[full article, abstract in English; abstract in Lithuanian] The article examines the modern computer-based educational environment and the requirements of the possible cognitive interface that enables the learner’s cognitive grounding by incorporating abductive reasoning into the educational process. Although the main emphasis is on cognitive and physiological aspects, the practical tools for enabling computational thinking in a modern constructionist educational environment are discussed. The presented analytical material and developed solutions are aimed at education with computers. However, the proposed solutions can (...) be generalized in order to create a computer-free educational environment. The generalized paradigm here is pragmatism, considered as a philosophical assumption. By designing and creating a pragmatist educational environment, a common way of organizing computational thinking that enables constructionist educational solutions can be found. (shrink)

These preprints were automatically compiled into a PDF from the collection of papers deposited in PhilSci-Archive in conjunction with the PSA 2018.

A cognitive ethnography of how bioengineering scientists create innovative modeling methods. In this first full-scale, long-term cognitive ethnography by a philosopher of science, Nancy J. Nersessian offers an account of how scientists at the interdisciplinary frontiers of bioengineering create novel problem-solving methods. Bioengineering scientists model complex dynamical biological systems using concepts, methods, materials, and other resources drawn primarily from engineering. They aim to understand these systems sufficiently to control or intervene in them. What Nersessian examines here is how cutting-edge bioengineering (...) scientists integrate the cognitive, social, material, and cultural dimensions of practice. Her findings and conclusions have broad implications for researchers in philosophy, science studies, cognitive science, and interdisciplinary studies, as well as scientists, educators, policy makers, and funding agencies. In studying the epistemic practices of scientists, Nersessian pushes the boundaries of the philosophy of science and cognitive science into areas not ventured before. She recounts a decades-long, wide-ranging, and richly detailed investigation of the innovative interdisciplinary modeling practices of bioengineering researchers in four university laboratories. She argues and demonstrates that the methods of cognitive ethnography and qualitative data analysis, placed in the framework of distributed cognition, provide the tools for a philosophical analysis of how scientific discoveries arise from complex systems in which the cognitive, social, material, and cultural dimensions of problem-solving are integrated into the epistemic practices of scientists. Specifically, she looks at how interdisciplinary environments shape problem-solving. Although Nersessian’s case material is drawn from the bioengineering sciences, her analytic framework and methodological approach are directly applicable to scientific research in a broader, more general sense, as well. (shrink)

Imagination is necessary for scientific practice, yet there are no in vivo sociological studies on the ways that imagination is taught, thought of, or evaluated by scientists. This article begins to remedy this by presenting the results of a qualitative study performed on two systems biology laboratories. I found that the more advanced a participant was in their scientific career, the more they valued imagination. Further, positive attitudes toward imagination were primarily due to the perceived role of imagination in problem-solving. (...) But not all problem-solving episodes involved clear appeals to imagination, only maximally specific problems did. This pattern is explained by the presence of an implicit norm governing imagination use in the two labs: only use imagination on maximally specific problems, and only when all other available methods have failed. This norm was confirmed by the participants, and I argue that it has epistemological reasons in its favour. I also found that its strength varies inversely with career stage, such that more advanced scientists do (and should) occasionally bring their imaginations to bear on more general problems. A story about scientific pedagogy explains the trend away from (and back to) imagination over the course of a scientific career. Finally, some positive recommendations are given for a more imagination-friendly scientific pedagogy. (shrink)

