and lost. After discussing what’s happened since the late 1970s, when the (...) Society and the journal began, I speculate about where the field is going. (shrink)

What will be the future of social science? Where exactly do we stand, and where do we go from here? What kinds of problems should we be addressing, with what kinds of approaches and arguments? In Cognitive Dimensions of Social Science, Mark Turner offers an answer to these pressing questions: social science is headed toward convergence with cognitive science. Together they will give us a new and better approach to the study of what human (...) beings are, what human beings do, what kind of mind they have, and how that mind developed over the history of the species. Turner, one of the originators of the cognitive scientific theory of conceptual integration, here explores how the application of that theory enriches the social scientific study of meaning, culture, identity, reason, choice, judgment, decision, innovation, and invention. About fifty thousand years ago, humans made a spectacular advance: they became cognitively modern. This development made possible the invention of the vast range of knowledge, practices, and institutions that social scientists try to explain. For Turner, the anchor of all social science - anthropology, political science, sociology, economics - must be the study of the cognitively modern human mind. In this book, Turner moves the study of those extraordinary mental powers to the center of social scientific research and analysis. (shrink)

This paper outlines the case for a future research program that uses the tools of experimental cognitive science to investigate questions that traditionally fall under the remit of the philosophy of historiography. The central idea is this – the epistemic profile of historians’ representations of the past is largely an empirical matter, determined in no small part by the cognitive processes that produce these representations. However, as the philosophy of historiography is not presently equipped to investigate such (...) cognitive questions, legitimate concerns about evidential quality go largely overlooked. The case of mental state representation provides an excellent illustration of this. Representations of past mental states – the thoughts and fears and knowledge and desires of past agents – play much the same evidential role in historiography as in everyday life, serving in the causal explanation of agents’ behaviors and supporting normative evaluation of those behaviors. However, we have good reason to suspect that the theory of mind processes that support these representations may be more susceptible to error when deployed in the context of historiography than under everyday conditions. This raises worries about the quality of evidence that theory of mind can provide historiography, worries which require experimental cognitive science to properly address. (shrink)