James Beebe (University of Buffalo)

I report the results of a study that investigated the views of researchers working in seven scientific disciplines (physics, chemistry, biology, economics, psychology, sociology, and anthropology) and in HPS in regard to four hypothesized dimensions of scientific realism. Among other things, we found that (i) natural scientists tended to express more strongly realist views than social scientists, (ii) HPS scholars tended to express more anti-realist views than natural scientists and many social scientists, (iii) van Fraassen’s characterization of scientific realism failed to cluster with more standard characterizations, and (iv) while those who endorsed the No-Miracles Argument were more likely to endorse scientific realism, those who endorsed the Pessimistic Induction were no more or less likely to endorse anti-realism. On the basis of these results, we are working to develop a fully validated measure of scientific realism.

Thomas Blanchard (Illinois Wesleyan University)

Jaegwon Kim has famously argued that on non-reductive physicalism, the causal powers of lower-level properties preclude higher-level properties from having any causal powers of their own. In response, non-reductive physicalists have sought to salvage the causal efficacy of higher-level properties in various ways. These debates have been driven in large part by appeals to intuition. For instance, Kim has claimed that his view stems from “a perfectly intuitive and ordinary understanding of the causal relation.” Yet so far no attempt has been made to examine whether laypeople really do find the relevant positions and principles intuitive. In this talk I will discuss the results of three experiments examining laypeople’s view of high-level causation in contexts involving multiple realization. (This talk is based on joint work with Dylan Murray and Tania Lombrozo.)    

Sam Johnson (Bath)

Many psychological processes are abductive—they have as their goal to infer hidden explanations from observations, including causal reasoning (inferring causes from effects), categorization (categories from object features), theory-of-mind (mental states from behavior), and perception (a 3-D world from a 2-D retinal array). Many cognitive scientists believe that the mind uses Bayesian logic to solve these problems, yet this seems dubious due to seemingly insurmountable computational challenges: People often lack sufficient information, principled ways to assign prior probabilities, or the requisite computational capacity to carry out Bayesian calculations. In this talk, I present experimental evidence that the mind uses a set of domain-general heuristics and strategies—collectively, explanatory logic—to address many of these challenges. In particular, I discuss how the mind deals with problems of missing evidence, the trade-off between simplicity and goodness-of-fit, and predictions from uncertain explanations. These strategies may make much of cognition possible, but nonetheless lead to systematic biases relative to Bayesian norms.    

Edouard Machery (University of Pittsburgh)

The vernacular concept of innateness appears to be incompatible with our scientifically informed understanding of evolution, development, and heredity since we now recognize that all traits develop as a result of the interaction between genetic and environmental factors.  Nonetheless, the concept of innateness is well and alive in many areas of science including mainstream developmental psychology, linguistics, and even evolutionary biology. This talk examines why scientists and philosophers adhere to a concept that has been so thoroughly criticized. Is it because scientists have a different, less objectionable (if at all) concept of innateness? Or rather, despite their sophistication, are scientists reverting to using a powerful, attractive, vernacular concept?

Peter Mattig (Wuppertal) and Michael Stoelzner (South Carolina)

Using questionnaires, interviews and key word searches of publications we analyze the change of the strategy of physicists at the LHC in the search for physics beyond the Standard Model (SM) of particle physics. While the SM explains all measurements at accelerators with outstanding precision based on only a few principles, its appears To have redundancies and does not include dark matter or energy, as suggested by astrophysical observations. Physicists therefore assume the SM to be just part of a more encompassing theory. The LHC is hoped to provide a glimpse of how this new theory should look like, as hypothesized in a plethora of models. Indeed, after the discovery and confirmation of the Higgs boson at the LHC, the last experimentally outstanding piece of the SM, most of these models have been frustrated. As yet, after 10 years of operation, no signs of the anticipated physics beyond the SM (BSM) has been found. We will present results from our empirical studies and show that in the current state particle physicists are turning more and more to model independent searches for BSM. We analyze the epistemic relevance of experimental and theoretical methods that are invoked, e.g. experimental signatures and effective field theory, and discuss the role of theory in this change of strategy. 

