This paper introduces coupled clustering—a novel computational framework for detecting corresponding themes in unstructured data. Gaining its inspiration from the structure mapping theory, our framework utilizes unsupervised statistical learning tools for automatic construction of aligned representations reflecting the context of the particular mapping being made. The coupled clustering algorithm is demonstrated and evaluated through detecting conceptual correspondences in textual corpora. In its current phase, the method is primarily oriented towards context-dependent feature-based similarity. However, it is preliminary demonstrated how it could (...) be utilized for identification of relational commonalities, as well. (shrink)

In this article I am going to reconstruct Stephen Toulmin’s procedural theory of concepts and explanations in order to develop two overlooked ideas from his philosophy of science: methods of representations and inferential techniques. I argue that these notions, when properly articulated, could be useful for shedding some light on how scientific reasoning is related to representational structures, concepts, and explanation within scientific practices. I will explore and illustrate these ideas by studying the development of the notion of instantaneous speed (...) during the passage from Galileo’s geometrical physics to analytical mechanics. At the end, I will argue that methods of representations could be considered as constitutive of scientific inference; and I will show how these notions could connect with other similar ideas from contemporary philosophy of science like those of models and model-based reasoning. (shrink)

The important role of mathematical representations in scientific thinking has received little attention from cognitive scientists. This study argues that neglect of this issue is unwarranted, given existing cognitive theories and laws, together with promising results from the cognitive historical analysis of several important scientists. In particular, while the mathematical wizardry of James Clerk Maxwell differed dramatically from the experimental approaches favored by Michael Faraday, Maxwell himself recognized Faraday as “in reality a mathematician of a very high order,” and his (...) own work as in some respects a re‐representation of Faraday’s field theory in analytic terms. The implications of the similarities and differences between the two figures open new perspectives on the cognitive role of mathematics as a learned mode of representation in science. (shrink)

: In 1856, Michael Faraday (1791–1867) conducted nearly a year's worth of research on the optical properties of gold, in the course of which he discovered the first metallic colloids. Following our own discovery of hundreds of the specimens prepared by Faraday for this research, the present paper describes the cognitive role of these "epistemic artifacts" in the dynamics of Faraday's research practices. Analysis of the specimens, Faraday's Diary records, and replications of selected procedures (partly to replace missing kinds of (...) specimens and partly to understand the "tacit knowledge" implicated in Faraday's research) are outlined, and a reconstruction of the events surrounding the initial discovery of metallic colloids is presented. (shrink)

The term conceptual simulation refers to a type of everyday reasoning strategy commonly called “what if” reasoning. It has been suggested in a number of contexts that this type of reasoning plays an important role in scientific discovery; however, little direct evidence exists to support this claim. This article proposes that conceptual simulation is likely to be used in situations of informational uncertainty, and may be used to help scientists resolve that uncertainty. We conducted two studies to investigate the relationship (...) between conceptual simulation and informational uncertainty. Study 1 was an in vivo study of expert scientists; the results suggest that scientists do use conceptual simulation in situations of informational uncertainty, and that they use conceptual simulation to make inferences from their data using the analogical reasoning process of alignment by similarity detection. Study 2 experimentally manipulated experts' level of uncertainty and provides further support for the hypothesis that conceptual simulation is more likely to be used in situations of informational uncertainty. Finally, we discuss the relationship between conceptual simulation and other types of reasoning using qualitative mental models. (shrink)

Many scientific discoveries have depended on external diagrams or visualizations. Many scientists also report to use an internal mental representation or mental imagery to help them solve problems and reason. How do scientists connect these internal and external representations? We examined working scientists as they worked on external scientific visualizations. We coded the number and type of spatial transformations (mental operations that scientists used on internal or external representations or images) and found that there were a very large number of (...) comparisons, either between different visualizations or between a visualization and the scientists’ internal mental representation. We found that when scientists compared visualization to visualization, the comparisons were based primarily on features. However, when scientists compared a visualization to their mental representation, they were attempting to align the two representations. We suggest that this alignment process is how scientists connect internal and external representations. (shrink)

In part because “imagination” is a slippery notion, its exact role in the production of scientific knowledge remains unclear. There is, however, one often explicit and deliberate use of imagination by scientists that can be (and has been) studied intensively by epistemologists and historians of science: thought experiments. The main goal of this article is to document the varieties of thought experimentation, not so much in terms of the different sciences in which they occur but rather in terms of the (...) different functions they fulfil. I argue that thought experimentation (and hence imagination) plays a role not only in theory choice but in singular causal analysis and scientific discovery as well. I pinpoint, moreover, some of the rules governing the use of thought experiments in theory choice and in singular causal analysis, that is, some of the criteria they should meet in order to fulfil those functions successfully. (shrink)

Can a set of musical metaphors in a treatise on ethics reveal something about the nature and source of moral autonomy? This article argues that it can. It shows how metaphorical usage of words like tone, pitch, and concord in Adam Smith's Theory of Moral Sentiments can be understood as elements of an analogical model for morality. What this model tells us about morality depends on how we conceptualise music. In contrast to earlier interpretations of Smith's metaphors that have seen (...) music as an aesthetic object, this article sees music as a practice. Understood in this way, the analogy allows us to see morality too as a practice––as moral tuning. This in turn reveals a novel answer to the intractable problem of conventionalism: moral autonomy consists in the freedom inherent in the constant need to interpret and reinterpret the strictly formal ideal of perfect propriety. (shrink)

Science is a form of distributed analysis involving both individual work that produces new knowledge and collaborative work to exchange information with the larger community. There are many particular ways in which individual and community can interact in science, and it is difficult to assess how efficient these are, and what the best way might be to support them. This paper reports on a series of experiments in this area and a prototype implementation using a research platform called CACHE. CACHE (...) both supports experimentation with different structures of interaction between individual and community cognition and serves as a prototype for computational support for those structures. We particularly focus on CACHE‐BC, the Bayes community version of CACHE, within which the community can break up analytical tasks into “mind‐sized” units and use provenance tracking to keep track of the relationship between these units. (shrink)

This paper analyzes three contrasting strategies for modeling intentional agency in contemporary analytic philosophy of mind and action, and draws parallels between them and similar strategies of scientific model-construction. Gricean modeling involves identifying primitive building blocks of intentional agency, and building up from such building blocks to prototypically agential behaviors. Analogical modeling is based on picking out an exemplary type of intentional agency, which is used as a model for other agential types. Theoretical modeling involves reasoning about intentional agency in (...) terms of some domain-general framework of lawlike regularities, which involves no detailed reference to particular building blocks or exemplars of intentional agency. Given the contrasting procedural approaches that they employ and the different types of knowledge that they embody, the three strategies are argued to provide mutually complementary perspectives on intentional agency. (shrink)

Critics of contemporary metaphysics argue that it attempts to do the hard work of science from the ease of the armchair. Physics, not metaphysics, tells us about the fundamental facts of the world, and empirical psychology is best placed to reveal the content of our concepts about the world. Exploring and understanding the world through metaphysical reflection is obsolete. In this paper, I will show why this critique of metaphysics fails, arguing that metaphysical methods used to make claims about the (...) world are similar to scientific methods used to make claims about the world, but that the subjects of metaphysics are not the subjects of science. Those who argue that metaphysics uses a problematic methodology to make claims about subjects better covered by natural science get the situation exactly the wrong way around: metaphysics has a distinctive subject matter, not a distinctive methodology. The questions metaphysicians address are different from those of scientists, but the methods employed to develop and select theories are similar. In the first section of the paper, I will describe the sort of subject matter that metaphysics tends to engage with. In the second section of the paper, I will show how metaphysical theories are classes of models and discuss the roles of experience, common sense and thought experiments in the construction and evaluation of such models. Finally, in the last section I will discuss the way these methodological points help us to understand the metaphysical project. Getting the right account of the metaphysical method allows us to better understand the relationship between science and metaphysics, to explain why doing metaphysics successfully involves having a range of different theories, to understand the role of thought experiments involving fictional worlds, and to situate metaphysical realism in a scientifically realist context. (shrink)

This paper explains how Kepler in his ``War onMars'' applied systems of models organized bothin a perspectival and in a stratifiedconceptual sense. With the help of thesesystems Kepler worked out successively moredeterminate models for the planetary orbits.Along the way he discovered the Keplerian lawsas consequences of the distance rule, hisleading regulative principle. The selection ofdecisive, so called privileged, observations,as well as the determinate geometrical andkinematical description of the phenomena,result from the application of this principleto the developing of models. Kepler's method (...) ofdiscovery may therefore appropriately bedesignated converse abduction. (shrink)

