This paper describes DIVA (Dynamic Imagery for Visual Analogy), a computational model of visual imagery based on the scene graph, a powerful representational structure widely used in computer graphics. Scene graphs make possible the visual display of complex objects, including the motions of individual objects. Our model combines a semantic-network memory system with computational procedures based on scene graphs. The model can account for people’s ability to produce visual images of moving objects, in particular the ability to use dynamic visual (...) analogies that compare two systems of objects in motion. (shrink)
We present a model for generating a kind of neural synchrony in which the individual spike trains of one neuron or group of neurons closely match the spike trains of another. This kind of neural synchrony has been observed in animals performing auditory, visual and attentional information processing tasks. Our model is realized in a system of functionally identical, refractory spiking neurons. Larger systems with more sophisticated information processing capabilities can be constructed from aggregated instances of the basic network.
Computer science only became established as a field in the 1950s, growing out of theoretical and practical research begun in the previous two decades. The field has exhibited immense creativity, ranging from innovative hardware such as the early mainframes to software breakthroughs such as programming languages and the Internet. Martin Gardner worried that "it would be a sad day if human beings, adjusting to the Computer Revolution, became so intellectually lazy that they lost their power of creative thinking" (Gardner, 1978, (...) p. vi-viii). On the contrary, computers and the theory of computation have provided great opportunities for creative work. This chapter examines several key aspects of creativity in computer science, beginning with the question of how problems arise in computer science. We then discuss the use of analogies in solving key problems in the history of computer science. Our discussion in these sections is based on historical examples, but the following sections discuss the nature of creativity using information from a contemporary source, a set of interviews with practicing computer scientists collected by the Association of Computing Machinery’s on-line student magazine, Crossroads. We then provide a general comparison of creativity in computer science and in the natural sciences. (shrink)
Computer modelling of personality and behaviour is becoming increasingly important in many ﬁelds of computer science and psychology. Personality and emotion-driven Believable Agents are needed in areas like human–machine interfaces, electronic advertising and, most notably, electronic entertainment. Computer models of personality can help explain personality by illustrating its underlying structure and dynamics. This work presents a neuralnetwork model of personality and personality change. The goals are to help understand personality and create more realistic and believable characters for (...) interactive video games. The model is based largely on trait theories of personality. Behaviour in the model results from the interaction of three components: (1) personality-based predispositions for behaviour, (2) moods/emotions and (3) environmental situations. Personality develops gradually over time depending on the situations encountered. Modelling personality change produces interesting and believable virtual characters whose behaviours change in psychologically plausible ways. (shrink)
Darwin’s theory of evolution by natural selection is central to modern biology, but is resisted by many people. This paper discusses the major psychological obstacles to accepting Darwin’s theory. Cognitive obstacles to adopting evolution by natural selection include conceptual difﬁculties, methodological issues, and coherence problems that derive from the intuitiveness of alternative theories. The main emotional obstacles to accepting evolution are its apparent conﬂict with valued beliefs about God, souls, and morality. We draw on the philosophy of science and on (...) a psychological theory of cognitive and emotional belief revision to make suggestions about what can be done to improve acceptance of Darwinian ideas. (shrink)
By the early part of the twentieth century, academia in the English-speaking world had stabilized (or ossified!) into a set of scientific and humanistic disciplines that still survives at the century’s end. The natural sciences have such disciplines as physics, chemistry, and biology, and the social sciences include economics, psychology, and sociology. These disciplines provide a convenient organizing principle for university departments and professional organizations, but they often bear little relation to cuttingedge research, which can concern topics that cut across (...) or occur at the boundaries of two or more of the established disciplines. When this happens, productive research and teaching must be interdisciplinary. Cognitive science is the interdisciplinary study of mind, embracing psychology, artificial intelligence, philosophy, neuroscience, linguistics, and anthropology. It is undoubtedly one of the major interdisciplinary successes of the twentieth century, with its own society, journal, and textbooks, and with more than sixty cognitive science programs established at universities in North American and Europe. This paper is an attempt to answer the question: What are the factors contributing to the success of the interdisciplinary field of cognitive science? My discussion is organized around the metaphor of the trading zone, a novel and fertile analogy that Gallison (1997) developed for his rich and detailed discussion of the practices of twentieth-century physics. To understand the diverse groups of December 1, 2006 experimenters and theoreticians, Gallison presents their interactions in terms of the trading zones described by anthropologists: Subcultures trade. Anthropologists have extensively studied how different groups, with radically different ways of dividing up the world and symbolically organizing its parts, can not only exchange goods but also depend essentially on those trades.. (shrink)
