Recently, various philosophers have argued that we can obtain knowledge via the imagination. In particular, it has been suggested that we can come to know concrete, empirical matters of everyday significance by appropriately imagining relevant scenarios. Arguments for this thesis come in two main varieties: black box reliability arguments and constraints-based arguments. We suggest that both strategies are unsuccessful. Against black-box arguments, we point to evidence from empirical psychology, question a central case-study, and raise concerns about a (claimed) evolutionary rationale (...) for the imagination's reliability. Against the constraints-based account, we argue that to the extent that it works, this does not give rise to knowledge that is distinctively from the imagination. We conclude by suggesting that the imagination's role in raising possibilities, traditionally seen as part of the context of discovery, can in fact play a role in justification, including as a bulwark against certain sorts of skepticism. (shrink)

The term ‘imagination’ may seem harmless. We talk about imagination all the time. Nonetheless, I will argue that we should treat it with suspicion. More precisely, I will argue that the explanatory power of the concept of imagination can be fully captured by a scientifically more respectable and more powerful concept, namely, the concept of mental imagery.

Scientists imagine for epistemic reasons, and these imaginings can be better or worse. But what does it mean for an imagining to be epistemically better or worse? There are at least three metaepistemological frameworks that present different answers to this question: epistemological consequentialism, deontic epistemology, and virtue epistemology. This paper presents empirical evidence that scientists adopt each of these different epistemic frameworks with respect to imagination, but argues that the way they do this is best explained if scientists are fundamentally (...) epistemic consequentialists about imagination. (shrink)

Theoretical models are widely held as sources of knowledge of reality. Imagination is vital to their development and to the generation of plausible hypotheses about reality. But how can imagination, which is typically held to be completely free, effectively instruct us about reality? In this paper I argue that the key to answering this question is in constrained uses of imagination. More specifically, I identify make-believe as the right notion of imagination at work in modelling. I propose the first overarching (...) taxonomy of types of constraints on scientific imagination that enables knowledge of reality. And I identify two main kinds of knowledge enabled by models, knowledge of the imaginary scenario specified by models and knowledge of reality. (shrink)

The epistemic value of models has traditionally been approached from a representational perspective. This paper argues that the artifactual approach evades the problem of accounting for representation and better accommodates the modal dimension of modeling. From an artifactual perspective, models are viewed as erotetic vehicles constrained by their construction and available representational tools. The modal dimension of modeling is approached through two case studies. The first portrays mathematical modeling in economics, while the other discusses the modeling practice of synthetic biology, (...) which exploits and combines models in various modes and media. Neither model intends to represent any actual target system. Rather, they are constructed to study possible mechanisms through the construction of a model system with built-in dependencies. (shrink)

Just as bodily actions are things you do with your body, mental actions are things you do with your mind. Both are different from things that merely happen to you. Where does the idea of mental action come from? What are mental actions? And why do they matter in philosophy? These are the three main questions answered in this paper. Section 1 introduces mental action through a brief history of the topic in philosophy. Section 2 explains what it is to (...) be a mental action in terms of intentional action. Section 3 argues that the fact that we can perform mental actions has some striking consequences concerning mental content. Section 4 shows how mental action helps to explain some important facts about self-knowledge and the normativity of thought. The conclusion mentions two further fruitful avenues for philosophical research on mental action. (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)

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)

The scientist’s toolkit counts at least three practices: real, thought and numerical experiments. Although a deep investigation of the relationships between these types of experiments should shed light on the nature of scientific enquiry, I argue that it has been compromised by at least four factors: (i) a bias for the epistemological superiority of real experiments; (ii) an almost exclusive focus on the links between either thought or numerical experiments, and real experiments; (iii) a tendency to try and reduce one (...) kind to another; and (iv) an excessive attention to the outputs of these types of experiments, more than to their processes. In this paper I support an unbiased triangular comparative analysis that focuses on the processes involved in real, thought and numerical experiments, and claim that all three types of experimentation are fundamental to scientific research. I do so by clarifying different notions of experimental processes, and by introducing a distinction between two varieties of mental simulation that play a role in them (i.e., mental models and imaginings). I then compare real, thought and numerical experiments in light of this distinction, showing their similarities, but also fundamental differences, which suggest that none of them is dispensable. (shrink)