Moti Mizrahi (Florida Tech)

There is an ongoing methodological debate in Philosophy of Science concerning the use of case studies as evidence for and/or against theories about science. In this paper, I aim to make a contribution to this debate by taking an empirical approach. I present the results of a systematic survey of the PhilSci-Archive, which suggest that a sizeable proportion of papers in Philosophy of Science contain appeals to case studies, as indicated by the occurrence of the indicator words “case study.” These results are confirmed by data mined from JSTOR on research articles published in three leading journals in the field: Philosophy of Science, the British Journal for the Philosophy of Science (BJPS), and the Journal for General Philosophy of Science (JGPS). The data also show upward trends in appeals to case studies in articles published in some of these journals. The empirical work I have done for this paper provides philosophers of science who are way of the use of case studies as evidence for and/or against theories about science with a wary to do Philosophy of Science that is informed by data rather than case studies.

Michiru Nagatsu (Helsinki)  

In this talk I outline how experimental philosophy can be fruitfully applied to economics as a science, and contribute empirically to debates concerning conceptual and methodological problems in economics. I discuss differences between folk and economic concepts of choice, preferences and beliefs. Some of them have been studied with experimental philosophy methods, while others have been long discussed in methodological debates between economics and psychology without clear solutions. I sketch a pilot design to make progress, regarding the notions of beliefs in game theory. I also discuss how X-phi can shed ight on the recent debates on the ethics of nudges.

Charles Pence, Oliver Lean, and Luca Rivelli (Louvain)

It’s a commonplace that the philosophy of science should, in principle, be able to offer normative recommendations to scientists about how to do science well – just as scientists themselves can, and often do. If we are to do so successfully, our philosophy had better be suitably empirical; that is, it should be in contact with the facts on the ground about scientific practice and the concrete problems it faces. But increasingly, with the development of empirical methods in philosophy of science, this drive appears to land philosophy in an identity crisis: In what sense would this empirical philosophy remain philosophy, rather than becoming absorbed into science? In this talk, we address this question focusing on case studies from the digital humanities; namely, the large-scale analysis of scientific literature through principled empirical methods. In particular, we will argue, philosophers engage in forms of normatively relevant abstraction and self-reflection that are made much more perspicuous via the application of digital-humanities (and, by extension, other empirical) tools.    

Carlos Santana (University of Utah)

My goal is to show that doing corpus-based linguistics as a means to aid philosophy of science is easy (well, sort of…) and fun (well, mostly…). I’ll quickly review existing studies which use machine learning to track diachronic and synchronic differences in science, then suggest that you make friends with a computer scientist if you want to run studies of that sort. My focus will be on research more accessible without a whole lot of special training, showing how existing tools make corpus analysis accessible to any researcher. I’ll exemplify this with a few examples from my own work in progress: comparing use of the term Anthropocene between geoscientists and environmental humanists; examining whether extreme weather event reporting is as non-committal as often claimed; and tracking the influence of terms from the environmental sciences (invasive species, global warming, etc.) on public discourse.

Samuel Schindler (Aarhus University)

Theoretical virtues are desirable properties of a scientific theory. In the philosophy of science, theoretical virtues have been debated in relation to scientific realism and theory choice. But are the properties philosophers consider virtuous also the properties that scientists really aim for in theory building? When seeking support for their arguments in these debates, philosophers have hitherto mostly relied upon their own intuitions or on historical case studies. This has obvious shortcomings: intuitions can be very subjective and misleading (Alexander and Weinberg 2007) and historical cases can be cherry-picked and non-representative (Pitt 2001). But there is an alternative way for philosophers to ground their arguments. The current paper contributes to this recent movement in experimental philosophy of science and presents the results of a survey on the views of scientists on theoretical virtues. It moreover compares these views to the views held by philosophers and draws consequences for debates about theory choice and scientific realism. 

Andrew Shtulman (Occidental College, LA)

Before learning science, we construct intuitive theories of the natural world. Intuitive theories function similarly to scientific theories, furnishing us with explanations and predictions, but they are less accurate and less precise. In this talk, I will show how intuitive theories survive the acquisition of scientific theories, competing with those theories to provide inferences about the same phenomena. While we can learn to privilege science over intuition, we cannot eliminate the conflict between them, as revealed by priming studies, training studies, and studies with science experts. I will explore the dynamics of this conflict, its cognitive underpinnings, and its implications for theories of conceptual change.

Deena Weisberg (Villanova University)

Abstract: Non-scientists possess pervasive and stubborn misconceptions about many aspects of science, leading to reasoning errors. This talk considers one such error: People are unduly attracted to explanations of scientific phenomena that contain irrelevant reductive language. This is especially the case when the reductive language refers to neuroscience. A series of studies investigates the source of this error and considers how it provides insight on the public understanding of science in general.