Scientific models share one central characteristic with fiction: their relation to the physical world is ambiguous. It is often unclear whether an element in a model represents something in the world or presents an artifact of model building. Fiction, too, can resemble our world to varying degrees. However, we assign a different epistemic function to scientific representations. As artifacts of human activity, how are scientific representations allowing us to make inferences about real phenomena? In reply to this concern, philosophers of (...) science have started analyzing scientific representations in terms of fictionalization strategies. Many arguments center on a dyadic relation between the model and its target system, focusing on structural resemblances and “as if” scenarios. This chapter provides a different approach. It looks more closely at model building to analyze the interpretative strategies dealing with the representational limits of models. How do we interpret ambiguous elements in models? Moreover, how do we determine the validity of model-based inferences to information that is not an explicit part of a representational structure? I argue that the problem of ambiguous inference emerges from two features of representations, namely their hybridity and incompleteness. To distinguish between fictional and non-fictional elements in scientific models my suggestion is to look at the integrative strategies that link a particular model to other methods in an ongoing research context. To exemplify this idea, I examine protein modeling through X-ray crystallography as a pivotal method in biochemistry. (shrink)

Recently, some philosophers of science (e.g., Gürol Irzik, Michael Friedman) have challenged the ‘received view’ on the relationship between Rudolf Carnap and Thomas Kuhn, suggesting that there is a close affinity (rather than opposition) between their philosophical views. In support of this argument, these authors cite Carnap and Kuhn’s similar views on incommensurability, theory-choice, and scientific revolutions. Against this revisionist view, I argue that the philosophical relationship between Carnap and Kuhn should be regarded as opposed rather than complementary. In particular, (...) I argue that a consideration of the fundamentally disparate nature of the broader philosophical projects of Carnap (logic of science) and Kuhn (providing a theory of scientific revolutions)renders the alleged similarities between their views superficial in comparison to their fundamental differences. In defense of the received view, I suggest that Carnap and Kuhn are model representatives of two contrasting styles of doing philosophy of science, viz., logical analysis and historical analysis respectively. This analysis clarifies the role played by Kuhn’s Structure of Scientific Revolutions in the demise of logical empiricism in the second half of the twentieth-century. (shrink)

This is the introduction to the Routledge Companion to Thought Experiments.

How do scientific models represent in a way that enables us to discover new truths about reality and draw inferences about it? Contemporary accounts of scientific discovery answer this question by focusing on the cognitive mechanisms involved in the generation of new ideas and concepts in terms of a special sort of reasoning—or model-based reasoning—involving imagery. Alternatively, I argue that answering this question requires that we recognise the crucial role of the propositional imagination in the construction and development of models (...) for the purpose of generating hypotheses that are plausible candidates for truth. I propose simple fictionalism as a new account of models as Waltonian games of make-believe and suggest that models can lead to genuine scientific discovery when they are used as representations that denote real world phenomena and generate two main kinds of theoretical hypotheses, model-world comparisons and direct attributions. (shrink)

This book contains a selection of the papers presented at the Logic, Reasoning and Rationality 2010 conference in Ghent. The conference aimed at stimulating the use of formal frameworks to explicate concrete cases of human reasoning, and conversely, to challenge scholars in formal studies by presenting them with interesting new cases of actual reasoning. According to the members of the Wiener Kreis, there was a strong connection between logic, reasoning, and rationality and that human reasoning is rational in so far (...) as it is based on logic. Later, this belief came under attack and logic was deemed inadequate to explicate actual cases of human reasoning. Today, there is a growing interest in reconnecting logic, reasoning and rationality. A central motor for this change was the development of non-classical logics and non-classical formal frameworks. The book contains contributions in various non-classical formal frameworks, case studies that enhance our apprehension of concrete reasoning patterns, and studies of the philosophical implications for our understanding of the notions of rationality. (shrink)

We contend that diagrams are tools not only for communication but also for supporting the reasoning of biologists. In the mechanistic research that is characteristic of biology, diagrams delineate the phenomenon to be explained, display explanatory relations, and show the organized parts and operations of the mechanism proposed as responsible for the phenomenon. Both phenomenon diagrams and explanatory relations diagrams, employing graphs or other formats, facilitate applying visual processing to the detection of relevant patterns. Mechanism diagrams guide reasoning about how (...) the parts and operations work together to produce the phenomenon and what experiments need to be done to improve on the existing account. We examine how these functions are served by diagrams in circadian rhythm research. (shrink)

