I argue for the causal character of modeling in data-intensive science, contrary to widespread claims that big data is only concerned with the search for correlations. After discussing the concept of data-intensive science and introducing two examples as illustration, several algorithms are examined. It is shown how they are able to identify causal relevance on the basis of eliminative induction and a related difference-making account of causation. I then situate data-intensive modeling within a broader framework of an epistemology of scientific (...) knowledge. In particular, it is shown to lack a pronounced hierarchical, nested structure. The significance of the transition to such “horizontal” modeling is underlined by the concurrent emergence of novel inductive methodology in statistics such as non-parametric statistics. Data-intensive modeling is well equipped to deal with various aspects of causal complexity arising especially in the higher level and applied sciences. (shrink)

Recent claims, mainly from computer scientists, concerning a largely automated and model-free data-intensive science have been countered by critical reactions from a number of philosophers of science. The debate suffers from a lack of detail in two respects, regarding the actual methods used in data-intensive science and the specific ways in which these methods presuppose theoretical assumptions. I examine two widely-used algorithms, classificatory trees and non-parametric regression, and argue that these are theory-laden in an external sense, regarding the framing of (...) research questions, but not in an internal sense concerning the causal structure of the examined phenomenon. With respect to the novelty of data-intensive science, I draw an analogy to exploratory as opposed to theory-directed experimentation. (shrink)

The success of particle detection in high energy physics colliders critically depends on the criteria for selecting a small number of interactions from an overwhelming number that occur in the detector. It also depends on the selection of the exact data to be analyzed and the techniques of analysis. The introduction of automation into the detection process has traded the direct involvement of the physicist at each stage of selection and analysis for the efficient handling of vast amounts of data. (...) This tradeoff, in combination with the organizational changes in laboratories of increasing size and complexity, has resulted in automated and semi-automated systems of detection. Various aspects of the semi-automated regime were greatly diminished in more generic automated systems, but turned out to be essential to a number of surprising discoveries of anomalous processes that led to theoretical breakthroughs, notably the establishment of the Standard Model of particle physics. The automated systems are much more efficient in confirming specific hypothesis in narrow energy domains than in performing broad exploratory searches. Thus, in the main, detection processes relying excessively on automation are more likely to miss potential anomalies and impede potential theoretical advances. I suggest that putting substantially more effort into the study of electron–positron colliders and increasing its funding could minimize the likelihood of missing potential anomalies, because detection in such an environment can be handled by the semi-automated regime—unlike detection in hadron colliders. Despite virtually unavoidable excessive reliance on automated detection in hadron colliders, their development has been deemed a priority because they can operate at currently highest energy levels. I suggest, however, that a focus on collisions at the highest achievable energy levels diverts funds from searches for potential anomalies overlooked due to tradeoffs at the previous energy thresholds. I also note that even in the same collision environment, different research strategies will opt for different tradeoffs and thus achieve different experimental outcomes. Finally, I briefly discuss current searches for anomalous process in the context of the previous analysis. (shrink)

The discovery and ongoing investigation of microRNAs suggest important conceptual and methodological lessons for philosophers and historians of biology. This paper provides an account of miRNA research and the shift from viewing these tiny regulatory entities as minor curiosities to seeing them as major players in the post-transcriptional regulation of genes. Conceptually, the study of miRNAs is part of a broader change in understandings of genetic regulation, in which simple switch-like mechanisms were reinterpreted as aspects of complex cellular and genome-wide (...) processes. Among them are the activities of small RNAs, previously regarded as non-functional. Methodologically, the miRNA story suggests new ways of characterizing biological research that should prove helpful to philosophers of science who seek to develop more pluralistic, pragmatic models of scientific inquiry. miRNA research displays iterative movements between multiple modes of investigation that include not only the proposal and testing of hypotheses but also exploratory, technology-oriented and question-driven modes of research. As an exemplary story of scientific discovery and development, the miRNA case illustrates transitions from genetics to genomics and systems biology, and it shows how diverse configurations of research practice are related to major scientific advances. (shrink)

Along three measurements at the Large Hadron Collider (LHC), a high energy particle accelerator, we analyze procedures and consequences of exploratory experimentation (EE). While all of these measurements fulfill the requirements of EE: probing new parameter spaces, being void of a target theory and applying a broad range of experimental methods, we identify epistemic differences and suggest a classification of EE. We distinguish classes of EE according to their respective goals: the exploration where an established global theory cannot provide the (...) details of a local phenomenon, exploration of an astonishing discovery and exploration to find a new entity. We find that these classes also differ with respect to the existence of an identifiable target and their impact on the background theory. The characteristics distinguish EE from other kinds of experimentation, even though these different kinds have not yet been systematically studied. The formal rigor and precision of LHC physics facilitates to analyze concept formation in its early state. In particular we emphasize the importance for nil–results for conceptualization and argue that conceptualization can also be achieved from nil–results only. (shrink)

