Neurological syndromes in which consciousness seems to malfunction, such as temporal lobe epilepsy, visual scotomas, Charles Bonnet syndrome, and synesthesia offer valuable clues about the normal functions of consciousness and ‘qualia’. An investigation into these syndromes reveals, we argue, that qualia are different from other brain states in that they possess three functional characteristics, which we state in the form of ‘three laws of qualia’. First, they are irrevocable: I cannot simply decide to start seeing the sunset as green, or (...) feel pain as if it were an itch; second, qualia do not always produce the same behaviour: given a set of qualia, we can choose from a potentially infinite set of possible behaviours to execute; and third, qualia endure in short-term memory, as opposed to non-conscious brain states involved in the on-line guidance of behaviour in real time. We suggest that qualia have evolved these and other attributes because of their role in facilitating non-automatic, decision-based action. We also suggest that the apparent epistemic barrier to knowing what qualia another person is experiencing can be overcome by using a ‘bridge’ of neurons; and we offer a hypothesis about the relation between qualia and one's sense of self. (shrink)

En los inicios del siglo XXI está desarrollándose una e-ciencia, una ciencia electrónica y altamente computarizada que exige un replanteamiento sobre la epistemología científica. A través del ejemplo de la Bioinformática y las Biotecnologías, el autor muestra algunas características de esta nueva e-ciencia e indica algunos de los problemas con los que deben enfrentarse los filósofos de la ciencia contemporáneos. Right at the beginning of the 21st century an e-Science is emerging, a highly computerized electronic science which demands a new (...) way of understanding scientific epistemology. Through Bioinformatics and Biotechnologies, the author analyzes some of the characteristics of this new e-Science and shows several problems which contemporary philosophers of science must face. (shrink)

Research on computational models of scientific discovery investigates both the induction of descriptive laws and the construction of explanatory models. Although the work in law discovery centers on knowledge‐lean approaches to searching a problem space, research on deeper modeling tasks emphasizes the pivotal role of domain knowledge. As an example, our own research on inductive process modeling uses information about candidate processes to explain why variables change over time. However, our experience with IPM, an artificial intelligence system that implements this (...) approach, suggests that process knowledge is insufficient to avoid consideration of implausible models. To this end, the discovery system needs additional knowledge that constrains the model structures. We report on an extended system, SC‐IPM, that uses such information to reduce its search through the space of candidates and to produce models that human scientists find more plausible. We also argue that although people carry out less extensive search than SC‐IPM, they rely on the same forms of knowledge—processes and constraints—when constructing explanatory models. (shrink)

Przedmowa Problematyka związana z zależnościami przyczynowymi, ich modelowaniem i odkrywa¬niem, po długiej nieobecności w filozofii i metodologii nauk, budzi współcześnie duże zainteresowanie. Wiąże się to przede wszystkim z dynamicznym rozwojem, zwłaszcza od lat 1990., technik obli¬czeniowych. Wypracowane w tym czasie sieci bayesowskie uznaje się za matematyczny język przyczynowości. Pozwalają one na daleko idącą auto¬matyzację wnioskowań, co jest także zachętą do podjęcia prób algorytmiza¬cji odkrywania przyczyn. Na potrzeby badań naukowych, które pozwalają na przeprowadzenie eksperymentu z randomizacją, standardowe metody ustalania zależności przyczynowych (...) opracowano na początku XX wieku. Zupełnie inaczej sprawa przedstawia się w przypadku badań nieeksperymentalnych, gdzie podobne rozwiązania pozostają kwestią przyszłości. Zadaniem tej książki jest podanie warunków, które powinny być spełnione przez te rozwiązania, oraz sformułowanie proceduralnego kryterium zależności przy¬czynowych jako szczegółowej realizacji tych warunków. Pociąga ono waż¬kie konsekwencje dla filozofii i metodologii nauk, które ujawnia – podany w Części II – zarys me-todolo¬gii proceduralnej. W literaturze przedmiotu brakuje w miarę wszechstronnego i systema¬tycznego omówie¬nia najnowszych filozoficznych i metodologicznych dys¬kusji na temat przy¬czynowości, co niech będzie wytłumaczeniem, dlaczego w niektórych punktach obecnej książki szczegółowo referuję trudno dos¬tępne teksty źró¬dłowe. Przymiotnik „proceduralny” używam tu w znaczeniu węższym niż Huw Price (w którego pracach właściwszy byłby termin „kryterialny”) dla podkre¬ślenia – zgodnie z łacińskim źródłosłowem procedo – że dla ustalenia przy¬czyny niezbędne jest podjęcie przez uczonych określonych interakcji z ba¬daną rzeczywistością. Zalążki zamysłu prezentowanego w tej książce przedstawiłem podczas warsztatów filozoficznych „Philosophy and Probability” w roku 2002, zor¬ganizowanych przez Instytut Filozofii Uniwersytetu w Konstancji. Wdzięczny jestem uczestnikom tych warsztatów za uwagi, a przede wszyst¬kim następującym osobom: Luc Bovens, Brandon Fitelson, Alan Hájek, Stephan Hartmann oraz Jon Williamson. Podczas międzynarodowej konferencji „Analytical Pragmatism”, zorgani¬zowanej w Lublinie w roku 2003 przez Wydział Filo¬zofii Katolickiego Uniwersytetu Lubelskiego, odniosłem swoją koncepcję do prac Nancy Cartwright. Szczególnie inspirujący okazał się komentarz Huw Price’a do mojego referatu i przeprowadzona z nim dysku¬sja. Ujęcie koncepcji metodologii proceduralnej na szerszym tle współ-czes¬nego nurtu empirystycznego w filozofii nauki przedstawiłem w roku 2004 podczas konferencji „5th Quadrennial Fellows Conference”, zorgani¬zowanej przez Instytut Filozofii Uniwersytetu Jagiellońskiego oraz Centrum Filozofii Nauki w Pittsburghu. Szczególnie pomocne w dalszych moich pra¬cach były uwagi Jamesa Bogena, Janet Kourany, Jamesa Lennoxa, Johna Nortona, Thomasa Bonka, Jana Woleńskiego i Johna Worralla, za które wyra¬żam swoją wdzięczność. Korpus książki powstał podczas mojego stażu w Centrum Filozofii Nauki w Pittsburghu, który odbyłem jako stypendysta Fundacji na Rzecz Nauki Polskiej w roku akademickim 2004-2005. Uczestniczyłem w tym czasie w życiu naukowym Centrum i w pracach badawczych zespołu z Instytutu Filozofii Uniwersytetu Carnegie-Mellon w Pittsburghu kierowanego przez Clarka Glymoura. Na jego ręce składam podziękowanie za wiele po¬mocnych uwag do moich wy¬stąpień oraz tekstów i za dyskusje przede wszystkim z nim samym i z jego najbliższymi współpracownikami: Peterem Spirtesem oraz Richardem Scheinesem, a także pozostałymi członkami tego zes¬połu, doktorantami i uczestnikami seminarium badawczego „Causality in the Social Sciences”. Za wieloletnie wsparcie, wielopłaszczyznowe inspiracje towarzyszące pi¬saniu tej książki, a także liczne pomocne uwagi do jej wcześniejszych wersji dziękuję przede wszystkim Księdzu Profesorowi Andrzejowi Bronkowi oraz Księdzu Profesorowi Józefowi Herbutowi, współprowadzącemu seminarium doktorskie w Katedrze Metodologii Nauk Katolickiego Uniwersytetu Lubel¬skiego im. Jana Pawła II, jak również pozostałym uczestnikom tego semina¬rium. Dziękuję mojej Żonie, dr Annie Kawalec za wiele wysiłku włożonego w ulepszenie redakcji – językowej i merytorycznej – obecnej książki. Książkę tę można czytać na kilka sposobów. Czytelnikom zainteresowa¬nym przede wszystkim prowadzeniem badań empirycznych polecałbym rozpoczęcie od Rozdziału 2. i kontynuację pozostałych rozdziałów Części I, a następnie Dodatków. Czytelnikom zainteresowanym problemami filozo¬fii i metodologii nauk polecałbym rozpoczęcie lektury książki od Części II i uzupełniającą lekturę Rozdziału 2., a następnie Wprowadzenia i Zakończenia. Czytelnikom mniej zainteresowanym zagadnieniami teoretycznymi pole¬całbym zapoznanie się z fascynującymi dziejami odkrycia przyczyn cholery przez Johna Snowa, które rekonstruuję w Rozdziale 1. W dalszej części nato¬miast polecałbym przejście do Wprowadzenia i Zakończenia, które w mniej specjalistyczny sposób przybliżają proponowane tu rozstrzygnięcia. Tekst książki nie był dotąd publikowany. Wyjątkiem są pewne fragmenty Rozdziału 8. oraz 9., które w zmienionej postaci ukazały się w Rocznikach Filozoficznych (Kawalec 2004). -/- Lublin, luty 2006 r. (shrink)

The Linda paradox is a key topic in current debates on the rationality of human reasoning and its limitations. We present a novel analysis of this paradox, based on the notion of verisimilitude as studied in the philosophy of science. The comparison with an alternative analysis based on probabilistic confirmation suggests how to overcome some problems of our account by introducing an adequately defined notion of verisimilitudinarian confirmation.

Some think that issues to do with scientific method are last century's stale debate; Popper was an advocate of methodology, but Kuhn, Feyerabend, and others are alleged to have brought the debate about its status to an end. The papers in this volume show that issues in methodology are still very much alive. Some of the papers reinvestigate issues in the debate over methodology, while others set out new ways in which the debate has developed in the last decade. The (...) book will be of interest to philosophers and scientists alike in the reassessment it provides of earlier debates about method and current directions of research. (shrink)

This article describes abductions as special patterns of inference to the best explanation whose structure determines a particularly promising abductive conjecture and thus serves as an abductive search strategy. A classification of different patterns of abduction is provided which intends to be as complete as possible. An important distinction is that between selective abductions, which choose an optimal candidate from given multitude of possible explanations, and creative abductions, which introduce new theoretical models or concepts. While selective abduction has dominated the (...) literature, creative abductions are rarely discussed, although they are essential in science. The article introduces several kinds of creative abductions, such as theoretical model abduction, common cause abduction and statistical factor analysis, and illustrates them by various real case examples. It is suggested to demarcate scientifically fruitful abductions from purely speculative abductions by the criterion of causal unification. (shrink)

I propose an approach to naturalized philosophy of science that takes the social nature of scientific practice seriously. I criticize several prominent naturalistic approaches for adopting "cognitive individualism", which limits the study of science to an examination of the internal psychological mechanisms of scientists. I argue that this limits the explanatory capacity of these approaches. I then propose a three-level model of the social nature of scientific practice, and use the model to defend the claim that scientific knowledge is socially (...) produced. (shrink)

We respond to Morris and Richardson 's claim that Pickering and Chater's arguments about the lack of a relation between cognitive science and folk psychology are flawed. We note that possible controversies about the appropriate uses for the two terms do not affect our arguments. We then address their claim that computational explanation of knowledge-rich processes has proved possible in the domains of problem solving, scientific discovery, and reasoning. We argue that, in all cases, computational explanation is only possible for (...) aspects of those processes that do not make reference to general knowledge. We conclude that consideration of the issues raised by Morris and Richardson reinforces our original claim that there are two fundamentally distinct projects for understanding the mind, one based on justification, and the other on computational explanation, and that these apply to non-overlapping aspects of mental life. (shrink)

Epistemologists have debated at length whether scientific discovery is a rational and logical process. If it is, according to the Artificial Intelligence hypothesis, it should be possible to write computer programs able to discover laws or theories; and if such programs were written, this would definitely prove the existence of a logic of discovery. Attempts in this direction, however, have been unsuccessful: the programs written by Simon's group, indeed, infer famous laws of physics and chemistry; but having found no new (...) law, they cannot properly be considered discovery machines. The programs written in the Turing tradition, instead, produced new and useful empirical generalization, but no theoretical discovery, thus failing to prove the logical character of the most significant kind of discoveries. A new cognitivist and connectionist approach by Holland, Holyoak, Nisbett and Thagard, looks more promising. Reflection on their proposals helps to understand the complex character of discovery processes, the abandonment of belief in the logic of discovery by logical positivists, and the necessity of a realist interpretation of scientific research. (shrink)

High-level perception--”the process of making sense of complex data at an abstract, conceptual level--”is fundamental to human cognition. Through high-level perception, chaotic environmen- tal stimuli are organized into the mental representations that are used throughout cognitive pro- cessing. Much work in traditional artificial intelligence has ignored the process of high-level perception, by starting with hand-coded representations. In this paper, we argue that this dis- missal of perceptual processes leads to distorted models of human cognition. We examine some existing artificial-intelligence models--”notably (...) BACON, a model of scientific discovery, and the Structure-Mapping Engine, a model of analogical thought--”and argue that these are flawed pre- cisely because they downplay the role of high-level perception. Further, we argue that perceptu- al processes cannot be separated from other cognitive processes even in principle, and therefore that traditional artificial-intelligence models cannot be defended by supposing the existence of a --œrepresentation module--� that supplies representations ready-made. Finally, we describe a model of high-level perception and analogical thought in which perceptual processing is integrated with analogical mapping, leading to the flexible build-up of representations appropriate to a given context. (shrink)

Invention is an important source of technology, and, to understand invention, one needs a model system or prototype. A candidate model system is the hand tool. Using the hand tool as an example, the authors present a systematic approach to invention, dealing with description, classification, and joining or integrating simpler forms. Heuristics for when to integrate are presented. Finally, the authors introduce a new way of thinking about hand tools: simple spatial transformations applied to an abstract element make possible the (...) re-creation of a large number of tools. (shrink)

The paper locates, appreciates, and extends several dimensions of Simon’s work in the direction of more recent contributions by people such as Gigerenzer and Dennett. The author’s “crowbar model of method” is compared to Simon’s scissors metaphor. Against an evolutionary background, both support a pragmatic rather than strong realist approach to theoretically deep and complex problems. The importance of implicit knowledge is emphasized, for humans, as well as nonhuman animals. Although Simon was a realist in some respects, his work on (...) bounded rationality, satisficing, problem solving, heuristics, models, and scientific discovery mark him as a pragmatist. Indeed, he should be regarded as one of the great American pragmatists, alongside Peirce, James, Dewey, and a few others. (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 responsible for them. (shrink)

While the extended cognition (EC) thesis has gained more followers in cognitive science and in the philosophy of mind and knowledge, our main goal is to discuss a different area of significance of the EC thesis: its relation to philosophy of science. In this introduction, we outline two major areas: (I) The role of the thesis for issues in the philosophy of cognitive science, such as: How do notions of EC figure in theories or research programs in cognitive science? Which (...) versions of the EC thesis appear, and with which arguments to support them? (II) The potentials and limits of the EC thesis for topics in general philosophy of science, such as: Can naturalism perhaps be further advanced by means of the more recent EC thesis? Can we understand “big science” or laboratory research better by invoking some version of EC? And can the EC thesis help in overcoming the notorious cognitive/social divide in science studies? (shrink)

Fourteen subjects were tape‐recorded while they undertook to find a law to summarize numerical data they were given. The source of the data was not identified, nor were the variables labeled semantically. Unknown to the subjects, the data were measurements of the distances of the planets from the sun and the periods of their revolutions about it—equivalent to the data used by Johannes Kepler to discover his third law of planetary motion.Four of the 14 subjects discovered the same law as (...) Kepler did (the period varies as the 3/2 power of the distance), and a fifth came very close to the answer. The subiects' protocols provide a detailed picture of the problem‐solving searches they engaged in, which were mainly, but not exclusively, in the space of possible functions for fitting the data, and provide explanations as to why some succeeded and the others failed.The search heuristics used by the subjects are similar to those embodied in the BACON program, computer simulation of certain scientific discovery processes. The experiment demonstrates the feasibility of examining some of the processes of scientific discovery by recreating, in the laboratory, discovery situations of substantial historical relevance. It demonstrates also, that under conditions rather similar to those of the original discoverer, a law can be rediscovered by persons of ordinary intelligence (i.e., the intelligence needed for academic success in a good university). The data for the successful subjects reveal no “creative” processes in this kind of a discovery situation different from those that are regularly observed in all kinds of problem‐solving settings. (shrink)

In this paper I argue against the traditional viewthat in discovery processes there is no place forrational decisions. First I argue that some historicalprocesses in which an empirical law was developed,were rational. Second, I identify some of themethodological rules that we can follow in order to berational when constructing an empirical law. Finally,I argue that people who deny that scientific discoverycan be rational do not understand the nature ofmethodological rules.

Human and machine discovery are gradual problem-solving processes of searching large problem spaces for incompletely defined goal objects. Research on problem solving has usually focused on search of an instance space (empirical exploration) and a hypothesis space (generation of theories). In scientific discovery, search must often extend to other spaces as well: spaces of possible problems, of new or improved scientific instruments, of new problem representations, of new concepts, and others. This paper focuses especially on the processes for finding new (...) problem representations and new concepts, which are relatively new domains for research on discovery.Scientific discovery has usually been studied as an activity of individual investigators, but these individuals are positioned in a larger social structure of science, being linked by the blackboard of open publication (as well as by direct collaboration). Even while an investigator is working alone, the process is strongly influenced by knowledge and skills stored in memory as a result of previous social interaction. In this sense, all research on discovery, including the investigations on individual processes discussed in this paper, is social psychology, or even sociology. (shrink)

This study investigated the cognitive processes involved in inductive reasoning. Sixteen undergraduates solved quadratic function–finding problems and provided concurrent verbal protocols. Three fundamental areas of inductive activity were identified: Data Gathering, Pattern Finding, and Hypothesis Generation. These activities are evident in three different strategies that they used to successfully find functions. In all three strategies, Pattern Finding played a critical role not previously identified in the literature. In the most common strategy, called the Pursuit strategy, participants created new quantities from (...) x and y, detected patterns in these quantities, and expressed these patterns in terms of x. These expressions were then built into full hypotheses. The processes involved in this strategy are instantiated in an ACT‐based model that simulates both successful and unsuccessful performance. The protocols and the model suggest that numerical knowledge is essential to the detection of patterns and, therefore, to higher‐order problem solving. (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)

Abstract Theories of induction in psychology and artificial intelligence assume that the process leads from observation and knowledge to the formulation of linguistic conjectures. This paper proposes instead that the process yields mental models of phenomena. It uses this hypothesis to distinguish between deduction, induction, and creative forms of thought. It shows how models could underlie inductions about specific matters. In the domain of linguistic conjectures, there are many possible inductive generalizations of a conjecture. In the domain of models, however, (...) generalization calls for only a single operation: the addition of information to a model. If the information to be added is inconsistent with the model, then it eliminates the model as false: this operation suffices for all generalizations in a Boolean domain. Otherwise, the information that is added may have effects equivalent (a) to the replacement of an existential quantifier by a universal quantifier, or (b) to the promotion of an existential quantifier from inside to outside the scope of a universal quantifier. The latter operation is novel, and does not seem to have been used in any linguistic theory of induction. Finally, the paper describes a set of constraints on human induction, and outlines the evidence in favor of a model theory of induction. (shrink)

Essential to Peirce's distinction among three kinds of reasoning, deduction, induction and abduction, is the claim that each is correlated to a unique species of validity irreducible to that of the others. In particular, abductive validity cannot be analyzed in either deductive or inductive terms, a consequence of considerable importance for the logical and epistemological scrutiny of scientific methods. But when the full structure of abductive argumentation — as viewed by the mature Peirce — is clarified, every inferential step in (...) the process can be seen to dissolve into familiar forms of deductive and inductive reasoning. Specifically, the final stage is a special type of practical inference which, if correct, is deductively valid, while the creative phase, surprisingly, is not inferential at all. In neither is abduction a type of inference to the best explanation. The result is a major reassessment of the relevance of Peirce's views to contemporary methodological studies. (shrink)

A new approach to analyze scientific methods as patternsof state transitions is proposed and exemplified by the two mostimportant, general methods: induction and deduction. Though only`local' states of science are considered in this paper, includinghypotheses, data, approximation and degree of fit, the approach caneasily be extended to more comprehensive kinds of states. Two `pure'forms of induction are distinguished, enumerative and hypothesisconstruction induction. A combination of these two forms is proposedto yield a more adequate picture of induction. While the pure forms (...) ofinduction are clearly distinct from the deductive pattern, the patternof the combined form of induction is very similar to the latter. Thepresent account of scientific methods not only points out thedifferences between different methods but – in contrast to usualdiscussions of methodology – also clarifies what they have in common. (shrink)

Science, like any other cognitive activity, is grounded in the sensorimotor interaction of our bodies with the environment. Human embodiment thus constrains the class of scientific concepts and theories which are accessible to us. The paper explores the possibility of doing science with artificial cognitive agents, in the framework of an interactivist-constructivist cognitive model of science. Intelligent robots, by virtue of having different sensorimotor capabilities, may overcome the fundamental limitations of human science and provide important technological innovations. Mathematics and nanophysics (...) are prime candidates for being studied by artificial scientists. (shrink)

Abduction is considered the most powerful, but also the most controversially discussed type of inference. Based on an analysis of Peirce’s retroduction, Lipton’s Inference to the Best Explanation and other theories, a new theory of abduction is proposed. It considers abduction not as intrinsically explanatory but as intrinsically conditional: for a given fact, abduction allows one to infer a fact that implies it. There are three types of abduction: Selective abduction selects an already known conditional whose consequent is the given (...) fact and infers that its antecedent is true. Conditional-creative abduction creates a new conditional in which the given fact is the consequent and a defined fact is the antecedent that implies the given fact. Propositional-conditional-creative abduction assumes that the given fact is implied by a hitherto undefined fact and thus creates a new conditional with a new proposition as antecedent. The execution of abductive inferences is specified by theory-specific patterns. Each pattern consists of a set of rules for both generating and justifying abductive conclusions and covers the complete inference process. Consequently, abductive inferences can be formalised iff the whole pattern can be formalised. The empirical consistency of the proposed theory is demonstrated by a case study of Semmelweis' research on puerperal fever. (shrink)

Research on the nature of science and science education enjoys a longhistory, with its origins in Ernst Mach's work in the late nineteenthcentury and John Dewey's at the beginning of the twentieth century.As early as 1909 the Central Association for Science and MathematicsTeachers published an article – ‘A Consideration of the Principles thatShould Determine the Courses in Biology in Secondary Schools’ – inSchool Science and Mathematics that reflected foundational concernsabout science and how school curricula should be informed by them. Sincethen (...) a large body of literature has developed related to the teaching andlearning about nature of science – see, for example, the Lederman and Meichtry reviews cited below. As well there has been intensephilosophical, historical and philosophical debate about the nature of scienceitself, culminating in the much-publicised ‘Science Wars’ of recent time. Thereferences listed here primarily focus on the empirical research related to thenature of science as an educational goal; along with a few influential philosophicalworks by such authors as Kuhn, Popper, Laudan, Lakatos, and others. Whilenot exhaustive, the list should prove useful to educators, and scholars in otherfields, interested in the nature of science and how its understanding can berealised as a goal of science instruction. The authors welcome correspondenceregarding omissions from the list, and on-going additions that can be made to it. (shrink)

In the twentieth century, no person epitomized more dramatically the “Renaissance mind” than Herbert A. Simon (1916–2001). In aworking life spanning over 60 years, Simon made seminal contributions to administrative theory, axiomatic foundations of physics, economics, sociology, econometrics, cognitive psychology, logic of scientific discovery, and artificial intelligence. Simon's life of the mind, thus, affords nothing less than a “laboratory” in which to observe and examine at close quarters the phenomenon ofmultidisciplinary creativity. In this paper, we attempt to shed some light (...) on the nature of Simon's creativity and the nature of his particular Renaissance mind. In particular, we have attempted here to articulate thecognitive styleunderlying Simon's multidisciplinary creativity. (shrink)

New computer systems of discovery create a research program for logic and philosophy of science. These systems consist of inference rules and control knowledge that guide the discovery process. Their paths of discovery are influenced by the available data and the discovery steps coincide with the justification of results. The discovery process can be described in terms of fundamental concepts of artificial intelligence such as heuristic search, and can also be interpreted in terms of logic. The traditional distinction that places (...) studies of scientific discovery outside the philosophy of science, in psychology, sociology, or history, is no longer valid in view of the existence of computer systems of discovery. It becomes both reasonable and attractive to study the schemes of discovery in the same way as the criteria of justification were studied: empirically as facts, and logically as norms. (shrink)

The instrumentalist argument from the underdetermination of theories by data runs as follows: (1) every theory has empirically equivalent rivals; (2) the only warrant for believing one theory over another is its possession of a greater measure of empirical virtue; (3) therefore belief in any theory is arbitrary. In this paper, I examine the status of the first premise. Several arguments against the universal availability of empirically equivalent theoretical rivals are criticized, and four algorithms for producing empirically equivalent rivals are (...) defended. I conclude that the case for the first premise of the argument from underdetermination is very strong. The disposition of the argument itself depends on the fate of the second premise. (shrink)

This paper attempts to establish a systems-semiotic framework explaining creativity in the design process, where the design process is considered to have as its basis the cognitive process. The design process is considered as the interaction between two or more cognitive systems resulting in a purposeful and ongoing transformation of their already complex representational structures and the production of newer ones, in order to fulfill an ill-defined goal. Creativity is considered as the result of an emergence of organizational complexity in (...) each cognitive system participating in the design process, while it is trying to purposefully incorporate new constraints in its meaning structures. The meanings generated in each system are identified as the contingent and anticipatory content of its representations, and where self-organization is the dominant process in which they are continuously involved. Furthermore, Peircean semiotic processes appear to provide the functionality needed by the emergent representational structures in order to complete the cycle of a creative design process. Creativity originates in the abductive stage of the semiotic process, the fallible nature of which is maintained in the proposed framework by the fact that the respective emergent representations can be misfits. The nodal points of the framework are identified and analyzed showing that a cognitive system needs the whole interactive anticipatory cycle in order to engage in a creative design process. (shrink)

Cognitive neuroscience is the branch of neuroscience that studies the neural mechanisms underpinning cognition and develops theories explaining them. Within cognitive neuroscience, computational neuroscience focuses on modeling behavior, using theories expressed as computer programs. Up to now, computational theories have been formulated by neuroscientists. In this paper, we present a new approach to theory development in neuroscience: the automatic generation and testing of cognitive theories using genetic programming (GP). Our approach evolves from experimental data cognitive theories that explain “the mental (...) program” that subjects use to solve a specific task. As an example, we have focused on a typical neuroscience experiment, the delayed-match-to-sample (DMTS) task. The main goal of our approach is to develop a tool that neuroscientists can use to develop better cognitive theories. (shrink)

Speaking: from Intention to Articulation Willem J. M. Levelt, 1989 (1993 paperback) Cambridge, MA: MIT Press ISBN: 0–262–12137–9(hb), 0–262–62089–8(pb)Rules for Reasoning Richard E. Nisbett (Ed.), 1993 Hillsdale, NJ, Lawrence Erlbaum Associates ISBN: 0–8058–1256–3(hb), 0–8085–1257–1 (pb)Readings in Philosophy and Cognitive Science Alvin I. Goldman, 1993 Cambridge, MA, MIT Press ISBN: 0–262–07153–3(hb), 0–262–57100–5(pb)Language Comprehension in Ape and Child, Monographs of the Society for Research in Child Development, Serial No. 233, Vol. 58, Nos 3–4 Sue Savage‐Rumbaugh, Jeannine Murphy, Rose A. Sevcik, Karen E. (...) Brakke, Shelly L. Williams & Duane M. Rumbaugh, with commentary by Elizabeth Bates, and a reply by the authors, 1993 Chicago: University of Chicago Press ISBN: 0–226–73542–7Language, Music and Mind Diana Raffman, 1993 Cambridge, MIT Press/Bradford Books ISBN: 0–262–18150–9Psychology of Proof Lance J. Rips, 1994 Cambridge, MA, MIT Press ISBN: 0–262–18153–3Human and Machine Thinking P. Johnson‐Laird, 1993 Hillsdale, NJ, Erlbaum ISBN: 0–8058–0921‐XIntelligent Behavior in Animals and Robots David McFarland & Thomas Bosser, 1993 Cambridge, MA and London, UK: MIT Press/ Bradford Books ISBN: 0–262–13293–1The Eye and the Mind: Reflections on Perception and the Problem of Knowledge Charles Landesman, 1993 Dordrecht: Kluwer Academic Publishers ISBN: 0–7923–2586–9The Moral Self GilG. Noam & Thomas E. Wren (Eds), in cooperation with Gertud Nunner‐Winkler & Wolfgang Edelstein, 1993 Cambridge, MA: MIT Press ISBN: 0–262–14052–1The Logic of Discovery: A Theory of the Rationality of Scientific Research Scott A. Kleiner, 1993 Dordrecht: Kluwer Academic Publishers ISBN: 0–7923–2371–8Folk Psychology and the Philosophy of Mind S.M. Christenson & D.R. Turner (Eds), 1993 Hillsdale, NJ: Lawrence Erlbaum ISBN: 0–805–80931–7. (shrink)

Currently there is hardly any connection between philosophy of science and Artificial Intelligence research. We argue that both fields can benefit from each other. As an example of this mutual benefit we discuss the relation between Inductive-Statistical Reasoning and Default Logic. One of the main topics in AI research is the study of common-sense reasoning with incomplete information. Default logic is especially developed to formalise this type of reasoning. We show that there is a striking resemblance between inductive-statistical reasoning and (...) default logic. A central theme in the logical positivist study of inductive-statistical reasoning such as Hempels Criterion of Maximal Specificity turns out to be equally important in default logic. We also discuss to what extent the relevance of the results of Logical Positivism to AI research could contribute to a reevaluation of Logical Positivism in general. (shrink)

Heuristics are often invoked in the philosophical, psychological, and cognitive science literatures to describe or explain methodological techniques or "shortcut" mental operations that help in inference, decision-making, and problem-solving. Yet there has been surprisingly little philosophical work done on the nature of heuristics and heuristic reasoning, and a close inspection of the way(s) in which "heuristic" is used throughout the literature reveals a vagueness and uncertainty with respect to what heuristics are and their role in cognition. This dissertation seeks to (...) remedy this situation by motivating philosophical inquiry into heuristics and heuristic reasoning, and then advancing a theory of how heuristics operate in cognition. I develop a positive working characterization of heuristics that is coherent and robust enough to account for a broad range of phenomena in reasoning and inference, and makes sense of empirical data in a systematic way. I then illustrate the work this characterization does by considering the sorts of problems that many philosophers believe heuristics solve, namely those resulting from the so-called frame problem. Considering the frame problem motivates the need to gain a better understanding of how heuristics work and the cognitive structures over which they operate. I develop a general theory of cognition which I argue underwrites the heuristic operations that concern this dissertation. I argue that heuristics operate over highly organized systems of knowledge, and I offer a cognitive architecture to accommodate this view. I then provide an account of the systems of knowledge that heuristics are supposed to operate over, in which I suggest that such systems of knowledge are concepts. The upshot, then, is that heuristics operate over concepts. I argue, however, that heuristics do not operate over conceptual content, but over metainformational relations between activated and primed concepts and their contents. Finally, to show that my thesis is empirically adequate, I consider empirical evidence on heuristic reasoning and argue that my account of heuristics explains the data. (shrink)

Wisdom: its nature, origins and development Robert J. Sternberg, Ed., 1990 Cambridge, Cambridge University Press 339 pp.Does Science Compute? Computational Models of Scientific Discovery and Theory Formation Jeff Shrager & Pat Langley, Eds. 1990 San Mateo, CA, Morgan Kaufman Publishers xi +498 pp.Arguing and Thinking: a rhetorical approach to social psychology Michael Billig, 1987 Cambridge, Cambridge University Press, 290 pp.The Absent Body Drew Leder, 1990 Chicago, IL, University of Chicago Press. x+218 pp., US$34.95 hardback, US $14.95 paperback.

Pickering and Chater (P&C) maintain that folk psychology and cognitive science should neither compete nor cooperate. Each is an independent enterprise, with a distinct subject matter and characteristic modes of explanation. P&C''s case depends upon their characterizations of cognitive science and folk psychology. We question the basis for their characterizations, challenge both the coherence and the individual adequacy of their contrasts between the two, and show that they waver in their views about the scope of each. We conclude that P&C (...) do not so muchdiscover ascreate the gap they find between folk psychology and cognitive science. It is an artifact of their implausible and unmotivated attempt to demarcate the two areas, and of the excessively narrow accounts they give of each. (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)

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)