Although philosophy has been only a minor contributor to cognitive science to date, this paper describes two projects in naturalistic philosophy of mind and one in naturalistic philosophy of science that have been pursued during the past 30 years and that can make theoretical and methodological contributions to cognitive science. First, stances on the mind–body problem (identity theory, functionalism, and heuristic identity theory) are relevant to cognitive science as it negotiates its relation to neuroscience and cognitive neuroscience. Second, analyses of (...) mental representations address both their vehicles and their contents; new approaches to characterizing how representations have content are particularly relevant to understanding the relation of cognitive agents to their environments. Third, the recently formulated accounts of mechanistic explanation in philosophy of science both provide perspective on the explanatory project of cognitive science and may offer normative guidance to cognitive science (e.g., by providing perspective on how multiple disciplinary perspectives can be integrated in understanding a given mechanism). (shrink)

The project of integrated HPS has occupied philosophers of science in one form or another since at least the 1960s. Yet, despite this substantial interest in bringing together philosophical and historical reflections on the nature of science, history of science and formal philosophy of science remain as divided as ever. In this paper, I will argue that the continuing separation between historical and formal philosophy of science is ill-founded. I will argue for this in both abstract and concrete terms. At (...) the abstract level, I reconstruct two possible arguments for the incompatibility of historical and formal philosophy of science and argue that they are both wanting. At the concrete level, I discuss how historical and formal philosophy of science have been brought together in practice, namely: in the form of a largely forgotten research tradition that I will refer to here as the study of formalized macro-units. After a brief exposition, I argue that this research tradition has been unduly overlooked by historically minded philosophers of science. Bringing together these observations, I argue that the divide between historical and formal philosophy of science is not grounded in any substantive arguments, but can be primarily attributed to disciplinary happenstance. (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)

Psychometrics is one of the main approaches to social scientific measurement. It is relied upon in drug testing, economic policymaking, recruitment, and other decision-making contexts. The first aim of this article is to introduce philosophers to key aspects of psychometrics, namely, classical test theory, item response theory, and construct validity. The second aim is to show how a debate on the nature of psychological attributes manifests in psychometrics. In this debate, realists claim that psychometric measures are indicators of independently existing (...) qualities, while operationalists argue that a measure defines its target attribute. The third and final aim is to argue that despite its poor reputation among philosophers, operationalism is a viable approach to psychometrics. (shrink)

Metaphors abound in both the arts and in science. Due to the traditional division between these enterprises as one concerned with aesthetic values and the other with epistemic values there has unfortunately been very little work on the relation between metaphors in the arts and sciences. In this paper, we aim to remedy this omission by defending a continuity thesis regarding the function of metaphor across both domains, that is, metaphors fulfill any of the same functions in science as they (...) do in the arts. Importantly, this involves the claim that metaphors in arts as well as science have both epistemic and aesthetic functions. (shrink)

When Johann and Daniel Bernoulli founded fluid dynamics they encountered several problems. To go beyond the vision of Newtonian particles, a new set of images was needed in order to deal with the spatial extensibility and lack of form of fluids. I point to evidence that analogy was an essential abductive strategy in the creation of this imagery. But its heuristic behavior is complex: analogy can provide an initial model or proto-model that establishes the starting point of a theoretical process, (...) but it can play other roles as well. The historical genesis analyzed here shows that the participation of analogy in physicists’ creativity is not so restricted and that its richness opens up the field for very different roles and strategies in model-based discovery processes. Analogies can crop up intermittently in the evolution of a theory; and they can cooperate with images, extreme case reasoning and thought experiments, and even activate these processes at origin. Although it may seem that the contributions of analogy are generative in the sense of helping to discover new aspects of reality, we must stress the evaluative function that sometimes is performed by analogical reasoning in order to gain confidence. The study of the Bernoulli’s genesis of the foundations of fluid dynamics generates interesting hypotheses about the multiple roles that analogy can play in scientific 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)