What do philosophers do? Twenty years ago, one might have heard such answers to this question as "analyze concepts" or "evaluate arguments". The answer "write computer programs" would have inspired a blank stare, and even a decade ago I wrote that computational philosophy of science might sound like the most self-contradictory enterprise in philosophy since business ethics (Thagard 1988). But computer use has since become much more common in philosophy, and computational modeling can be seen as a useful addition (...) to philosophical method, not as the abandonment of it. I will try in this paper to summarize how computational models are making substantial contributions to the philosophy of science. If philosophy consisted primarily of conceptual analysis, or mental self-examination, or generation of a priori truths, then computer modeling would indeed be alien to the enterprise. But I prefer a different picture of philosophy, as primarily concerned with producing and evaluating theories, for example theories of knowledge (epistemology), reality (metaphysics), and right and wrong (ethics). The primary function of a theory of knowledge is to explain how knowledge grows, which requires both describing the structure of knowledge and the inferential procedures by which knowledge can be increased. Although epistemologists often focus on mundane knowledge, the most impressive knowledge gained by human beings comes through the operation of science: experimentation, systematic observation, and theorizing concerning the experimental and observational results. Hence at the core of epistemology is the need to understand the structure and growth of scientific knowledge, a project for which computational models can be very useful. In attempting to understand the structure and development of scientific knowledge, philosophers of science have traditionally employed a number of methods such as logical analysis and historical case studies. Computational modeling provides an additional method that has already advanced understanding of such traditional problems in the philosophy of science as theory evaluation and scientific discovery.. (shrink)
Biology is the study of life, psychology is the study of mind, and medicine is the investigation of the causes and treatments of disease. This chapter describes how the central concepts of life, mind, and disease have undergone fundamental changes in the past 150 years or so. There has been a progression from theological, to qualitative, to mechanistic explanations of the nature of life, mind and disease. This progression has involved both theoretical change, as new theories with greater explanatory power (...) replaced older ones, and emotional change as the new theories brought reorientation of attitudes toward the nature of life, mind, and disease. After a brief comparison of theological, qualitative, and mechanistic explanations, I will describe how shifts from one kind of explanation to another have carried with them dramatic kinds of conceptual change in the key concepts in the life sciences. Three generalizations follow about the nature of conceptual change in the history of science: there has been a shift from conceptualizations in terms of simple properties to ones in terms of complex relations; conceptual change is theory change; and conceptual change is often emotional as well as cognitive. The contention that historical development proceeds in three stages originated with the nineteenth-century French philosophers, Auguste Comte, who claimed that November 9, 2006 human intellectual development progresses from a theological to a “metaphysical” stage to a “positive” (scientific) stage (Comte, 1988). The stages I have in mind are different from Comte’s, so let me say what they involve. By the theological stage I mean systems of thought in which the primary explanatory entities are supernatural ones beyond the reach of science, such as gods, devils, angels, spirits, and souls. For example, the concept of fire was initially theological, as in the Greek myth of Prometheus receiving fire from the gods.. (shrink)
In 1983, Dr. J. Robin Warren and Dr. Barry Marshall reported finding a new kind of bacteria in the stomachs of people with gastritis. Warren and Marshall were soon led to the hypothesis that peptic ulcers are generally caused, not by excess acidity or stress, but by a bacterial infection. Initially, this hypothesis was viewed as preposterous, and it is still somewhat controversial. In 1994, however, a U. S. National Institutes of Health Consensus Development Panel concluded that infection appears to (...) play an important contributory role in the pathogenesis of peptic ulcers, and recommended that antibiotics be used in their treatment. Peptic ulcers are common, affecting up to 10% of the population, and evidence has mounted that many ulcers can be cured by eradicating the bacteria.. (shrink)
Debates about evolution and creation inevitably raise philosophical issues about the nature of scientific knowledge. What is a theory? What is an explanation? How is science different from non- science? How should theories be evaluated? Does science achieve truth? The aim of this chapter is to give a concise and accessible introduction to the philosophy of science, focusing on questions relevant to understanding evolution by natural selection, creation, and intelligent design. For the questions just listed, I state what I think (...) is the best available answer and show how it applies to debates about evolution and creationism. I also indicate alternative answers that are preferred by other philosophers. I hope that the result will be useful for science educators and anyone else involved in controversies about evolution and creation. (shrink)
��This article reviews a theory of explanatory coherence that provides a psychologically plausible account of how people evaluate competing explanations. The theory is implemented in a computational model that uses simple artiﬁcial neural networks to simulate many important cases of scientific and legal reasoning. Current research directions include extensions to emotional thinking and implementation in more biologically realistic neural networks.