In the second half of the nineteenth century, gas discharge research was transformed from a playful and fragmented field into a new branch of physical science and technology. From the 1850s onwards, several technical innovations – powerful high-voltage supplies, the enhancement of glass-blowing skills, or the introduction of mercury air-pumps – allowed for a major extension of experimental practices and expansion of the phenomenological field. Gas discharge tubes served as containers in which resources from various disciplinary contexts could be brought (...) together; along with the experimental apparatus built around them the tubes developed into increasingly complex interfaces mediating between the human senses and the micro-world. The focus of the following paper will be on the physicist and chemist Johann Wilhelm Hittorf , his educational background and his attempts to understand gaseous conduction as a process of interaction between electrical energy and matter. Hittorf started a long-term project in gas discharge research in the early 1860s. In his research he tried to combine a morphological exploration of gas discharge phenomena – aiming at the experimental production of a coherent phenomenological manifold – with the definition and precise measurements of physical properties. (shrink)

In addition to his experiments on selection in pure lines, Wilhelm Johannsen performed less well-known hybridisation experiments with beans. This article describes these experiments and discusses Johannsen’s motivations and interpretations, in the context of developments in early genetics. I will show that Johannsen first presented the hybridisation experiments as an additional control for his selection experiments. The latter were dedicated to investigating heredity with respect to debates concerning the significance of natural selection of continuous variation for evolution. In the course (...) of the establishment of a Mendelian research program after 1900, the study of heredity gained increasing independence from questions of evolution, and focused more on the modes and mechanisms of heredity. Further to their role as control experiments, Johannsen also saw his hybridisation experiments as contributing to the Mendelian program, by extending the scope of the principles of Mendelian inheritance to quantitative characters. Towards the end of the first decade of genetics, Johannsen revisited his experiments to illustrate the many–many relationship between genes and characters, at a time when that relationship appeared increasingly complex, and the unit-character concept, accordingly, became inadequate. For the philosophy of science, the example shows that experiments can have multiple roles in a research programme, and can be interpreted in the light of questions other than those that motivated the experiments in the first place. (shrink)

In this essay, we examine the use of resting state fMRI data for psychological inferences. We argue that resting state studies hold the paired promises of discovering novel functional brain networks, and of avoiding some of the limitations of task-based fMRI. However, we argue that the very features of experimental design that enable resting state fMRI to support exploratory science also generate a novel confound. We argue that seemingly key features of resting state functional connectivity networks may be artefacts resulting (...) from sampling a ‘mixture distribution’ of diverse brain networks active at different times during the scan. We explore the consequences of this ‘mixture view’ for attempts to theorize about the cognitive or psychological functions of resting state networks, as well as the value of exploratory experiments. (shrink)

In this article, I introduce the notion of horizon for scientific practice (HSP), representing limits or boundaries within which scientists ply their trade, to facilitate analysis of scientific discovery and progress. The notion includes not only constraints that delimit scientific practice, e.g. of bringing experimentation to a temporary conclusion, but also possibilities that open up scientific practice to additional scientific discovery and to further scientific progress. Importantly, it represents scientific practice as a dynamic and developmental integration of activities to investigate (...) and analyze the natural world. I use the discovery of the clotting factor, thrombin, and the experiments conducted by the Johns Hopkins physiologist, William Howell, on the enzymatic nature of thrombin to illustrate the notion of HSP. In a concluding section, I compare the notion of HSP to other notions for scientific practice proposed in the history and philosophy of science literature. (shrink)

The purpose of this paper is to show how the degree of experimental validity of scientific procedures is crucially involved in determining two typical pragmatic modes in science, namely, the preservation of useful procedures and the disposal of useless ideas. The term 'pragmatic' will here be used following Schurz's characterisation of being internally pragmatic, as referring to that which proves useful for scientific or epistemic goals. The first part of the paper consists in a characterisation of the notion of experimental (...) validity. The second part is focused on several historical examples illustrating how the above pragmatic modes relate to the question of experimental validity. The Michelson-Morley experiment is presented as a case of a highly valid and useful experiment preserved through the development of different theories (like, on the one hand, the ether theory, upheld by Fitzgerald and Lorentz, and, on the other hand, Einstein's special theory of relativity). The concept of caloric will be discussed as an example of an idea that was discarded once it became useless, after heat was understood as a form of energy within the new frame provided by the kinetic theory of heat towards the middle of the 19th century. (shrink)