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)

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 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)

This paper defends the deflationary character of two recent views regarding scientific representation, namely RIG Hughes’ DDI model and the inferential conception. It is first argued that these views’ deflationism is akin to the homonymous position in discussions regarding the nature of truth. There, we are invited to consider the platitudes that the predicate “true” obeys at the level of practice, disregarding any deeper, or more substantive, account of its nature. More generally, for any concept X, a deflationary approach is (...) then defined in opposition to a substantive approach, where a substantive approach to X is an analysis of X in terms of some property P, or relation R, accounting for and explaining the standard use of X. It then becomes possible to characterize a deflationary view of scientific representation in three distinct senses, namely: a “no-theory” view, a “minimalist” view, and a “use-based” view – in line with three standard deflationary responses in the philosophical literature on truth. It is then argued that both the DDI model and the inferential conception may be suitably understood in any of these three different senses. The application of these deflationary ‘hermeneutics’ moreover yields significant improvements on the DDI model, which bring it closer to the inferential conception. It is finally argued that what these approaches have in common – the key to any deflationary account of scientific representation – is the denial that scientific representation may be ultimately reduced to any substantive explanatory property of sources, or targets, or their relations. (shrink)

Sometimes we learn through the use of imagination. The epistemology of imagination asks how this is possible. One barrier to progress on this question has been a lack of agreement on how to characterize imagination; for example, is imagination a mental state, ability, character trait, or cognitive process? This paper argues that we should characterize imagination as a cognitive ability, exercises of which are cognitive processes. Following dual process theories of cognition developed in cognitive science, the set of imaginative processes (...) is then divided into two kinds: one that is unconscious, uncontrolled, and effortless, and another that is conscious, controlled, and effortful. This paper outlines the different epistemological strengths and weaknesses of the two kinds of imaginative process, and argues that a dual process model of imagination helpfully resolves or clarifies issues in the epistemology of imagination and the closely related epistemology of thought experiments. (shrink)

Computational systems biologists create and manipulate computational models of biological systems, but they do not always have straightforward epistemic access to the content and behavioural profile of such models because of their length, coding idiosyncrasies, and formal complexity. This creates difficulties both for modellers in their research groups and for their bioscience collaborators who rely on these models. In this paper we introduce a new kind of visualization that was developed to address just this sort of epistemic opacity. The visualization (...) is unusual in that it depicts the dynamics and structure of a computer model instead of that model’s target system, and because it is generated algorithmically. Using considerations from epistemology and aesthetics, we explore how this new kind of visualization increases scientific understanding of the content and function of computer models in systems biology to reduce epistemic opacity. (shrink)

Paul Thagard has recently argued that thought experiments are dangerous and misleading when we try to use them as evidence for claims. This paper refutes his skepticism. Building on Thagard’s own work in cognitive science, I suggest that Thagard has much that is positive to say about how thought experiments work. My last section presents some new directions for research on the intersection between thought experiments and cognitive science.

When new theoretical terms are introduced into scientific discourse, prevailing accounts imply, analytic or semantic truths come along with them, by way of either definitions or reference-fixing descriptions. But there appear to be few or no analytic truths in scientific theory, which suggests that the prevailing accounts are mistaken. This paper looks to research on the psychology of natural kind concepts to suggest a new account of the introduction of theoretical terms that avoids both definition and reference-fixing description. At the (...) core of the account is a novel psychological process that I call introjection. (shrink)

There has been little investigation to date of the way metacognition is involved in conceptual change. It has been recognised that analytic metacognition is important to the way older children acquire more sophisticated scientific and mathematical concepts at school. But there has been barely any examination of the role of metacognition in earlier stages of concept acquisition, at the ages that have been the major focus of the developmental psychology of concepts. The growing evidence that even young children have a (...) capacity for procedural metacognition raises the question of whether and how these abilities are involved in conceptual development. More specifically, are there developmental changes in metacognitive abilities that have a wholescale effect on the way children acquire new concepts and replace existing concepts? We show that there is already evidence of at least one plausible example of such a link and argue that these connections deserve to be investigated systematically. (shrink)

Computer simulation and thought experiments seem to produce knowledge about the world without intervening in the world. This has called for a comparison between the two methods. However, Chandrasekharan et al. argue that the nature of contemporary science is too complex for using TEs. They suggest CS as the tool for contemporary sciences and conclude that it will replace TEs. In this paper, by discussing a few TEs from the history of science, I show that the replacement thesis about TE (...) is a failure. The paper is divided into three sections. The first section discusses the arguments of Chandrasekharan et al. and demonstrates the three distinct aspects of the replacement thesis. The second section examines the argument against TE and shows that they are inadequate to prove the withering of TE from science. The third section discusses Albert Einstein’s Magnet and Conductor TE and demonstrates that replacing such TE with CS yield no advantage. (shrink)

In many fields of biology, both the phenomena to be explained and the mechanisms proposed to explain them are commonly presented in diagrams. Our interest is in how scientists construct such diagrams. Researchers begin with evidence, typically developed experimentally and presented in data graphs. To arrive at a robust diagram of the phenomenon or the mechanism, they must integrate a variety of data to construct a single, coherent representation. This process often begins as the researchers create a first sketch, and (...) it continues over an extended period as they revise the sketch until they arrive at a diagram they find acceptable. We illustrate this process by examining the sketches developed in the course of two research projects directed at understanding the generation of circadian rhythms in cyanobacteria. One identified a new aspect of the phenomenon itself, whereas the other aimed to develop a new mechanistic account. In both cases, the research resulted in a paper in which the conclusion was presented in a diagram that the authors deemed adequate to convey it. These diagrams violate some of the normative “cognitive design principles” advanced by cognitive scientists as constraints on successful visual communication. We suggest that scientists’ sketching is instead governed by norms of success that are broadly explanatory: conveying the phenomenon or mechanism. (shrink)

This article proposes a conceptual mapping to outline salient properties and relations that allow for a knowledge transfer from the well-established greenwashing phenomenon to the more recent machinewashing. We account for relevant dissimilarities, indicating where conceptual boundaries may be drawn. Guided by a “reasoning by analogy” approach, the article addresses the structural analogy and machinewashing idiosyncrasies leading to a novel and theoretically informed model of machinewashing. Consequently, machinewashing is defined as a strategy that organizations adopt to engage in misleading behavior (...) about ethical Artificial Intelligence /algorithmic systems. Machinewashing involves misleading information about ethical AI communicated or omitted via words, visuals, or the underlying algorithm of AI itself. Furthermore, and going beyond greenwashing, machinewashing may be used for symbolic actions such as lobbying and prevention of stricter regulation. By outlining diverse theoretical foundations of the established greenwashing domain and their relation to specific research questions, the article proposes a machinewashing model and a set of theory-related research questions on the macro, meso, and micro-level for future machinewashing research. We conclude by stressing limitations and by outlining practical implications for organizations and policymakers. (shrink)

Among others, the term problem plays a major role in the various attempts to characterize interdisciplinarity or transdisciplinarity, as used synonymously in this paper. Interdisciplinarity is regarded as problem solving among science, technology and society and as problem orientation beyond disciplinary constraints. The point of departure of this paper is that the discourse and practice of ID have problems with the problem. The objective here is to shed some light on the vague notion of problem in order to advocate a (...) specific type of interdisciplinarity: problem-oriented interdisciplinarity. The outline is as follows: Taking an ex negativo approach, I will show what problem-oriented ID does not mean. Using references to well-established distinctions in philosophy of science, I will show three other types of ID that should not be placed under the umbrella term problem-oriented ID : object-oriented ID, theory-oriented ID, and method-oriented ID. Different philosophical thought traditions can be related to these distinguishable meanings. I will then clarify the notion of problem by looking at three systematic elements: an undesired state, a desired state, and the barriers in getting from the one to the other. These three elements include three related kinds of knowledge: systems, target, and transformation knowledge. This paper elaborates further methodological and epistemological elements of problem-oriented ID. It concludes by stressing that problem-oriented ID is the most needed as well as the most challenging type of ID. (shrink)