This article interprets emotional change as a transition in a complex dynamical sys- tem. We argue that the appropriate kind of dynamical system is one that extends recent work on how neural networks can perform parallel constraint satisfaction. Parallel processes that integrate both cognitive and affective constraints can give rise to states that we call emotional gestalts, and transitions can be understood as emotional ges- talt shifts. We describe computational models that simulate such phenomena in ways that show how dynamical (...) and gestalt metaphors can be given a concrete realization. (shrink)
Deep appreciation of the relevance of emotion to epistemology requires a rich account of how emotional mental states such as happiness, sadness and desire interact with cognitive states such as belief and doubt. Analytic philosophy since Gottlob Frege and Bertrand Russell has assumed that such mental states are propositional attitudes, which are relations between a self and a proposition, an abstract entity constituting the meaning of a sentence. This chapter shows the explanatory defects of the doctrine of propositional attitudes, and (...) proposes instead that beliefs, desires, and emotions should be construed naturalistically using current understanding of brain mechanisms. Mental states are patterns of neural activity, not relations between dubious entities such as selves and propositions. From this perspective, it becomes easy to see how cognition and emotion are intertwined, and hence how emotions can be integral to epistemology. I begin by reviewing some of the ways in which emotions are relevant to epistemology: as frequent contributors to the growth of knowledge, as sometime impediments to knowledge acquisition, and as components of knowledge about persons and morality. I then argue that propositional attitudes do not exist, because the selves and the propositions that they purportedly relate do not exist. Thus the doctrine of propositional attitudes is as useless for epistemology as it is for explaining human action. I argue for an alternative construal of mental states as patterns of neural activity, and July 23, 2007 describe how it is possible to give a theoretically rich and empirically supported account of the neurophysiological interconnections of cognition and emotion. Finally, I discuss the epistemological significance of this naturalistic, materialist reconstrual of cognitions and emotions. (shrink)
Studies in the history, philosophy, sociology, and psychology of science and technology have gathered much information about important cases of scientific development. These cases usually concern the most successful scientists and inventors, such as Darwin, Einstein, and Edison. But case studies rarely address the question of what made these investigators more accomplished than the legions of scientific laborers whose names have been forgotten. This chapter is an attempt to identify many of the psychological and other factors that make some scientists (...) highly successful. I explore two sources of information about routes to scientific achievement. The first derives from a survey that Jeff Shrager conducted at the Workshop on Cognitive Studies of Science and Technology at the University of Virginia in March, 2001. He asked the participants to list “7 habits of highly creative people”, and after the workshop he and I compiled a list of habits recommended by the distinguished group of historians, philosophers, and psychologists at the workshop. My second source of information about the factors contributing to scientific success is advice given by three distinguished biologists who each won a Nobel prize: Santiago Ramón y Cajal, Peter Medawar, and James Watson. These biologists provide advice that usefully supplements the suggestions from the workshop participants. (shrink)
Students face many important decisions: What college or university should I attend? What should I study? What kind of job should I try to get? Which people should I hang out with? Should I continue or break off a relationship? Should I get married? Should I have a baby? What kind of medical treatment should I use? A theory of practical reasoning should have something to say about how students and other people can improve their decision making.