People frequently report that, at times, their thought has a vocal character. Thinking commonly appears to be accompanied or constituted by silently ‘talking’ to oneself in inner speech. In this paper, we explore the specifically epistemic role of inner speech in conscious reasoning. A plausible position—but one I argue is ultimately wrong—is that inner speech plays asolelyfacilitative role that is exhausted by (i) serving as the vehicle of representation for conscious reasoning, and/or (ii) allowing one to focus on certain types (...) of objects or relations, e.g., causal relations, abstracta, counterfactuals, etc., or to consciously entertain structured propositional contents that it would be hard (or impossible) to focus on or entertain with representations in other (e.g., imagistic) formats. According to this position, inner speech doesn't figure as a justificatory element in our reasoning or as the partial epistemic basis of our conclusions—it merely facilitates reasoning through (i) and/or (ii). In contrast to the view that inner speech is amerefacilitator, I establish that (outside of potentially playing roles (i) and/or (ii)) the language we use itself serves as a crucial source of information in reasoning. In other words, we reason from propositions about the language we use in inner speech as opposed to exclusively reasoning from the semantic contents of the speech. My conclusion follows from how we use language as a cognitive tool to keep track of information, e.g., the contents of premises, lemmas, previous reasoning results, etc., in reasoning. (shrink)

In some of the papers in which she develops and defends the mental modelview of thought experiments in physics, Nersessian expresses the belief that her account has implications for thought experiments in other domains as well. In this paper, I argue, firstly, that counterfactual reasoning has a legitimate place in historical inquiry, and secondly, that the mental model view can account for such "alternative histories". I proceed as follows. Firstly, I review the main accounts of thought experiments in physics and (...) point at some explanatory advantages of the mental model view. Subsequently, I argue that historians cannot dispense with counterfactual reasoning altogether and qualify a number of principled objections against the explicit use of alternative histories for theoretical purposes. Finally, I show that the mental model view can account for such thought experiments in history. (shrink)

Philosophers of science today by and large reject the cataclysmic and irrational interpretation of the scientific enterprise claimed by Kuhn. Many computational models have been implemented to rationally study the conceptual change in science. In this recent tradition a key role is played by the concept of abduction as a mechanism by which new explanatory hypotheses are introduced. Nevertheless some problems in describing the most interesting abductive issues rise from the classical computational approach. It describes a cognitive process (and so (...) abduction) by the manipulation of internal symbolic representations of external world. This view assumes a discrete set of representations fixed in discrete time jumps, and cannot adequately account for the issue of anticipation and causation of a new hypothesis. An integration of the traditional computational view with some ideas developed inside the so-called dynamical approach can suggest some important insights. The concept of attractor is very significant. It permits a description of the abductive generation of new hypotheses in terms of a catastrophic rearrangement of the parameters responsible for the behavior of the system. (shrink)

Our brains make up a series of signs and are engaged in making or manifesting or reacting to a series of signs: through this semiotic activity they are at the same time engaged in “being minds” and so in thinking intelligently. An important effect of this semiotic activity of brains is a continuous process of “externalization of the mind” that exhibits a new cognitive perspective on the mechanisms underling the semiotic emergence of abductive processes of meaning formation. To illustrate this (...) process I will take advantage of the analysis of some aspects of the cognitive interplay between internal and external representations. I consider this interplay critical in analyzing the relation between meaningful semiotic internal resources and devices and their dynamical interactions with the externalized semiotic materiality suitably stocked in the environment. Hence, minds are material, “extended” and artificial in themselves. A considerable part of human abductive thinking is occurring through an activity consisting in a kind of reification in the external environment and a subsequent re-projection and reinterpretation through new configurations of neural networks and chemical processes. I also illustrate how this activity takes advantage of hybrid representations and how it can nicely account for various processes of creative and selective abduction, bringing up the question of how multimodal aspects involving a full range of sensory modalities are important in hypothetical reasoning. (shrink)

What I call theoretical abduction (sentential and model-based)certainly illustrates much of what is important in abductive reasoning, especially the objective of selecting and creating a set of hypotheses that are able to dispense good (preferred) explanations of data, but fails to account for many cases of explanation occurring in science or in everyday reasoning when the exploitation of the environment is crucial. The concept of manipulative abduction is devoted to capture the role of action in many interesting situations: action provides (...) otherwise unavailable information that enables the agent to solve problems by starting and performing a suitable abductive process of generation or selection of hypotheses. Many external things, usually inert from the epistemological point of view, can be transformed into what I call epistemic mediators, which are illustrated in the last part of the paper, together with an analysis of the related notions of ``perceptual and inceptual rehearsal'' and of ``external representation''. (shrink)

In her book Abductive Reasoning Atocha Aliseda stresses the attention to the logical models of abduction, centering on the semantic tableaux as a method for extending and improving both the whole cognitive/philosophical view on it and on other more restricted logical approaches. I will describe the importance of increasing logical knowledge on abduction also taking advantage of some ideas coming from the so-called distributed cognition where logical models are seen as forms of cognitive externalizations of preexistent in-formal human reasoning performances.

Computational philosophy (CP) aims at investigating many important concepts and problems of the philosophical and epistemological tradition in a new way by taking advantage of information-theoretic, cognitive, and artificial intelligence methodologies. I maintain that the results of computational philosophy meet the classical requirements of some Peircian pragmatic ambitions. Indeed, more than a 100 years ago, the American philosopher C.S. Peirce, when working on logical and philosophical problems, suggested the concept of pragmatism(pragmaticism, in his own words) as a logical criterion to (...) analyze what words and concepts express through their practical meaning. Many words have been spent on creative processes and reasoning, especially in the case of scientific practices. In fact, many philosophers have usually offered a number of ways of construing hypotheses generation, but they aim at demonstrating that the activity of generating hypotheses is paradoxical, obscure, and thus not analyzable. Those descriptions are often so far from Peircian pragmatic prescription and so abstract to result completely unknowable and obscure. To dismiss this tendency and gain interesting insight about the so-called logic of scientific discovery we need to build constructive procedures, which could play a role in moving the problem-solving process forward by implementing them in some actual models. The computational turn gives us a new way to understand creative processes in a strictly pragmatic sense. In fact, by exploiting artificial intelligence and cognitive science tools, computational philosophy allows us to test concepts and ideas previously conceived only in abstract terms. It is in the perspective of these actual computational models that I find the central role of abduction in the explanation of creative reasoning in science. I maintain that the computational philosophy analysis of model-based and manipulative abduction and of external and epistemic mediators is important not only to delineate the actual practice of abduction, but also to further enhance the development of programs computationally adequate in rediscovering, or discovering for the first time, for example, scientific hypotheses or mathematical theorems. The last part of the paper is devoted to illustrating the problem of the extra-theoretical dimension of reasoning and discovery from the perspective of some mathematical cases derived from calculus and geometry. (shrink)

Philosophers have usually offered a number of ways of describing hypotheses generation, but all aim at demonstrating that the activity of generating hypotheses is paradoxical, illusory or obscure, and then not analysable. Those descriptions are often so far from Peircian pragmatic prescription and so abstract to result completely unknowable and obscure. The “computational turn” gives us a new way to understand creative processes in a strictly pragmatic sense. In fact, by exploiting artificial intelligence and cognitive science tools, computational philosophy allows (...) us to test concepts and ideas previously conceived only in abstract terms. It is in the perspective of these actual computational models that I find the central role of abduction in the explanation of creative reasoning in science. Creativity and discovery are no more seen as a mysterious irrational process, but, thanks to constructive accounts, as a complex relationship among different inferential steps that can be clearly analysed and identified. I maintain that the computational philosophy analysis of model-based and manipulative abduction and of external and epistemic mediators is important not only to delineate the actual practice of abduction, but also to further enhance the development of programs computationally adequate in rediscovering, or discovering for the first time, for example, scientific hypotheses or mathematical theorems. (shrink)

What I call theoretical abduction (sentential and model-based) certainly illustrates much of what is important in abductive reasoning, especially the objective of selecting and creating a set of hypotheses that are able to dispense good (preferred) explanations of data, but fails to account for many cases of explanations occurring in science or in everyday reasoning when the exploitation of the environment is crucial. The concept of manipulative abduction is devoted to capture the role of action in many interesting situations: action (...) provides otherwise unavailable information that enables the agent to solve problems by starting and performing a suitable abductive process of generation or selection of hypotheses. Many external things, usually inert from the epistemological point of view, can be transformed into what I call epistemic mediators, which are illustrated in the last part of the paper, together with an analysis of the related notion of external representation . Finally, some examples of computational programs that simulate geometrical reasoning are illustrated. The computational embodiment generates a kind of squared epistemic mediator: geometrical construction, as an example of epistemic mediator, is further mediated. (shrink)

This paper discerns two types of mathematization, a foundational and an explorative one. The foundational perspective is well-established, but we argue that the explorative type is essential when approaching the problem of applicability and how it influences our conception of mathematics. The first part of the paper argues that a philosophical transformation made explorative mathematization possible. This transformation took place in early modernity when sense acquired partial independence from reference. The second part of the paper discusses a series of examples (...) from the history of mathematics that highlight the complementary nature of the foundational and exploratory types of mathematization. (shrink)

There is a demand to nurture scientific creativity in science education. This paper proposes that the relevant conceptual infrastructure with which to teach scientific creativity is often already included in philosophy of science courses, even those that do not cover scientific creativity explicitly. More precisely, it is shown how paradigm theory can serve as a framework with which to introduce the differences between combinational, exploratory, and transformational creativity in science. Moreover, the types of components given in Kuhn’s disciplinary matrix are (...) argued to indicate a further subdivision within transformational creativity that makes explicit that this most radical type of creativity that aims to go beyond and thus to transform the current paradigm can take many different directions. More generally, it is argued that there are several synergies between the topic of scientific creativity and paradigm theory that can be utilized in most philosophy of science courses at relative ease. Doing so should promote the understanding of scientific creativity among students, provide another way to signify the relevance of paradigm theory, and more strategically be a way of reinforcing the place of philosophy of science in science education. (shrink)

In recent years, the philosophical focus of the modeling literature has shifted from descriptions of general properties of models to an interest in different model functions. It has been argued that the diversity of models and their correspondingly different epistemic goals are important for developing intelligible scientific theories. However, more knowledge is needed on how a combination of different epistemic means can generate and stabilize new entities in science. This paper will draw on Rheinberger’s practice-oriented account of knowledge production. The (...) conceptual repertoire of Rheinberger’s historical epistemology offers important insights for an analysis of the modelling practice. I illustrate this with a case study on network modeling in systems biology where engineering approaches are applied to the study of biological systems. I shall argue that the use of multiple means of representations is an essential part of the dynamic of knowledge generation. It is because of – rather than in spite of – the diversity of constraints of different models that the interlocking use of different epistemic means creates a potential for knowledge production. (shrink)

Traditional frameworks for evaluating scientific models have tended to downplay their exploratory function; instead they emphasize how models are inherently intended for specific phenomena and are to be judged by their ability to predict, reproduce, or explain empirical observations. By contrast, this paper argues that exploration should stand alongside explanation, prediction, and representation as a core function of scientific models. Thus, models often serve as starting points for future inquiry, as proofs of principle, as sources of potential explanations, and as (...) a tool for reassessing the suitability of the target system. This is illustrated by a case study of the varied career of reaction-diffusion models in the study of biological pattern formation, which was initiated by Alan Turing in a classic 1952 paper. Initially regarded as mathematically elegant, but biologically irrelevant, demonstrations of how, in principle, spontaneous pattern formation could occur in an organism, such Turing models have only recently rebounded, thanks to advances in experimental techniques and computational methods. The long-delayed vindication of Turing’s initial model, it is argued, is best explained by recognizing it as an exploratory tool. (shrink)

It is usually claimed that in order to assess a thought experiment we should assess the nomological possibility, or realizability in principle, of its scenario. This is undoubtedly true for many TEs, such as Bohr’s reply to Einstein’s photon box. Nevertheless, in some cases, such as Maxwell’s demon, this requirement should be relaxed. Many accounts of TEs fail in this regard. In particular, experimental and some mental model accounts are too strict, since they always require realizability in principle. This paper (...) aims at analysing the notion of possibility at play in the scenarios of scientific TEs, and sheds some new light on their nature and function. (shrink)

This contribution provides an assessment of the epistemological role of scientific models. The prevalent view that all scientific models are representations of the world is rejected. This view points to a unified way of resolving epistemic issues for scientific models. The emerging consensus in philosophy of science that models have many different epistemic roles in science is presented and defended.

In some of the papers in which she develops and defends the mental modelview of thought experiments in physics, Nersessian expresses the belief that her account has implications for thought experiments in other domains as well. In this paper, I argue, firstly, that counterfactual reasoning has a legitimate place in historical inquiry, and secondly, that the mental model view can account for such "alternative histories". I proceed as follows. Firstly, I review the main accounts of thought experiments in physics and (...) point at some explanatory advantages of the mental model view. Subsequently, I argue that historians cannot dispense with counterfactual reasoning altogether and qualify a number of principled objections against the explicit use of alternative histories for theoretical purposes. Finally, I show that the mental model view can account for such thought experiments in history. (shrink)

. Philosophers and scientists often cite ontic factors when explaining the methods and success of scientific inquiry. That is, the adoption of a method or approach is explained in reference to the kind of system in which the scientist is interested: these are explanations of why scientists do what they do, that appeal to properties of their target systems. We present a framework for understanding such “Opticks to his Principia. Newton’s optical work is largely experiment-driven, while the Principia is primarily (...) mathematical, so usually, each work is taken to exemplify a different kind of science. However, Newton himself often presented them in terms of a largely consistent method. We use our framework to articulate an original and plausible position: that the differences between the Opticks and the Principia are due to the kinds of systems targeted. That is, we provide an ontic-driven explanation of methodological differences. We suspect that ontic factors should have a more prominent role in historical explanations of scientific method and development. (shrink)

Most philosophical accounts of scientific models assume that models represent some aspect, or some theory, of reality. They also assume that interpretation plays only a supporting role. This paper challenges both assumptions. It proposes that models can be used in science to interpret reality. (a) I distinguish these interpretative models from representational ones. They find new meanings in a target system’s behaviour, rather than fit its parts together. They are built through idealisation, abstraction and recontextualisation. (b) To show how interpretative (...) models work, I offer a case study on the scientific controversy over foetal pain. It highlights how pain scientists use conflicting models to interpret the human foetus and its behaviour, and thereby to support opposing claims about whether the foetus can feel pain. (c) I raise a sceptical worry and a methodological challenge for interpretative models. To address the latter, I use my case study to compare how interpretative and representational models ought to be evaluated. (shrink)

According to a recent philosophical claim, “works of fiction are thought experiments” (Elgin 2007: 47), though there are relevant differences, as the role of spoilers shows—they can ruin a novel but improve the understanding we can gain through a thought experiment. In the present article I will analyze the role of spoilers and argue for a more differentiated perspective on the relation between literature and thought experiments. I will start with a short discussion of different perspectives on thought experiments and (...) argue that the mental-model view and the conception of games of make-believe are most promising for developing the present analogy. Then I will assess the similarities and differences between thought experiments and other works of fiction. I will focus on the role of spoilers and, more generally, on the foretaste context, of which they are part. This context guides readers of literary works of art to draw their attention to the literary and aesthetic quality of the text. In the case of thought experiments, on the other hand, it (i) prompts them to accept the presence of fictional elements in worldly-cognitive works and (ii) draws their attention towards cognitively relevant elements of the story. A discussion of Borges’ Pierre Menard in the last part will show that literary works of art become thought experiments if they are embedded in an appropriate foretaste context. Spoilers, thus, unveil that even works which—due to their length or plenty of detail—usually are not considered thought experiments, can perform similar cognitive functions. (shrink)

This inaugural handbook documents the distinctive research field that utilizes history and philosophy in investigation of theoretical, curricular and pedagogical issues in the teaching of science and mathematics. It is contributed to by 130 researchers from 30 countries; it provides a logically structured, fully referenced guide to the ways in which science and mathematics education is, informed by the history and philosophy of these disciplines, as well as by the philosophy of education more generally. The first handbook to cover the (...) field, it lays down a much-needed marker of progress to date and provides a platform for informed and coherent future analysis and research of the subject. -/- The publication comes at a time of heightened worldwide concern over the standard of science and mathematics education, attended by fierce debate over how best to reform curricula and enliven student engagement in the subjects There is a growing recognition among educators and policy makers that the learning of science must dovetail with learning about science; this handbook is uniquely positioned as a locus for the discussion. -/- The handbook features sections on pedagogical, theoretical, national, and biographical research, setting the literature of each tradition in its historical context. Each chapter engages in an assessment of the strengths and weakness of the research addressed, and suggests potentially fruitful avenues of future research. A key element of the handbook’s broader analytical framework is its identification and examination of unnoticed philosophical assumptions in science and mathematics research. It reminds readers at a crucial juncture that there has been a long and rich tradition of historical and philosophical engagements with science and mathematics teaching, and that lessons can be learnt from these engagements for the resolution of current theoretical, curricular and pedagogical questions that face teachers and administrators. (shrink)

The central aim of science is to make sense of the world. To move forward as a community endeavor, sense-making must be systematic and focused. The question then is how do scientists actually experience the sense-making process? In this chapter we examine the “practice turn” in science studies and in particular how as a result of this turn scholars have come to realize that models are the “functional unit” of scientific thought and form the center of the reasoning/sense-making process. This (...) chapter will explore a context-dependent view of models and modeling in science. From this analysis we present a framework for delineating the different aspects of model-based reasoning and describe how this view can be useful in educational settings. This framework highlights how modeling supports and focuses scientific practice on sense-making. (shrink)

This thesis studies the role of conceptual content in inference and reasoning. The first two chapters offer a theoretical and historical overview of the relation between inference and meaning in philosophy and psychology. In particular, a critical analysis of the formality thesis, i.e., the idea that rational inference is a rule-based and topic-neutral mechanism, is advanced. The origins of this idea in logic and its influence in philosophy and cognitive psychology are discussed. Chapter 3 consists of an analysis of the (...) relationship between inference and representation. It is argued that inference has to be studied from a pluralistic per- spective due to its dependence on different formats of representing information. The following four chapters apply conceptual spaces, a formal theory of concepts within cognitive semantics, to three concept-based inference-types. First, an explication of Sellars notion of material inference is advanced. Later, the model is extended to account for nonmonotonic inference by studying the role expectations in reasoning. Finally, a conceptual space-model of category-based induction is presented. This model predicts most of the empirical properties of this psychological phenomenon and subsumes some of the previous theories in psychology. It is stated that the explanatory fruitfulness of this new approach is evidence for the failure of the formality thesis and calls for a unified model of rational inference that puts semantics at center stage. The last chapter of the thesis discusses how inference and concepts interact in scientific reasoning, which makes constant use of hybrid symbolic structures for representing conceptual information. Stephen Toulmin’s notions of method of representation and inferential technique are developed and applied in a case study about the emergence of the notion of instantaneous speed during the passage from geometrical physics to analytical mechanics. It is claimed that this analysis provides support to the pluralistic perspective for theorizing about reasoning. (shrink)

A ciência é feita das escolhas de seus protagonistas e como tal, repleta de subjetividade. Uma teoria científica é uma suposição explicativa e negar a influência da imaginação como agente ativo na construção do conhecimento seria no mínimo ingenuidade. Embora a ciência possua regras bem definidas, seu método se limita a obtenção e tratamento de dados. O surgimento da ideia ou da hipótese inicial é fruto do salto intuitivo da livre imaginação humana. A Experimentação Mental é o processo de empregar (...) situações imaginárias para ajudar-nos a entender ou prever de que maneira as coisas podem se comportar na realidade. Assim como os Experimentos Concretos os Experimentos Mentais são uma ferramenta essencial na construção do conhecimento científico. Tais situações imaginárias são parte importante da argumentação dos cientistas na busca do convencimento de seus pares, servindo também como ferramenta didática. Neste trabalho, através da revisão de algumas das principais referências sobre este conceito, buscaremos analisar questões epistemológicas envolvidas. Afinal o que são Experimentos Mentais? Será que eles podem nos fornecer uma fonte de conhecimentos do mundo natural? De onde vem esse conhecimento? O objetivo principal desta análise é buscar padrões de interpretação através de categorias de definições apresentadas por pontos de vista significativos. Diferentes autores discutem os limites e potencialidades dos Experimentos Mentais, baseado em suas considerações propomos aqui cinco categorias distintas para sua compreensão: Processo de Re-contextualização, Intuições Platônicas, Argumentos Pitorescos, Experimentos e Manipulação de Modelos Mentais. Palavras-chave: Experimentos mentais. Gedanken experiment. Epistemologia. (shrink)

This paper provides the first systematic philosophical analysis of an increasingly important part of modern scientific practice: analogue quantum simulation. We introduce the distinction between `simulation' and `emulation' as applied in the context of two case studies. Based upon this distinction, and building upon ideas from the recent philosophical literature on scientific understanding, we provide a normative framework to isolate and support the goals of scientists undertaking analogue quantum simulation and emulation. We expect our framework to be useful to both (...) working scientists and philosophers of science interested in cutting-edge scientific practice. (shrink)