This work interprets the notion of thought experiment (TE) from the viewpoint of a functional reading of the a priori, that is, an a priori that is devoid of any particular content. It is true that Kant ascribes some content to the (synthetic) a priori, but only a functional reading of the a priori agrees with the spirit of Kant's philosophy and can be used for developing a consistent account of TEs. On the basis of this concept of the a (...) priori, an account is developed that mediates between Brown and Norton by considering TEs from two distinct but connected points of view: transcendental and operational. On the one hand, the account is transcendental in a very usual sense of the word: TEs are the condition of the possibility of real world experiments (REs) because, without the a priori capacity of the mind to reason counterfactually, we could not devise any hypothesis and would be unable to plan the corresponding RE that should test it. This capacity underpins the distinction in principle — a properly transcendental distinction — between TEs and REs. But while there is a distinction in principle between TE and RE on the transcendental level of the a priori capacity of the mind to reason counterfactually, the exclusively func¬tional character of the a priori entails that, on the operational-methodological level, TEs —considering their empirical content and actual performance within a discipline — are and function essentially like REs. (shrink)

What role does the imagination play in scientific progress? After examining several studies in cognitive science, I argue that one thing the imagination does is help to increase scientific understanding, which is itself indispensable for scientific progress. Then, I sketch a transcendental justification of the role of imagination in this process.

The recent discussion on scientific representation has focused on models and their relationship to the real world. It has been assumed that models give us knowledge because they represent their supposed real target systems. However, here agreement among philosophers of science has tended to end as they have presented widely different views on how representation should be understood. I will argue that the traditional representational approach is too limiting as regards the epistemic value of modelling given the focus on the (...) relationship between a single model and its supposed target system, and the neglect of the actual representational means with which scientists construct models. I therefore suggest an alternative account of models as epistemic tools. This amounts to regarding them as concrete artefacts that are built by specific representational means and are constrained by their design in such a way that they facilitate the study of certain scientific questions, and learning from them by means of construction and manipulation. (shrink)

Metaphors are powerful communicative tools because they produce ”framing effects’. These effects are especially palpable when the metaphor is an insult that denigrates the hearer or someone he cares about. In such cases, just comprehending the metaphor produces a kind of ”complicity’ that cannot easily be undone by denying the speaker’s claim. Several theorists have taken this to show that metaphors are engaged in a different line of work from ordinary communication. Against this, I argue that metaphorical insults are rhetorically (...) powerful because they combine perspectives, presupposition, and pragmatics in the service of speech acts with assertoric force. (shrink)

The act of understanding is at the heart of all scientific activity; without it any ostensibly scientific activity is as sterile as that of a high school student substituting numbers into a formula. Ordinary language often uses visual metaphors in connection with understanding. When we finally understand what someone is trying to point out to us, we exclaim: “I see!” When someone really understands a subject matter, we say that she has “insight”. There appears to be a link between visualization (...) and understanding, and between visualizability and intelligibility. This applies in science no less than in daily life: visualization is regarded as a useful means of achieving scientific understanding, even in the .. (shrink)

Modeling is an important scientific practice, yet it raises significant philosophical puzzles. Models are typically idealized, and they are often explored via imaginative engagement and at a certain “distance” from empirical reality. These features raise questions such as what models are and how they relate to the world. Recent years have seen a growing discussion of these issues, including a number of views that treat modeling in terms of indirect representation and analysis. Indirect views treat the model as a bona (...) fide object, specified by the modeler and used to represent and reason about some portion of the concrete empirical world. On some indirect views, model systems are abstract entities, such as mathematical structures, while on other views they are concrete hypothetical things. Here I assess these views and offer a novel account of models. I argue that regarding models as abstracta results in some significant tensions with the practice of modeling, especially in areas where non-mathematical models are common. Furthermore, viewing models as concrete hypotheticals raises difficult questions about model-world relations. The view I argue for treats models as direct, albeit simplified, representations of targets in the world. I close by suggesting a treatment of model-world relations that draws on a recent work by Stephen Yablo concerning the notion of partial truth. (shrink)

one takes to be the most salient, any pair could be judged more similar to each other than to the third. Goodman uses this second problem to showthat there can be no context-free similarity metric, either in the trivial case or in a scientifically ...

I argue that thought experiments are a form of experimental reasoning similar to real experiments. They require the same ability to participate by following a narrative as real experiments do. Participation depends in turn on using what we already know to visualize, manipulate and understand what is unfamiliar or problematic. I defend the claim that visualization requires embodiment by an example which shows how tacit understanding of the properties of represented objects and relations enables us to work out how such (...) objects might behave in a postulated world. This knowledge is that of embodied agents. That thought experiments require embodied participation is what makes them experiments rather than arguments. Unlike real experiments, from which ordinary perception has been displaced by instrumentation, thought experiments still appeal to relatively unmediated common sense, even when their purpose is to criticize or subvert common sense notions. (shrink)

At the age of sixteen, Einstein imagined chasing after a beam of light. He later recalled that the thought experiment had played a memorable role in his development of special relativity. Famous as it is, it has proven difficult to understand just how the thought experiment delivers its results. It fails to generate problems for an ether-based electrodynamics. I propose that Einstein’s canonical statement of the thought experiment from his 1946 “Autobiographical Notes,” makes most sense not as an argument against (...) ether-based electrodynamics, but as an argument against “emission” theories of light. (shrink)

When the New Organon appeared in 1620, part of a six-part programme of scientific inquiry entitled 'The Great Renewal of Learning', Francis Bacon was at the high point of his political career, and his ambitious work was groundbreaking in its attempt to give formal philosophical shape to a new and rapidly emerging experimentally-based science. Bacon combines theoretical scientific epistemology with examples from applied science, examining phenomena as various as magnetism, gravity, and the ebb and flow of the tides, and anticipating (...) later experimental work by Robert Boyle and others. His work challenges the entire edifice of the philosophy and learning of his time, and has left its mark on all subsequent philosophical discussions of scientific method. This volume presents a new translation of the text into modern English by Michael Silverthorne, and an introduction by Lisa Jardine that sets the work in the context of Bacon's scientific and philosophical activities. (shrink)

What sort of claims do scientific models make and how do these claims then underwrite empirical successes such as explanations and reliable policy interventions? In this paper I propose answers to these questions for the class of models used throughout the social and biological sciences, namely idealized deductive ones with a causal interpretation. I argue that the two main existing accounts misrepresent how these models are actually used, and propose a new account. *Received July 2006; revised August 2008. †To contact (...) the author, please write to: Department of Philosophy, University of Missouri, St. Louis, 599 Lucas Hall (MC 73), One University Blvd., St. Louis, MO 63121-4400; e-mail: [email protected]. (shrink)

Thought experiments in science are merely picturesque argumentation. I support this view in various ways, including the claim that it follows from the fact that thought experiments can err but can still be used reliably. The view is defended against alternatives proposed by my cosymposiasts.

Despite their intuitive appeal and a long philosophical history, imagery-based accounts of the imagination have fallen into disfavor in contemporary discussions. The philosophical pressure to reject such accounts seems to derive from two distinct sources. First, the fact that mental images have proved difficult to accommodate within a scientific conception of mind has led to numerous attempts to explain away their existence, and this in turn has led to attempts to explain the phenomenon of imagining without reference to such ontologically (...) dubious entities as mental images. Second, even those philosophers who accept mental images in their ontology have worried about what seem to be fairly obvious examples of imaginings that occur without imagery. In this paper, I aim to relieve both these points of philosophical pressure and, in the process, develop a new imagery-based account of the imagination: the imagery model. (shrink)

Most scientific models are not physical objects, and this raises important questions. What sort of entity are models, what is truth in a model, and how do we learn about models? In this paper I argue that models share important aspects in common with literary fiction, and that therefore theories of fiction can be brought to bear on these questions. In particular, I argue that the pretence theory as developed by Walton (1990, Mimesis as make-believe: on the foundations of the (...) representational arts. Harvard University Press, Cambridge/MA) has the resources to answer these questions. I introduce this account, outline the answers that it offers, and develop a general picture of scientific modelling based on it. (shrink)

Peter Vickers examines 'inconsistent theories' in the history of science--theories which, though contradictory, are held to be extremely useful. He argues that these 'theories' are actually significantly different entities, and warns that the traditional goal of philosophy to make substantial, general claims about how science works is misguided.

Several major breakthroughs in the history of physics have been prompted not by new empirical data but by thought experiments. James Robert Brown and John Norton have developed accounts of how thought experiments can yield such advances. Brown argues that knowledge gained via thought experiments demands a Platonic explanation; thought experiments for Brown are a window into the Platonic realm of the laws of nature. Norton argues that thought experiments are just cleverly disguised inductive or deductive arguments, so no new (...) account of their epistemology is needed. In this paper, I argue that although we do not need to invoke any Platonic insight to explain thought experimentation, Norton’s eliminativist account fails to capture the unique epistemological importance of thought experiments qua thought experiments. I then present my own account, according to which thought experiments are a particular type of inductive inference that is uniquely suited to generate new breakthroughs. (shrink)

Pointing to similarities between challenges encountered in today's neural coding and twentieth-century behaviorism, we draw attention to lessons learned from resolving the latter. In particular, Perceptual Control Theory posits behavior as a closed-loop control process with immediate and teleological causes. With two examples, we illustrate how these ideas may also address challenges facing current neural coding paradigms.

This paper introduces and defends an account of model-based science that I dub model pluralism. I argue that despite a growing awareness in the philosophy of science literature of the multiplicity, diversity, and richness of models and modeling practices, more radical conclusions follow from this recognition than have previously been inferred. Going against the tendency within the literature to generalize from single models, I explicate and defend the following two core theses: any successful analysis of models must target sets of (...) models, their multiplicity of functions within science, and their scientific context and history and for almost any aspect x of phenomenon y, scientists require multiple models to achieve scientific goal z. (shrink)

Fictionalists believe that scientific models are about model systems that are imaginary. Michael Weisberg has claimed that fictionalism is indefensible because many scientific models are about model systems that are unimaginable. According to a certain account of imagination, what Weisberg says is plausible. According to another, more defensible account of imagination, it is not. I discuss these issues within the context of an allegedly unimaginable model system in ecology, but the conclusions I draw are more general. I then describe how (...) fictionalism should be recast in order to deal with Weisberg’s critique. (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)

Computer simulations and experiments share many important features. One way of explaining the similarities is to say that computer simulations just are experiments. This claim is quite popular in the literature. The aim of this paper is to argue against the claim and to develop an alternative explanation of why computer simulations resemble experiments. To this purpose, experiment is characterized in terms of an intervention on a system and of the observation of the reaction. Thus, if computer simulations are experiments, (...) either the computer hardware or the target system must be intervened on and observed. I argue against the first option using the non-observation argument, among others. The second option is excluded by e.g. the over-control argument, which stresses epistemological differences between experiments and simulations. To account for the similarities between experiments and computer simulations, I propose to say that computer simulations can model possible experiments and do in fact often do so. (shrink)

From antiquity to the end of the twentieth century, philosophical discussions of understanding remained undeveloped, guided by a 'received view' that takes understanding to be nothing more than knowledge of an explanation. More recently, however, this received view has been criticized, and bold new philosophical proposals about understanding have emerged in its place. In this book, Kareem Khalifa argues that the received view should be revised but not abandoned. In doing so, he clarifies and answers the most central questions in (...) this burgeoning field of philosophical research: what kinds of cognitive abilities are involved in understanding? What is the relationship between the understanding that explanations provide and the understanding that experts have of broader subject matters? Can there be understanding without explanation? How can one understand something on the basis of falsehoods? Is understanding a species of knowledge? What is the value of understanding? (shrink)

I claim that one way thought experiments contribute to scientific progress is by increasing scientific understanding. Understanding does not have a currently accepted characterization in the philosophical literature, but I argue that we already have ways to test for it. For instance, current pedagogical practice often requires that students demonstrate being in either or both of the following two states: 1) Having grasped the meaning of some relevant theory, concept, law or model, 2) Being able to apply that theory, concept, (...) law or model fruitfully to new instances. Three thought experiments are presented which have been important historically in helping us pass these tests, and two others that cause us to fail. Then I use this operationalization of understanding to clarify the relationships between scientific thought experiments, the understanding they produce, and the progress they enable. I conclude that while no specific instance of understanding (thus conceived) is necessary for scientific progress, understanding in general is. (shrink)

"_Of Literature and Knowledge_ looks... like an important advance in this new and very important subject... literature is about to become even more interesting." – Edward O. Wilson, Pellegrino University Professor, Harvard University. Framed by the theory of evolution, this colourful and engaging volume presents a new understanding of the mechanisms by which we transfer information from narrative make-believe to real life. Ranging across game theory and philosophy of science, as well as poetics and aesthetics, Peter Swirski explains how literary (...) fictions perform as a systematic tool of enquiry, driven by thought experiments. Crucially, he argues for a continuum between the cognitive tools employed by scientists, philosophers and scholars or writers of fiction. The result is a provocative study of our talent and propensity for creating imaginary worlds, different from the world we know yet invaluable to our understanding of it. _Of Literature and Knowledge_ is a noteworthy challenge to contemporary critical theory, arguing that by bridging the gap between literature and science we might not only reinvigorate literary studies but, above all, further our understanding of literature. (shrink)

Models as Make-Believe offers a new approach to scientific modelling by looking to an unlikely source of inspiration: the dolls and toy trucks of children's games of make-believe.

Diagrams have distinctive characteristics that make them an effective medium for communicating research findings, but they are even more impressive as tools for scientific reasoning. Focusing on circadian rhythm research in biology to explore these roles, we examine diagrammatic formats that have been devised to identify and illuminate circadian phenomena and to develop and modify mechanistic explanations of these phenomena.

Contemplating imaginary scenarios that evoke certain sorts of quasi‐sensory intuitions may bring us to new beliefs about contingent features of the natural world. These beliefs may be produced quasi‐observationally; the presence of a mental image may play a crucial cognitive role in the formation of the belief in question. And this albeit fallible quasi‐observational belief‐forming mechanism may, in certain contexts, be sufficiently reliable to count as a source of justification. This sheds light on the central puzzle surrounding scientific thought experiment, (...) which is how contemplation of an imaginary scenario can lead to new knowledge about contingent features of the natural world. (shrink)

Computer simulation (CS) and thought experiments (TE) 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. (2013) 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. (2013) 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)

Creativity is typically defined as a disposition to produce valuable ideas. We argue that this is a mistake and defend a new definition of creativity in terms of the imagination. It follows that creativity has instrumental value at most and then only in the right circumstances. We consider the role of tradition and judgment in worthwhile creativity and argue that there is frequently a tension between greater creativity and the production of value.

In Leo Szilard’s 1929 thought experiment, a Maxwell demon manipulates a one-molecule gas to reverse the second law of thermodynamics. The demon must fail, Szilard argued, since there is hidden entropy creation in the demon’s collecting of information. This thought experiment is an inconsistent muddle of improper idealizations. It diverted an already successful literature of exorcism into degenerating speculations about about a connection between thermodynamic entropy and information. These confusions persist today in a voluminous literature. Narrative conventions in a thought (...) experiment allow thought experimenters great latitude in deciding which processes are typical and bear generalization and which can be idealized away as incidental. For its abuse of this latitude and the extent of the harm it caused, this thought experiment merits the distinction of the worst thought experiment in science. (shrink)