Reasoning by jurors concerning whether an accused person should be convicted of committing a crime is a kind of casual inference. Jurors need to decide whether the evidence in the case was caused by the accused’s criminal action or by some other cause. This paper compares two computational models of casual inference: explanatory coherence and Bayesian networks. Both models can be applied to legal episodes such as the von Bu¨low trials. There are psychological and computational reasons for preferring the explanatory (...) coherence account of legal inference. (shrink)
Explanation of why things happen is one of humans’ most important cognitive operations. In everyday life, people are continually generating explanations of why other people behave the way they do, why they get sick, why computers or cars are not working properly, and of many other puzzling occurrences. More systematically, scientists develop theories to provide general explanations of physical phenomena such as why objects fall to earth, chemical phenomena such as why elements combine, biological phenomena such as why species evolve, (...) medical phenomena such as why organisms develop diseases, and psychological phenomena such as why people sometimes make mental errors. This chapter reviews computational models of the cognitive processes that underlie these kinds of explanations of why events happen. It is not concerned with another sense of explanation that just means clarification, as when someone explains the U. S. constitution. The focus will be on scientific explanations, but more mundane examples will occasionally be used, on the grounds that the cognitive processes for explaining why events happen are much the same in everyday life and in science, although scientific explanations tend tobe more systematic and rigorous than everyday ones. In addition to providing a concise review of previous computational models of explanation, this chapter describes a new neural network model that shows how explanations can be performed by multimodal distributed representations. (shrink)
Science is studied in very different ways by historians, philosophers, psychologists, and sociologists. Not only do researchers from different fields apply markedly different methods, they also tend to focus on apparently disparate aspects of science. At the farthest extremes, we find on one side some philosophers attempting logical analyses of scientific knowledge, and on the other some sociologists maintaining that all knowledge is socially constructed. This paper is an attempt to view history, philosophy, psychology, and sociology of science from a (...) unified perspective. (shrink)
This paper uses a psychological/computational theory of emotional coherence to explain several aspects of religious belief and practice. After reviewing evidence for the importance of emotion to religious thought and cognition in general, it describes psychological and social mechanisms of emotional cognition. These mechanisms are relevant to explaining the acquisition and maintenance of religious belief, and also shed light on such practices as prayer and other rituals. These psychological explanations are contrasted with ones based on biological evolution.
Modern medicine has produced many successful theories concerning the causes of diseases. For example, we know that tuberculosis is caused by the bacterium Mycobacterium tuberculosis, and that scurvy is caused by a deficiency of vitamin C. This chapter discusses the nature of medical theories from the perspective of the philosophy, history, and psychology of science. I will review prominent philosophical accounts of what constitutes a scientific theory, and develop a new account of medical theories as representations of mechanisms that explain (...) disease. An account of the nature of medical theories should illuminate many aspects of the development and application of medical knowledge. Most importantly, it should contribute to understanding of medical explanation, both at the general level of causes of diseases and at the individual level of diagnosis of particular cases of a disease. Medical researchers seek to explain the causes of diseases such as tuberculosis, while physicians seek to identify diseases that explain symptoms such as fever. A medical theory such as the bacterial theory of tuberculosis provides good explanations at both the general and individual levels. The primary aim of this chapter is to show how these explanations work. A secondary aim is to show how an account of medical theories can shed light on other aspects of medical research and practice, including the nature of medical discovery, the process of evaluation of competing medical theories, and the ways in which effective treatments of disease depend on the development of good mechanistic theories about diseases. (shrink)
analogies that epistemologists have used to discuss the structure and validity of knowledge. After reviewing foundational, coherentist, and other metaphors for knowledge, we discuss the metaphilosophical significance of the prevalence of such metaphors. We argue that they support a view of philosophy as akin to science rather than poetry or rhetoric.
Whereas scientists formulate laws and theories to account for observations, inventors create new technology to accomplish practical goals. Scientific discovery and technological innovation are among the most important accomplishments of the creative human mind. The aim of this paper is to compare how scientists produce discoveries with how inventors produce new technology. After briefly reviewing an account of the recent discovery of the bacterial theory of ulcers, we show that a similar account applies to the discovery that dinosaurs became extinct (...) because of an asteroid collision. Both these discoveries involved a combination of serendipity, questioning and search. We then describe how these three processes also contributed to a very important recent technological innovation, the development of the programming language Java. The paper concludes with a more general assessment of the similarities and differences between cognitive processes involved in discovery and invention. (shrink)
Scientists sometimes change their minds. A 2008 survey on the Edge Web site presented more than 100 self-reports of thinkers changing their minds about scientific and methodological issues (http://www.edge.org/q2008/q08_index.html). For example, Stephen Schneider, a Stanford biologist and climatologist, reported how new evidence in the 1970s led him to abandon his previously published belief that human atmospheric emissions would likely have a cooling rather than a warming effect. Instead, he came to believe – what is now widely accepted – that greenhouse (...) gases such as carbon dioxide are contributing to the dramatic trend of global warming. Similarly, Laurence Smith, a UCLA geographer, reported how in 2007 he came to believe that major changes resulting from global warming will come much sooner than he had previously thought. Observations such as the major sea-ice collapse in Canada’s Northwest Passage had not been predicted to occur so soon by available computational models, but indicated that climate change is happening much faster than expected. Evidence accumulated over the past three decades is widely taken to show that global warming will have major impacts on human life, and that policy changes such as reducing the production of greenhouse gases are urgently needed. However, such scientific and policy conclusions have received considerable resistance, for example from former American president George W. Bush and Canadian Prime Minister Stephen Harper. (shrink)
In their introduction to this volume, Ram and Leake usefully distinguish between task goals and learning goals. Task goals are desired results or states in an external world, while learning goals are desired mental states that a learner seeks to acquire as part of the accomplishment of task goals. We agree with the fundamental claim that learning is an active and strategic process that takes place in the context of tasks and goals (see also Holland, Holyoak, Nisbett, and Thagard, 1986). (...) But there are important questions about the nature of goals that have rarely been addressed. First, how can a cognitive system deal with incompatible task goals? Someone may want both to get lots of research done and to relax and have fun with his or her friends. Learning how to accomplish both these tasks will take place in the context of goals that cannot be fully realized together. Second, how are goals chosen in the first place and why are some goals judged to be more important than others? People do not simply come equipped with goals and priorities: we sometimes have to learn what is important to us by adjusting the importance of goals in the context of other compatible and incompatible goals. This paper presents a theory and a computational model of how goals can be adopted or rejected in the context of decision making. In contrast to classical decision theory, it views decision making as a process not only of choosing actions but also of evaluating goals. Our theory can therefore be construed as concerned with the goal-directed learning of goals. (shrink)
Despite the growing appreciation of the relevance of affect to cognition, analogy researchers have paid remarkably little attention to emotion. This paper discusses three general classes of analogy that involve emotions. The most straightforward are analogies and metaphors about emotions, for example "Love is a rose and you better not pick it." Much more interesting are analogies that involve the transfer of emotions, for example in empathy in which people understand the emotions of others by imagining their own emotional reactions (...) in similar situations. Finally, there are analogies that generate emotions, for example analogical jokes that generate emotions such as surprise and amusement. (shrink)
The authors present a neurological theory of how cognitive information and emotional information are integrated in the nucleus accumbens during effective decision making. They describe how the nucleus accumbens acts as a gateway to integrate cognitive information from the ventromedial prefrontal cortex and the hippocampus with emotional information from the amygdala. The authors have modeled this integration by a network of spiking artificial neurons organized into separate areas and used this computational model to simulate 2 kinds of cognitive–affective integration. The (...) model simulates successful performance by people with normal cognitive–affective integration. The model also simulates the historical case of Phineas Gage as well as subsequent patients whose ability to make decisions became.. (shrink)
This paper is an investigation of the degree of incommensurability between Western scientific medicine and traditional Chinese medicine, focusing on the practice and theory of acupuncture. We describe the structure of traditional Chinese medicine, oriented around such concepts as yin, yang, qi, and xing, and discuss how the conceptual and explanatory differences between Western medicine and traditional Chinese medicine generate impediments to their comparison and evaluation. We argue that the linguistic, conceptual, ontological, and explanatory impediments can to a large extent (...) be overcome, and conclude that the dramatic differences between Western and traditional Chinese medicine do not provide insurmountable barriers to rational evaluation of acupuncture. We conclude with a discussion of the intentional and emotional aspects of conceptual change. (shrink)
Barnes, A. and P. Thagard (1997) Empathy and analogy . Dialogue: Canadian Philosophical Review , 36: 705-720. HTML Croft, D., & Thagard, P. (2002). Dynamic imagery: A computational model of motion and visual analogy. In L. Magnani and N. Nersessian (Eds.), Model-based reasoning: Science, technology, values . New York: Kluwer/Plenum, 259-274. PDF only. HTML description of program and code for DIVA.
We propose a unified theory of intentions as neural processes that integrate representations of states of affairs, actions, and emotional evaluation. We show how this theory provides answers to philosophical questions about the concept of intention, psychological questions about human behavior, computational questions about the relations between belief and action, and neuroscientific questions about how the brain produces actions. Our theory of intention ties together biologically plausible mechanisms for belief, planning, and motor control. The computational feasibility of these mechanisms is (...) shown by a model that simulates psychologically important cases of intention. (shrink)
The role of psychology in science studies Content Type Journal Article Category Book Review Pages 1-4 DOI 10.1007/s11016-012-9666-1 Authors Paul Thagard, Philosophy Department, University of Waterloo, Waterloo, ON N2L 3G1, Canada Journal Metascience Online ISSN 1467-9981 Print ISSN 0815-0796.
This volume serves as a detailed introduction for those new to the field as well as a rich source of new insights and potential research agendas for those already engaged with the philosophy of economics.
This article is a response to Elijah Millgram's argument that my characterization of coherence as constraint satisfaction is inadequate for philosophical purposes because it provides no guarantee that the most coherent theory available will be true. I argue that the constraint satisfaction account of coherence satisfies the philosophical, computational, and psychological prerequisites for the development of epistemological and ethical theories.
This paper proposes an account of the self as a multilevel system consisting of social, individual, neural, and molecular mechanisms. It argues that the functioning of the self depends on causal relations between mechanisms operating at different levels. In place of reductionist and holistic approaches to cognitive science, I advocate a method of multilevel interacting mechanisms. This method is illustrated by showing how self-concepts operate at several different levels.
This article challenges the common view that improvements in critical thinking are best pursued by investigations in informal logic. From the perspective of research in psychology and neuroscience, hu-man inference is a process that is multimodal, parallel, and often emo-tional, which makes it unlike the linguistic, serial, and narrowly cog-nitive structure of arguments. At-tempts to improve inferential prac-tice need to consider psychological error tendencies, which are patterns of thinking that are natural for peo-ple but frequently lead to mistakes in judgment. (...) This article discusses two important but neglected error ten-dencies: motivated inference and fear-driven inference. (shrink)
Here are some of the most important discoveries in the history of medicine: blood circulation (1620s), vaccination, (1790s), anesthesia (1840s), germ theory (1860s), X- rays (1895), vitamins (early 1900s), antibiotics (1920s-1930s), insulin (1920s), and oncogenes (1970s). This list is highly varied, as it includes basic medical knowledge such has Harvey’s account of how the heart pumps blood, hypotheses about the causes of disease such as the germ theory, ideas about the treatments of diseases such as antibiotics, and medical instruments such (...) as X-ray machines. The philosophy of medicine should be able to contribute to understanding of the nature of discoveries such as these. The great originators of the field of philosophy of science were all concerned with the nature of scientific discovery, including Francis Bacon (1960), William Whewell (1967), John Stuart Mill (1974), and Charles Peirce (1931-1958). The rise of logical positivism in the 1930s pushed discovery off the philosophical agenda, but the topic was revived through the work of philosophers such as Norwood Russell Hanson (1958), Thomas Nickles (1980), Lindley Darden (1991, 2006), and Nancy Nersessian (1984). Scientific discovery has also become an object of investigations for researchers in the fields of cognitive psychology and artificial intelligence, as seen in the work of Herbert Simon, Pat Langley, and others (Langley et al., 1987; Klahr, 2000). Today, scientific September 14, 2009 discovery is an interdisciplinary topic at the intersection of the philosophy, history, and psychology of science. The aim of this chapter is to identify patterns of discovery that illuminate some of the most important developments in the history of medicine. I have used a variety of sources to identify forty great medical discoveries (Adler, 2004; Friedman and Friedland, 1998; Science Channel, 2006; Strauss and Strauss, 2006).. (shrink)
Many kinds of creativity result from combination of mental representations. This paper provides a computational account of how creative thinking can arise from combining neural patterns into ones that are potentially novel and useful. We defend the hypothesis that such combinations arise from mechanisms that bind together neural activity by a process of convolution, a mathematical operation that interweaves structures. We describe computer simulations that show the feasibility of using convolution to produce emergent patterns of neural activity that can support (...) cognitive and emotional processes underlying human creativity. (shrink)
This paper uses the economic crisis of 2008 as a case study to examine the explanatory validity of collective mental representations. Distinguished economists such as Paul Krugman and Joseph Stiglitz attribute collective beliefs, desires, intentions, and emotions to organizations such as banks and governments. I argue that the most plausible interpretation of these attributions is that they are metaphorical pointers to a complex of multilevel social, psychological, and neural mechanisms. This interpretation also applies to collective knowledge in science: scientific communities (...) do not literally have collective representations, but social mechanisms do make important contributions to scientific knowledge. (shrink)
Este artigo investiga as mudanças conceituais revolucionárias que ocorreram quando a teoria do flogisto de Stahl foi substituída pela teoria do oxigênio de Lavoisier. Utilizando técnicas extraídas da inteligência artificial, o artigo descreve os estágios cruciais no desenvolvimento conceitual de Lavoisier, de 1772 até 1789. Em seguida, é esboçada uma teoria computacional da mudança conceitual de modo a explicar a descoberta de Lavoisier da teoria do oxigênio e a substituiçáo da teoria do flogisto. Este artigo é uma traduçáo de “The (...) Conceptual Structure of The Chemical Revolution”, publicado originalmente em Philosophy of Science , número 57, p. 183-209, 1990. Todos os direitos do artigo pertencem à revista Philosophy of Science , editada pela University of Chicago Press. O copyright do artigo original é de 1990, da Philosophy of Science Association. Os tradutores agradecem a Paul Thagard e a Philosophy of Science a permissáo para esta traduçáo. [NT.]. (shrink)
Contrary to common views that philosophy is extraneous to cognitive science, this paper argues that philosophy has a crucial role to play in cognitive science with respect to generality and normativity. General questions include the nature of theories and explanations, the role of computer simulation in cognitive theorizing, and the relations among the different ﬁelds of cognitive science. Normative questions include whether human thinking should be Bayesian, whether decision making should maximize expected utility, and how norms should be established. These (...) kinds of general and normative questions make philosophical reﬂection an important part of progress in cognitive science. Philosophy operates best, however, not with a priori reasoning or conceptual analysis, but rather with empirically informed reﬂection on a wide range of ﬁndings in cognitive science. (shrink)
The ultimatum game is a simple bargaining situation where the behavior of people frequently contradicts the optimal strategy according to classical game theory. Thus, according to many scholars, the commonly observed behavior should be considered irrational. We argue that this putative irrationality stems from a wrong conception of metanormativity (the study of norms about the establishment of norms). After discussing different metanormative conceptions, we defend a Quinean, naturalistic approach to the evaluation of norms. After reviewing empirical literature on the ultimatum (...) game, we argue that the common behavior in the ultimatum game is rational and justified. We therefore suggest that the norms of economic rationality should be amended. (shrink)
One of the most impressive feats in robotics was the 2005 victory by a driverless Volkswagen Touareg in the DARPA Grand Challenge. This paper discusses what can be learned about the nature of representation from the car’s successful attempt to navigate the world. We review the hardware and software that it uses to interact with its environment, and describe how these techniques enable it to represent the world. We discuss robosemantics, the meaning of computational structures in robots. We argue that (...) the car constitutes a refutation of semantic arguments against the possibility of strong artificial intelligence. (shrink)
This paper proposes a theory of how conscious emotional experience is produced by the brain as the result of many interacting brain areas coordinated in working memory. These brain areas integrate perceptions of bodily states of an organism with cognitive appraisals of its current situation. Emotions are neural processes that represent the overall cognitive and somatic state of the organism. Conscious experience arises when neural representations achieve high activation as part of working memory. This theory explains numerous phenomena concerning emotional (...) consciousness, including diﬀerentiation, integration, intensity, valence, and change. Ó 2007 Elsevier Inc. All rights reserved. (shrink)