We investigate the context of discovery of two significant achievements of twentieth century biochemistry: the chemiosmotic mechanism of oxidative phosphorylation and the dark reaction of photosynthesis. The pursuit of these problems involved discovery strategies such as the transfer, recombination and reversal of previous causal and mechanistic knowledge in biochemistry. We study the operation and scope of these strategies by careful historical analysis, reaching a number of systematic conclusions: even basic strategies can illuminate “hard cases” of scientific discovery that go far (...) beyond simple extrapolation or analogy; the causal–mechanistic approach to discovery permits a middle course between the extremes of a completely substrate-neutral and a completely domain-specific view of scientific discovery; the existing literature on mechanism discovery underemphasizes the role of combinatorial approaches in defining and exploring search spaces of possible problem solutions; there is a subtle interplay between a fine-grained mechanistic and a more coarse-grained causal level of analysis, and both are needed to make discovery processes intelligible. (shrink)

We investigate the context of discovery of two significant achievements of twentieth century biochemistry: the chemiosmotic mechanism of oxidative phosphorylation and the dark reaction of photosynthesis. The pursuit of these problems involved discovery strategies such as the transfer, recombination and reversal of previous causal and mechanistic knowledge in biochemistry. We study the operation and scope of these strategies by careful historical analysis, reaching a number of systematic conclusions: even basic strategies can illuminate “hard cases” of scientific discovery that go far (...) beyond simple extrapolation or analogy; the causal–mechanistic approach to discovery permits a middle course between the extremes of a completely substrate-neutral and a completely domain-specific view of scientific discovery; the existing literature on mechanism discovery underemphasizes the role of combinatorial approaches in defining and exploring search spaces of possible problem solutions; there is a subtle interplay between a fine-grained mechanistic and a more coarse-grained causal level of analysis, and both are needed to make discovery processes intelligible. (shrink)

This article looks at recent work in cognitive science on mathematical cognition from the perspective of history and philosophy of mathematical practice. The discussion is focused on the work of Lakoff and Núñez, because this is the first comprehensive account of mathematical cognition that also addresses advanced mathematics and its history. Building on a distinction between mathematics as it is presented in textbooks and as it presents itself to the researcher, it is argued that the focus of cognitive analyses of (...) historical developments of mathematics has been primarily on the former, even if they claim to be about the latter. (shrink)

Biological sciences have strived to adopt the conceptual framework of physics and have become increasingly quantitatively oriented, aiming to refute the assertion that biology appears unquantifiable, unpredictable, and messy. But despite all effort, biology is characterized by a paucity of quantitative statements with universal applications. Nonetheless, many biological disciplines—most notably molecular biology—have experienced an ascendancy over the last 50 years. The underlying core concepts and ideas permeate and inform many neighboring disciplines. This surprising success is probably not so much attributable (...) to mathematical and statistical approaches in molecular biology, but rather to the preponderance of qualitative approaches, especially visualization. Visualizations can be afforded by quantitative research, but usually they rely on both, quantitative and qualitative research. I claim the following three features to be responsible for the unceasing zeal for using visualizations: visual representations facilitate reasoning, images can be cognitively processed in a “fast” manner, and abstractions of visual representations prompt conceptual advances. In summary, visualizations have largely contributed to the success of molecular biology by conveying its concepts to other disciplines, and at the same time, eclipsing mere quantitative approaches. However, visualizations also bear the risk of misinterpretation when traversing neighboring disciplines and even more so when pervading nonscientific domains. (shrink)

A popular trend in the sciences of the mind is to understand cognition as embodied, embedded, enactive, ecological, and so on. While some of the work under the label of “embodied cognition” takes for granted key commitments of traditional cognitive science, other projects coincide in treating embodiment as the starting point for an entirely different way of investigating all of cognition. Focusing on the latter, this paper discusses how embodied cognitive science can be made more reflexive and more sensitive to (...) the implications that our views of cognition have for how we understand scientific practice, including our own theorizing about cognition. Inspired by the “strong programme” in the sociology of scientific knowledge, I explore the prospect of an analogously “strong” program in embodied cognitive science. I first draw from Dewey’s transactional notion of “situation” to identify a broad sense in which embodied cognitive science takes cognition, as an embodied phenomenon, to be _situated_. I then sketch a perspective I call _situated reflexivity_, which extends the Deweyan analysis to understand scientific practice in the same terms, and thereby illustrates what research in line with a strong program in embodied cognitive science can look like. This move, I propose, has the potential of setting up a new inquiry situation that makes more salient the embodiment of scientific practice and that, through this, can help organize our own embodied cognitive activities as we try to make sense of scientific work, including our own. (shrink)

An intuitive view is that creativity involves bringing together what is already known and familiar in a way that produces something new. In cognitive science, this intuition is typically formalized in terms of computational processes that combine or associate internally represented information. From this computationalist perspective, it is hard to imagine how non-representational approaches in embodied cognitive science could shed light on creativity, especially when it comes to abstract conceptual reasoning of the kind scientists so often engage in. The present (...) article offers an entry point to addressing this challenge. The scientific project of embodied cognitive science is a continuation of work in the functionalist tradition in psychology developed over a century ago by William James and John Dewey, among others. The focus here is on how functionalist views on the nature of mind, thought, and experience offer an alternative starting point for cognitive science in general, and for the cognitive science of scientific creativity in particular. The result may seem paradoxical. On the one hand, the article claims that the functionalist conceptual framework motivates rejecting mainstream cognitive views of creativity as the combination or association of ideas. On the other hand, however, the strategy adopted here—namely, revisiting ideas from functionalist psychology to inform current scientific theorizing—can itself be described as a process of arriving at new, creative ideas from combinations of old ones. As is shown here, a proper understanding of cognition in light of the functionalist tradition resolves the seeming tension between these two claims. (shrink)

We outline a framework for analyzing episodes from the history of science in which the application of mathematics plays a constitutive role in the conceptual development of empirical sciences. Our starting point is the inferential conception of the application of mathematics, recently advanced by Bueno and Colyvan. We identify and discuss some systematic problems of this approach. We propose refinements of the inferential conception based on theoretical considerations and on the basis of a historical case study. We demonstrate the usefulness (...) of the refined, dynamical inferential conception using the well-researched example of the genesis of general relativity. Specifically, we look at the collaboration of the physicist Einstein and the mathematician Grossmann in the years 1912--1913, which resulted in the jointly published ``Outline of a Generalized Theory of Relativity and a Theory of Gravitation,'' a precursor theory of the final theory of general relativity. In this episode, an independently developed mathematical theory, the theory of differential invariants and the absolute differential calculus, was applied in the process of physical theorizing aiming at finding a relativistic theory of gravitation. We argue that the dynamical inferential conception not only provides a natural framework to describe and analyze this episode, but it also generates new questions and insights. We comment on the mathematical tradition on which Grossmann drew, and on his own contributions to mathematical theorizing. We argue that the dynamical inferential conception allows us to identify both the role of heuristics and of mathematical resources as well as the systematic role of problems and mistakes in the reconstruction of episodes of conceptual innovation and theory change. (shrink)