A notion of experimental series is developed, in which experiments or experimental sets are connected through experimental suggestions arising from previous experimental outcomes. To that end, the justification of Howard Temin’s DNA provirus hypothesis is examined. The hypothesis originated with evidence from two exploratory experimental sets on an oncogenic virus and was substantiated by including evidence from three additional experimental sets. Collectively these sets comprise an experimental series and the accumulative evidence from the series was adequate to justify the hypothesis (...) by persuading the virology community of its veracity. The notion of crucial experiment is also discussed in terms of experimental series. (shrink)

A notion of experimental series is developed, in which experiments or experimental sets are connected through experimental suggestions arising from previous experimental outcomes. To that end, the justification of Howard Temin's DNA provirus hypothesis is examined. The hypothesis originated with evidence from two exploratory experimental sets on an oncogenic virus and was substantiated by including evidence from three additional experimental sets. Collectively these sets comprise an experimental series and the accumulative evidence from the series was adequate to justify the hypothesis (...) by persuading the virology community of its veracity. The notion of crucial experiment is also discussed in terms of experimental series. (shrink)

Observation and experiment as categories for analysing scientific practice have a long pedigree in writings on science. There has, however, been little attempt to delineate observation and experiment with respect to analysing scientific practice; in particular, scientific experimentation, in a systematic manner. Someone who has presented a systematic account of observation and experiment as categories for analysing scientific experimentation is Ian Hacking. In this paper, I present a detailed analysis of Hacking’s observation versus experiment account. Using a range of cases (...) from various fields of scientific enquiry, I argue that the observation versus experiment account is not an adequate framework for delineating scientific experimentation in a systematic manner. (shrink)

According to Joshua Alexander, philosophers use intuitions routinely as a form of evidence to test philosophical theories but experimental philosophy demonstrates that these intuitions are unreliable and unrepresentative.1 According to Herman Cappelen, philosophers never use intuitions as evidence (despite the vacuous sentential leader ‘intuitively’) and experimental philosophy lacks a rationale for its much-touted existence.2 That two books are diametrically opposed on methodology in philosophy is not noteworthy. But eyebrows might be raised at such contradictory accounts of the phenomenology of philosophical (...) inquiry. What is it that (analytic) philosophers do? Why is it so difficult to achieve consensus on the professional activities in which we engage? (shrink)

This paper proposes an account of scientific data that makes sense of recent debates on data-driven and ‘big data’ research, while also building on the history of data production and use particularly within biology. In this view, ‘data’ is a relational category applied to research outputs that are taken, at specific moments of inquiry, to provide evidence for knowledge claims of interest to the researchers involved. They do not have truth-value in and of themselves, nor can they be seen as (...) straightforward representations of given phenomena. Rather, they are fungible objects defined by their portability and prospective usefulness as evidence. (shrink)

I propose a framework that explicates and distinguishes the epistemic roles of data and models within empirical inquiry through consideration of their use in scientific practice. After arguing that Suppes’ characterization of data models falls short in this respect, I discuss a case of data processing within exploratory research in plant phenotyping and use it to highlight the difference between practices aimed to make data usable as evidence and practices aimed to use data to represent a specific phenomenon. I then (...) argue that whether a set of objects functions as data or models does not depend on intrinsic differences in their physical properties, level of abstraction or the degree of human intervention involved in generating them, but rather on their distinctive roles towards identifying and characterizing the targets of investigation. The paper thus proposes a characterization of data models that builds on Suppes’ attention to data practices, without however needing to posit a fixed hierarchy of data and models or a highly exclusionary definition of data models as statistical constructs. (shrink)

I propose a framework that explicates and distinguishes the epistemic roles of data and models within empirical inquiry through consideration of their use in scientific practice. After arguing that Suppes’ characterization of data models falls short in this respect, I discuss a case of data processing within exploratory research in plant phenotyping and use it to highlight the difference between practices aimed to make data usable as evidence and practices aimed to use data to represent a specific phenomenon. I then (...) argue that whether a set of objects functions as data or models does not depend on intrinsic differences in their physical properties, level of abstraction or the degree of human intervention involved in generating them, but rather on their distinctive roles towards identifying and characterizing the targets of investigation. The paper thus proposes a characterization of data models that builds on Suppes’ attention to data practices, without however needing to posit a fixed hierarchy of data and models or a highly exclusionary definition of data models as statistical constructs. (shrink)

This paper examines two parallel discussions of scientific modeling which have invoked experimentation in addressing the role of models in scientific inquiry. One side discusses the experimental character of models, whereas the other focuses on their exploratory uses. Although both relate modeling to experimentation, they do so differently. The former has considered the similarities and differences between models and experiments, addressing, in particular, the epistemic value of materiality. By contrast, the focus on exploratory modeling has highlighted the various kinds of (...) exploratory functions of models in the early stages of inquiry. These two perspectives on modeling are discussed through a case study in the field of synthetic biology. The research practice in question explores biological control by making use of an ensemble of different epistemic means: mathematical models and simulations, synthetic genetic circuits and intracellular measuring devices, and finally electronic circuits. We argue that the study of exploratory modeling should trace the ways different epistemic means, in different materialities, are being combined over time. Finally, the epistemic status of such novel investigative objects as synthetic genetic circuits is evaluated, with the conclusion that they can function as both experiments and models. (shrink)

This paper explores the case of using robots to simulate evolution, in particular the case of Hamilton’s Law. The uses of robots raises several questions that this paper seeks to address. The first concerns the role of the robots in biological research: do they simulate something or do they participate in something? The second question concerns the physicality of the robots: what difference does embodiment make to the role of the robot in these experiments. Thirdly, how do life, embodiment and (...) social behavior relate in contemporary biology and why is it possible for robots to illuminate this relation? These questions are provoked by a strange similarity that has not been noted before: between the problem of simulation in philosophy of science, and Deleuze’s reading of Plato on the relationship of ideas, copies and simulacra. (shrink)

ArgumentIn the theory-dominated view of scientific experimentation, all relations of theory and experiment are taken on a par; namely, that experiments are performed solely to ascertain the conclusions of scientific theories. As a result, different aspects of experimentation and of the relations of theory to experiment remain undifferentiated. This in turn fosters a notion of theory-ladenness of experimentation (TLE) that is toocoarse-grainedto accurately describe the relations of theory and experiment in scientific practice. By contrast, in this article, I suggest that (...) TLE should be understood as anumbrella conceptthat has different senses. To this end, I introduce a three-fold distinction among the theories of high-energy particle physics (HEP) as background theories, model theories, and phenomenological models. Drawing on this categorization, I contrast two types of experimentation, namely, “theory-driven” and “exploratory” experiments, and I distinguish between the “weak” and “strong” senses of TLE in the context of scattering experiments from the history of HEP. This distinction enables identifying the exploratory character of the deep-inelastic electron-proton scattering experiments – performed at the Stanford Linear Accelerator Center (SLAC) between the years 1967 and 1973 – thereby shedding light on a crucial phase of the history of HEP, namely, the discovery of “scaling,” which was the decisive step towards the construction of quantum chromo-dynamics as a gauge theory of strong interactions. (shrink)

In this paper, I propose an account that accommodates the possibility of experimentation being exploratory in cases where the procedures necessary to plan and perform an experiment are dependent on the theoretical accounts of the phenomena under investigation. The present account suggests that experimental exploration requires the implementation of an exploratory procedure that serves to extend the range of possible outcomes of an experiment, thereby enabling it to pursue its objectives. Furthermore, I argue that the present account subsumes the notion (...) of exploratory experimentation, which is often attributed in the relevant literature to the works of Friedrich Steinle and Richard Burian, as a particular type of experimental exploration carried out in the special cases where no well-formed theoretical framework of the phenomena under investigation exists. I illustrate the present account in the context of the ATLAS experiment at CERN’s Large Hadron Collider, where the long-sought Higgs boson has been discovered in 2012. I argue that the data selection procedure carried out in the ATLAS experiment illustrates an exploratory procedure in the sense suggested by the present account. I point out that this particular data selection procedure is theory-laden in that its implementation is crucially dependent on the theoretical models of high energy particle physics which the ATLAS experiment is aimed to test. However, I argue that the foregoing procedure is not driven by the above-mentioned theoretical models, but rather by a particular data selection strategy. I conclude that the ATLAS experiment illustrates that, contrary to what previous studies have suggested, there are cases of experimentation in which exploration serves to test theoretical predictions and that theory-ladenness plays an essential role in experimentation being exploratory. (shrink)

“Ancient DNA Research” is the practice of extracting, sequencing, and analyzing degraded DNA from dead organisms that are hundreds to thousands of years old. Today, many researchers are interested in adapting state-of-the-art molecular biological techniques and high-throughput sequencing technologies to optimize the recovery of DNA from fossils, then use it for studying evolutionary history. However, the recovery of DNA from fossils has also fueled the idea of resurrecting extinct species, especially as its emergence corresponded with the book and movie Jurassic (...) Park in the 1990s. In this paper, I use historical material, interviews with scientists, and philosophical literature to argue that the search for DNA from fossils can be characterized as a data-driven and celebrity-driven practice. Philosophers have recently argued the need to seriously consider the role of data-driven inquiry in the sciences, and likewise, this history highlights the need to seriously consider the role of celebrity in shaping the kind of research that gets pursued, funded, and ultimately completed. On this point, this history highlights that the traditional philosophical and scientific distinctions between data-driven and hypothesis-driven research are not always useful for understanding the process and practice of science. Consequently, I argue that the celebrity status of a particular research practice can be considered as a “serious epistemic strategy” that researchers, as well as editors and funders, employ when making choices about their research and publication processes. This interplay between celebrity and methodology matters for the epistemology of science. (shrink)

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

Exploratory inquiry has difficulty attracting research funding because funding agencies have little sense of how to detect good science in exploratory contexts. After documenting and explaining the focus on hypothesis testing among a variety of institutions responsible for distinguishing between good and bad science, I analyze the NIH grant review process. I argue that a good explanation for the focus on hypothesis testing—at least at the level of science funding agencies—is the fact that hypothesis-driven research is relatively easy to appraise. (...) I then explore one method by which we might gauge the epistemic merits of different styles of inquiry. (shrink)

The concept of the cortical column refers to vertical cell bands with similar response properties, which were initially observed by Vernon Mountcastle’s mapping of single cell recordings in the cat somatic cortex. It has subsequently guided over 50 years of neuroscientific research, in which fundamental questions about the modularity of the cortex and basic principles of sensory information processing were empirically investigated. Nevertheless, the status of the column remains controversial today, as skeptical commentators proclaim that the vertical cell bands are (...) a functionally insignificant by-product of ontogenetic development. This paper inquires how the column came to be viewed as an elementary unit of the cortex from Mountcastle’s discovery in 1955 until David Hubel and Torsten Wiesel’s reception of the Nobel Prize in 1981. I first argue that Mountcastle’s vertical electrode recordings served as criteria for applying the column concept to electrophysiological data. In contrast to previous authors, I claim that this move from electrophysiological data to the phenomenon of columnar responses was concept-laden, but not theory-laden. In the second part of the paper, I argue that Mountcastle’s criteria provided Hubel Wiesel with a conceptual outlook, i.e. it allowed them to anticipate columnar patterns in the cat and macaque visual cortex. I argue that in the late 1970s, this outlook only briefly took a form that one could call a ‘theory’ of the cerebral cortex, before new experimental techniques started to diversify column research. I end by showing how this account of early column research fits into a larger project that follows the conceptual development of the column into the present. (shrink)

In contemporary human brain mapping, it is commonly assumed that the “mind is what the brain does”. Based on that assumption, task-based imaging studies of the last three decades measured differences in brain activity that are thought to reflect the exercise of human mental capacities (e.g., perception, attention, memory). With the advancement of resting state studies, tractography and graph theory in the last decade, however, it became possible to study human brain connectivity without relying on cognitive tasks or constructs. It (...) therefore is currently an open question whether the assumption that “the mind is what the brain does” is an indispensable working hypothesis in human brain mapping. This paper argues that the hypothesis is, in fact, dispensable. If it is dropped, researchers can “meet the brain on its own terms” by searching for new, more adequate concepts to describe human brain organization. Neuroscientists can establish such concepts by conducting exploratory experiments that do not test particular cognitive hypotheses. The paper provides a systematic account of exploratory neuroscientific research that would allow researchers to form new concepts and formulate general principles of brain connectivity, and to combine connectivity studies with manipulation methods to identify neural entities in the brain. These research strategies would be most fruitful if applied to the mesoscopic scale of neuronal assemblies, since the organizational principles at this scale are currently largely unknown. This could help researchers to link microscopic and macroscopic evidence to provide a more comprehensive understanding of the human brain. The paper concludes by comparing this account of exploratory neuroscientific experiments to recent proposals for large-scale, discovery-based studies of human brain connectivity. (shrink)

Farmers all over the world perform experiments, and have done so since long before modern experimental science and its recognized forerunners. There is a rich anthropological literature on these experiments, but the philosophical issues that they give rise to have not received much attention. Based on the anthropological literature, this study investigates methodological and philosophical issues pertaining to farmers’ experiments, including the choice of interventions to be tested, the planning of experiments, and the use of control fields and other means (...) to deal with confounding factors. Farmers’ experiments have some advantages over the field trials of agricultural scientists, but also some comparative disadvantages. The two experimental traditions are complementary, and neither of them can replace the other. