Forthcoming. “The Role of Imagination in Making Water from Moon Rocks: How Scientists Use Imagination to Break Constraints on Imagination.” Analysis. DOI: 10.1093/analys/anae015 (with H. Sargeant).

Scientists recognize the necessity of imagination for solving tough problems. But how does the cognitive faculty responsible for daydreaming also help in solving scientific problems? Philosophers claim that imagination is informative only when it is constrained to be maximally realistic. However, using a case study from space science, we show that scientists use imagination intentionally to break reality-oriented constraints. To do this well, they first target low-confidence constraints, and then progressively higher-confidence constraints until a plausible solution is found. This paper exemplifies a new approach to epistemology of imagination that focuses on sets of imaginings (rather than individual imaginings), and responsible (rather than reliable) imaginings.

Forthcoming. “Scientific Models and Thought experiments: Same Same but Different.” In T. Knuuttila, Natalia Carrillo & Rami Koskinen (eds.). Handbook of Philosophy of Scientific Modeling. London: Routledge (with R. El Skaf).

The philosophical literatures on models and thought experiments have been developing exponentially, and independently, for decades. This independence is surprising, given how similar models and thought experiments are. They each have “lives of their own,” they sit between theory and experience, they are important for both pedagogy and cutting-edge science, they galvanize conceptual changes and paradigm shifts, and they involve entertaining imaginary scenarios and working out what happens. Recently, philosophers have begun to highlight these similarities. This entry aims at taking the idea further, by trying to systematically identify places where insights from one literature can be taken up in the other. Along the way, important differences will also be highlighted.

Forthcoming. “Moving Targets and Models of Nothing: A New Sense of Abstraction for Philosophy of Science.” In J. Sánchez-Dorado and C. Ambrosio (eds.). Understanding through Abstraction in Science and Art. London: Routledge (with A. Kozlov).

As Nelson Goodman highlighted, there are two main senses of “abstract” that can be found in discussions about abstract art. On the one hand, a representation is abstract if it leaves out certain features of its target. On the other hand, something can be abstract to the extent that it does not represent a concrete subject. The first sense of “abstract” is well-known in philosophy of science. For example, philosophers discuss mathematical models of physical, biological, and economic systems as being abstract in this sense. However, it is the second sense that dominates discussions of abstract art in aesthetics. For example, abstract art was (and is) considered revolutionary precisely for being non-figurative. Through an analysis of artists including Kandinsky, Malevich, and Mondrian, we develop a reading of this second sense, which we call “generative abstraction,” as opposed to “subtractive” abstraction. Generative abstraction is a process in which a new artifact is created which does not represent the initial concrete target system that inspired it (if there was one), where the artifact’s features are explored for their own sake, and where the “language” of the new artifact is in some way more “universal.” Focusing on this sense of abstraction is helpful in revealing the complexity of the process of crafting an abstract artifact, in problematizing the notion that abstraction can always be un-done (or concretized), as well as revealing new ways for abstractions to be epistemically (un)successful.

Forthcoming. “Inclusivity in the Education of Scientific Imagination.” In E. Hildt, K. Laas, C. Miller, and E. Brey (eds.). Building Inclusive Ethical Cultures in STEM. Routledge (with H. Sargeant).

Scientists imagine constantly. They do this when generating research problems, designing experiments, interpreting data, troubleshooting, drafting papers and presentations, and giving feedback. But when and how do scientists learn how to use imagination? Across six years of ethnographic research, it has been found that advanced career scientists feel comfortable using and discussing imagination, while graduate and undergraduate students of science often do not. In addition, members of marginalized and vulnerable groups tend to express negative views about the strength of their own imaginations, and the general usefulness of imagination in science. After introducing these findings and discussing the typical relationship between a student and their imagination across a career in science, we argue that reducing the number or power of active imaginations in science is epistemically counterproductive and finally suggest a number of ways to bring imagination back into science in a more inclusive way, especially through courses on imagination for scientists, role models, and exemplar-based learning.

2023. “The future won’t be pretty: The nature and value of ugly, AI-designed experiments.” In M. Ivanova and A. Murphy (eds). The Aesthetics of Scientific Experiments. London: Routledge.

Can an ugly experiment be a good experiment? Philosophers have identified many beautiful experiments and explored ways in which their beauty might be connected to their epistemic value. In contrast, the present chapter seeks out (and celebrates) ugly experiments. Among the ugliest are those being designed by AI algorithms. Interestingly, in the contexts where such experiments tend to be deployed, low aesthetic value correlates with high epistemic value. In other words, ugly experiments can be good. Given this, we should conclude that beauty is not generally necessary or sufficient for epistemic value, and increasing beauty will not generally tend to increase epistemic value.

2023. “Counterpossibles in Science: An Experimental Study.” Synthese. DOI: 10.1007/s11229-022-04014-0 (with B. McLoone and C. Grützner).

A counterpossible is a counterfactual whose antecedent is impossible. The vacuity thesis says all counterpossibles are true solely because their antecedents are impossible. Recently, some have rejected the vacuity thesis by citing purported non-vacuous counterpossibles in science. One limitation of this work, however, is that it is not grounded in experimental data. Do scientists actually reason non-vacuously about counterpossibles? If so, what is their basis for doing so? We presented biologists (N = 86) with two counterfactual formulations of a well-known model in biology, the antecedents of which contain what many philosophers would characterize as a metaphysical impossibility. Participants consistently judged one counterfactual to be true, the other to be false, and they explained that they formed these judgments based on what they perceived to be the mathematical relationship between the antecedent and consequent. Moreover, we found no relationship between participants’ judgments about the (im)possibility of the antecedent and whether they judged a counterfactual to be true or false. These are the first experimental results on counterpossibles in science with which we are familiar. We present a modal semantics that can capture these judgments, and we deal with a host of potential objections that a defender of the vacuity thesis might make.

2022. “Sharpening the Tools of Imagination.” Synthese 200: 451.

Thought experiments, models, diagrams, computer simulations, and metaphors can all be understood as tools of the imagination. While these devices are usually treated separately in philosophy of science, this paper provides a unified account according to which tools of the imagination are epistemically good insofar as they improve scientific imaginings. Improving scientific imagining is characterized in terms of epistemological consequences: more improvement means better consequences. A distinction is then drawn between tools being good in retrospect, at the time, and in general. In retrospect, tools are evaluated straightforwardly in terms of the quality of their consequences. At the cutting edge, tools are evaluated positively insofar as there is reason to believe that using them will have good consequences. Lastly, tools can be generally good, insofar as their use encourages the development of epistemic virtues, which are good because they have good epistemic consequences.

2022. “Scientists are Epistemic Consequentialists about Imagination.” Philosophy of Science 90 (3):518–538. DOI: 10.1017/psa.2022.31

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.

2022. “Understanding Metaphorical Understanding (Literally).” European Journal for Philosophy of Science. DOI: 10.1007/s13194-022-00479-5 (with D. Wilkenfeld).

Metaphors are found all throughout science: in published papers, working hypotheses, policy documents, lecture slides, grant proposals, and press releases. They serve different functions, but perhaps most striking is the way they enable understanding, of a theory, phenomenon, or idea. In this paper, we leverage recent advances on the nature of metaphor and the nature of understanding to explore how they accomplish this feat. We attempt to shift the focus away from the epistemic value of the content of metaphors, to the epistemic value of the metaphor’s consequences. Many famous scientific metaphors are epistemically good, not primarily because of what they say about the world, but because of how they cause us to think. Specifically, metaphors increase understanding either by improving our sets of representations (by making them more minimal or more accurate), or by making it easier for us to encode and process data about complex subjects by changing how we are disposed to conceptualize those subjects. This view hints towards new positions concerning testimonial understanding, factivity, abilities, discovery via metaphor, and the relation between metaphors and models.

2022. “Holism and Reductionism in the Illness/Disease Debate.” In Wuppuluri & Stewart (eds.), From Electrons to Elephants and Elections: Saga of Content and Context. Springer (with M. Buzzoni and L. Tesio).

In the last decades it has become clear that medicine must find some way to combine its scientific and humanistic sides. In other words, an adequate notion of medicine requires an integrative position that mediates between the analytic-reductionist and the normative-holistic tendencies we find therein. This is especially important as these different styles of reasoning separate “illness” (something perceived and managed by the whole individual in concert with their environment) and “disease” (a “mechanical failure” of a biological element within the body). While the demand for an integrative view has typically been motivated by ethical concerns, we claim that it is also motivated, perhaps even more fundamentally, by epistemological and methodological reasons. Evidence-based bio-medicine employs experimental and statistical techniques which eliminate important differences in the ways that conscious humans evaluate, live with, and react to disease and illness. However, it is precisely these experiences that underpin the concepts and norms of bio-medicine. Humanistic disciplines, on the other hand, have the resources to investigate these experiences in an intersubjectively testable way. Medicine, therefore, cannot afford to ignore its nature as a human science; it must be concerned not only with disease and illness, but also with the ways in which patients as persons respond to malady. Insofar as attitudes and expectations influence the criteria of illness and disease, they must be studied as part of the genuine subject matter of medicine as a human science. In general, we urge that this is a necessary step to overcome today’s trend to split evidence-based and clinical medicine.

2022. “Science Funding Policy and the COVID-19 Pandemic.” The Journal of Risk and Safety in Medicine 33(3):1-6. DOI: 10.3233/JRS-227015 (with V. Sikimić and J. Shaw).

Science funding policy is constantly evolving as a result of geopolitical, technological, cultural, social, and economic shifts. The last major upheaval of science funding policy happened in response to a catastrophic series of events: World War II. The newest worldwide catastrophe, the COVID-19 pandemic, has prompted similar reflections on fundamental questions about the roles of the sciences in society and the relationships between governments, private industry, public bodies, and the broader public. This is the introduction to a special issue on science funding policy, containing a series of reflections and insights which urge drastic and urgent changes that ought to be made.

2021. “Guilty Artificial Minds: Folk Attributions of Mens Rea and Culpability to Artificially Intelligent Agents.”In Proceedings of the ACM on Human-Computer Interaction Vol. 5, CSCW2, Article 363. (with M. Kneer).

While philosophers hold that it is patently absurd to blame robots or hold them morally responsible, a series of recent empirical studies suggest that people do ascribe blame to AI systems and robots in certain contexts. This is disconcerting: Blame might be shifted from the owners, users or designers of AI systems to the systems themselves, leading to the diminished accountability of the responsible human agents. In this paper, we explore one of the potential underlying reasons for robot blame, namely the folk’s willingness to ascribe inculpating mental states or “mens rea” to robots. In a vignette-based experiment (N=513), we presented participants with a situation in which an agent knowingly runs the risk of bringing about substantial harm. We manipulated agent type (human v. group agent v. AI-driven robot) and outcome (neutral v. bad), and measured both moral judgment (wrongness of the action and blameworthiness of the agent) and mental states attributed to the agent (recklessness and the desire to inflict harm). We found that (i) judgments of wrongness and blame were relatively similar across agent types, possibly because (ii) attributions of mental states were, as suspected, similar across agent types. This raised the question – also explored in the experiment – whether people attribute knowledge and desire to robots in a merely metaphorical way (e.g., the robot “knew” rather than really knew). However, (iii), according to our data people were unwilling to downgrade to mens rea in a merely metaphorical sense. Finally, (iv), we report a surprising and novel finding, which we call the inverse outcome effect on robot blame: People were less willing to blame artificial agents for bad outcomes than for neutral outcomes. This suggests that they are implicitly aware of the dangers of overattributing blame to robots when harm comes to pass, such as inappropriately letting the responsible human agent off the moral hook. .

2021. “Telling Stories in Science: Feyerabend and Thought Experiments.” HOPOS: The Journal of the International Society for the History of Philosophy of Science 10(2). DOI: 10.1086/712946.

The history of the philosophy of thought experiments has touched on the work of Kuhn, Popper, Duhem, Mach, Lakatos, and other big names of the 20th century, but so far, almost nothing has been written about Paul Feyerabend. His most influential work was Against Method, 8 chapters of which concern a case study of Galileo with a specific focus on Galileo’s thought experiments. In addition, the later Feyerabend was very interested in what might be called the epistemology of drama, including stories and myths. This paper brings these different aspects of Feyerabend’s work together in an attempt to present what might have been his considered views on scientific thought experiments. I conclude by contrasting Feyerabend’s ideas with two modern currents in the debate surrounding thought experiments: 1) the claim that the epistemology of thought experiments is just the epistemology of deductive or inductive arguments, and 2) the claim that the specifically narrative quality of thought experiments must be taken into account if we want a complete epistemology of thought experiments.

2021. “Motivating the History of the Philosophy of Thought Experiments.” HOPOS: The Journal of the International Society for the History of Philosophy of Science 10(2). DOI: 10.1086/712940. (With Y. Fehige).

This is the introduction to a special issue of HOPOS on the history of the philosophy of thought experiments.

2021. “Playing the Blame Game with Robots.” Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction. DOI: 10.1145/3434074.3447202 (with M. Kneer).

Recent research has shown that people are quite willing to ascribe moral blame to AI-driven artificial agents. In an experiment with 347 participants, we manipulated the explicitly specified capacities of such artificial agents, and explored the extent to which people are willing to ascribe potentially inculpating mental states to them and blame them for their actions. Moreover, we investigated whether the different capacities of the artificial agents or AI systems have an influence on the moral assessment of human agents who own and use them. Our results show that the more sophisticated an AI system is, the more participants will blame it when it puts human lives at risk, and the less they are willing to blame the human agents using it. Furthermore, the findings suggest that an AI system only begins to be perceived as blameworthy once it obtains a “theory of mind,” that is, once it obtains some knowledge and experience of how humans generally think and feel.

2020. “The Productive Anarchy of Scientific Imagination.” Philosophy of Science 87: 968–978. DOI: 10.1086/710629.

Imagination is important for many things in science: solving problems, interpreting data, designing studies, etc. Philosophers of imagination typically account for the productive role played by imagination in science by focusing on how imagination is constrained, e.g., by using self-imposed rules to infer logically, or model events accurately. But the constraints offered by these philosophers either constrain too much, or not enough, and they can never account for uses of imagination that are needed to break today’s constraints in order to make progress tomorrow. Thus, epistemology of imagination needs to make room for an element of epistemological anarchy.

2020. “The Material Theory of Induction and the Epistemology of Thought Experiments.” Studies in History and Philosophy of Science Part A 83: 17–27. DOI: 10.1016/j.shpsa.2020.03.005.

John D. Norton is responsible for a number of influential views in contemporary philosophy of science. This paper will discuss two of them. The material theory of induction claims that inductive arguments are ultimately justified by their material features, not their formal features. Thus, while a deductive argument can be valid irrespective of the content of the propositions that make up the argument, an inductive argument about, say, apples, will be justified (or not) depending on facts about apples. The argument view of thought experiments claims that thought experiments are arguments, and that they function epistemically however arguments do. These two views have generated a great deal of discussion, although there hasn’t been much written about their combination. I argue that despite some interesting harmonies, there is a serious tension between them. I consider several options for easing this tension, before suggesting a set of changes to the argument view that I take to be consistent with Norton’s fundamental philosophical commitments, and which retain what seems intuitively correct about the argument view. These changes require that we move away from a unitary epistemology of thought experiments and towards a more pluralist position.

2019. “Towards a Dual Process Epistemology of Imagination.” Synthese 198:1329–1350. DOI: 10.1007/s11229-019-02116-w.

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.

2019. “Everyday Scientific Imagination: A Qualitative Study of the Uses, Norms, and Pedagogy of Imagination in Science.” Science & Education 28(6), 711-730. DOI: 10.1007/s11191-019-00067-9.

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.

2019. “P-Curving X-Phi: Does Experimental Philosophy Have Evidential Value?” Analysis 79(4): 669–684. DOI: 10.1093/analys/anz007. (With E. Machery and D. Colaço). (This paper was highlighted as one of the best philosophical papers in 2019 by Oxford University Press here). DOI: 10.1093/analys/anz007.

In this article, we analyse the evidential value of the corpus of experimental philosophy (x-phi). While experimental philosophers claim that their studies provide insight into philosophical problems, some philosophers and psychologists have expressed concerns that the findings from these studies lack evidential value. Barriers to evidential value include selection bias (i.e., the selective publication of significant results) and p-hacking (practices that increase the odds of obtaining a p-value below the significance level). To find out whether the significant findings in x-phi papers result from selection bias or p-hacking, we applied a p-curve analysis to a corpus of 365 x-phi chapters and articles. Our results suggest that this corpus has evidential value, although there are hints of p-hacking in a few parts of the x-phi corpus.

2019. “The Role of Imagination in Social Scientific Discovery: Why Machine Discoverers Will Need Imagination Algorithms.” Pp. 49-66 in M. Addis et al. (eds.) Scientific Discovery in the Social Sciences. Springer: Heidelberg. DOI: 10.1007/978-3-030-23769-1_4.

When philosophers discuss the possibility of machines making scientific discoveries, they typically focus on discoveries in physics, biology, chemistry and mathematics. Observing the rapid increase of computer-use in science, however, it becomes natural to ask whether there are any scientific domains out of reach for machine discovery. For example, could machines also make discoveries in qualitative social science? Is there something about humans that makes us uniquely suited to studying humans? Is there something about machines that would bar them from such activity? A close look at the methodology of interpretive social science reveals several abilities necessary to make a social scientific discovery, and one capacity necessary to possess any of them is imagination. For machines to make discoveries in social science, therefore, they must possess imagination algorithms.

2019. “Peeking Inside the Black Box: A New Kind of Scientific Visualization.” Minds and Machines 29: 87–107. (With N. Nersessian). DOI: 10.1007/s11023-018-9484-3.

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 (observed in a qualitative study of a systems biology laboratory) 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.

2018. “The Content-Dependence of Imaginative Resistance.” Pp. 143-166 in F. Cova and S. Rénhault (eds.), Advances in Experimental Philosophy of Aesthetics. London: Bloomsbury. (With H. Kim and M. Kneer).

An observation of Hume’s has received a lot of attention over the last decade and a half: Although we can standardly imagine the most implausible scenarios, we encounter resistance when imagining propositions at odds with established moral (or perhaps more generally evaluative) convictions. The literature is ripe with ‘solutions’ to this so-called ‘Puzzle of Imaginative Resistance’. Few, however, question the plausibility of the empirical assumption at the heart of the puzzle. In this paper, we explore empirically whether the difficulty we witness in imagining certain propositions is indeed due to claim type (evaluative v. non-evaluative) or whether it is much rather driven by mundane features of content. Our findings suggest that claim type plays but a marginal role, and that there might hence not be much of a ‘puzzle’ to be solved.

2018. “How Thought Experiments Increase Understanding.” Pp. 526-44 in M. Stuart et al. (eds.), The Routledge Companion to Thought Experiments. London: Routledge.

We might think that thought experiments are at their most powerful or most interesting when they produce new knowledge. This would be a mistake; thought experiments that seek understanding are just as powerful and interesting, and perhaps even more so. A growing number of epistemologists are emphasizing the importance of understanding for epistemology, arguing that it should supplant knowledge as the central notion. In this chapter, I bring the literature on understanding in epistemology to bear on explicating the different ways that thought experiments increase three important kinds of understanding: explanatory, objectual and practical.

2018. “Thought Experiments: The State of the Art.” Pp. 1-28 in M. Stuart et al. (eds.), The Routledge Companion to Thought Experiments. London: Routledge.

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

2017. “Imagination: A Sine Qua Non of Science.” Croatian Journal of Philosophy Vol. XVII, No. 49: 9-32.

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.

2016. “Norton and the Logic of Thought Experiments.” Axiomathes 26: 451–466. DOI: 10.1007/s10516-016-9306-2.

John D. Norton defends an empiricist epistemology of thought experiments , the central thesis of which is that thought experiments are nothing more than arguments. Philosophers have attempted to provide counterexamples to this claim, but they haven’t convinced Norton. I will point out a more fundamental reason for reformulation that criticizes Norton’s claim that a thought experiment is a good one when its underlying logical form possesses certain desirable properties. I argue that by Norton’s empiricist standards, no thought experiment is ever justified in any deep sense due to the properties of its logical form. Instead, empiricists should consider again the merits of evaluating thought experiments more like laboratory experiments , and less like arguments.

2016. “Taming Theory with Thought Experiments: Understanding and Scientific Progress.” Studies in History and Philosophy of Science Part A 58: 24-33. DOI: 10.1016/j.shpsa.2016.04.002 0039-3681.

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.

2015. “Philosophical Conceptual Analysis as an Experimental Method.” Pp. 267-292 in Gamerschlag et al. (eds.). Meaning, Frames and Conceptual Representation. Düsseldorf: Düsseldorf University Press.

Philosophical conceptual analysis is an experimental method. Focusing on this helps to justify it from the skepticism of experimental philosophers who follow Weinberg, Nichols & Stich (2001). To explore the experimental aspect of philosophical conceptual analysis, I consider a simpler instance of the same activity: everyday linguistic interpretation. I argue that this, too, is experimental in nature. And in both conceptual analysis and linguistic interpretation, the intuitions considered problematic by experimental philosophers are necessary but epistemically irrelevant. They are like variables introduced into mathematical proofs which drop out before the solution. Or better, they are like the hypotheses that drive science, which do not themselves need to be true. In other words, it does not matter whether or not intuitions are accurate as descriptions of the natural kinds that undergird philosophical concepts; the aims of conceptual analysis can still be met.

2014. “Cognitive Science and Thought Experiments: A Refutation of Paul Thagard’s Skepticism.” Perspectives on Science 22: 98-121. DOI: 10.1162/POSC_a_00130.

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.

2014. “On the Origins of the Philosophy of Thought Experiments: The Forerun.” Perspectives on Science 22: 13-54 (with Y. Fehige). DOI: 10.1162/POSC_a_00127.

The history of thought experiments is now gaining a great deal of attention, and this is due to the renewed interest of philosophers on the subject. This paper inquires into the history of the philosophy of thought experiments. We name the period to be examined in this paper the “forerun.” Its main stakeholders are Georg C. Lichtenberg, Novalis, and Immanuel Kant. We will present and discuss the work of each of them in order to characterize the period, and then reveal parallels and lessons that apply to more recently proposed accounts of thought experiments.

2014. “Introduction to the Special Issue on Thought Experiments.” Perspectives on Science 22: 1-12 (with Y. Fehige). DOI: 10.1162/POSC_e_00126.

This is the introduction to a special issue of Perspectives on Science, which was the outcome of a workshop entitled, “Thought Experiments in Science: Four Blind Spots,” held at the University of Toronto, March 23rd, 2012. The recent revival in philosophical study of thought experiments has mostly been limited to fields like epistemology, science studies, and metaphilosophy. With this issue we hope to facilitate a discussion about how some other disciplinary perspectives might bear on the subject; specifically, the history of philosophy, literary studies, phenomenology and cognitive science.


2023. (Un)Successful Remembering and Imagining. Special issue of Philosophy and the Mind Sciences (with Y-T. Lin, C. McCarroll, and K. Michaelian).

2022. Feyerabend and the Philosophy of Physics. Special issue of International Studies in the Philosophy of Science (with J. Shaw).

2021. Thought Experiments in the History of Philosophy of Science. HOPOS special issue (with Y. Fehige) 10(2).

2018. The Routledge Companion to Thought Experiments. London: Routledge (with Y. Fehige and James R. Brown). DOI: 10.4324/9781315175027.

Thought experiments are a means of imaginative reasoning that lie at the heart of philosophy, from the pre-Socratics to the modern era, and they also play central roles in a range of fields, from physics to politics. The Routledge Companion to Thought Experiments is an invaluable guide and reference source to this multifaceted subject. Comprising over 30 chapters by a team of international contributors, the Companion covers the following important areas: the history of thought experiments, from antiquity to the trolley problem and quantum non-locality; thought experiments in the humanities, arts, and sciences, including ethics, physics, theology, biology, mathematics, economics, and politics; theories about the nature of thought experiments; new discussions concerning the impact of experimental philosophy, cross-cultural comparison studies, metaphilosophy, computer simulations, idealization, dialectics, cognitive science, the artistic nature of thought experiments, and metaphysical issues.

This broad ranging Companion goes backwards through history and sideways across disciplines. It also engages with philosophical perspectives from empiricism, rationalism, naturalism, skepticism, pluralism, contextualism, and neo-Kantianism to phenomenology. This volume will be valuable for anyone studying the methods of philosophy or any discipline that employs thought experiments, as well as anyone interested in the power and limits of the mind.

2014. Thought Experiments. Perspectives on Science special issue 22:2 (with Y. Fehige).


2020. “Thought Experiments.” In D. Pritchard (ed). Oxford Bibliographies of Philosophy. New York: Oxford (with J. R. Brown). DOI: 10.1093/OBO/9780195396577-0143.

2020. “Thought Experiments.” In: Glăveanu V. (ed). The Palgrave Encyclopedia of the Possible. Palgrave Macmillan. DOI: 10.1007/978-3-319-98390-5_59-1.


2023. Review of Bedeviled: A Shadow History of Demons in Science by Jimena Canales. ISIS 114:2.

2023. “How to Tame your Feyerabend.” Review of Interpreting Feyerabend edited by Karim Bschir and Jamie Shaw. Metascience 32:173–176. DOI: 10.1007/s11016-023-00863-8.

2021. Review of Arnon Levy and Peter Godfrey-Smith (eds.), The Scientific Imagination: Philosophical and Psychological Perspectives, Oxford University Press. Journal for General Philosophy of Science.

2013. “Thought Experiments in Methodological and Historical Contexts edited by Katerina Ierodiakonou and Sophie Roux.” HOPOS: The Journal of the International Society for the History of Philosophy of Science 3: 154-57 (with J. Brown). DOI: 10.1086/667764.

2012. “Laboratory of the Mind by James R. Brown.” Spontaneous Generations: A Journal for the History and Philosophy of Science 6: 237-241. DOI: 10.4245/sponge.v6i1.15820.


Forthcoming. Post for Auxiliary Hypotheses, a blog by the British Society for the Philosophy of Science.

Forthcoming. “Spacing out: why we dream of living in space.” Site Magazine.

2021. “Workshop Report: Successful and Unsuccessful Remembering and Imagining.” Post for The Junkyard of the Mind, an academic blog on the imagination (with Y-T Lin, Kirk Michaelian, C. McCarroll, and I-J Wang).

2022. “Science and Imagination.” Imaginezine.

2021. “Epistemology of Experimental Imagination.” Post for The Junkyard of the Mind, an academic blog on the imagination curated by Amy Kind of Claremont McKenna College.

2021. “Imagining Our Future in Space: NASA’s Sociotechnical Imaginary.” Post for The Junkyard of the Mind, an academic blog on the imagination curated by Amy Kind of Claremont McKenna College.

2020. Responsible Life Science Policy: Between Private and Public Funding. The Reasoner. (With V. Sikimic and J. Shaw)

2020. Symposium for Jim Davies’s book Imagination. Post for The Junkyard of the Mind, an academic blog on the imagination.

2019. “Ethics of Scientific Imagination: Who Gets to Use Imagination in Science?” Post for The Junkyard of the Mind, an academic blog on the imagination curated by Amy Kind of Claremont McKenna College.

2019.  “Is X-Phi P-Hacked?” Post for Dailynous, a blog providing news for and about the philosophy profession.

2019. “How Does Experimental Philosophy Fare under the P-Curve?” Post for the New Experimental Philosophy Blog, a blog on experimental philosophy managed by members of the University of Waikato Experimental Philosophy Group and the Australasian Experimental Philosophy Group.

2018. “From Painting to Pig-Human Hybrids: Imagination and Our Interaction with Art and Science.” The Junkyard of the Mind, an academic blog on the imagination curated by Amy Kind of Claremont McKenna College.

2017. “Using Imagination to Empathize with Space Robots, Demons, and Other Weird Stuff.” The Junkyard of the Mind, an academic blog on the imagination curated by Amy Kind of Claremont McKenna College.

2015. “Better Science Policy in Canada.” The Bubble Chamber, a blog on the history and philosophy of science.


In Preparation “Empirically Disambiguating Imagination and Supposition” (with M. Kneer)

In Preparation. “Artificial Guilt.” The Politics and Governance of Blame. OUP (with Markus Kneer).

In Preparation. “The Rise of Chemical Thought Experiments.”

In Preparation. “Pragmatic Understanding Can Do on Its Own.”

In Preparation. “AI-designed experiments: The future isn’t pretty.” Aesthetics of Experiments. Routledge.

In Preparation. “Models of Nothing: Abstracting from Reality in Science and Art.” Understanding through Abstraction in Science and Art. Routledge.

In Preparation. “The Experimental Philosophy of Imagination.” Handbook on Imagination and Creativity. OUP.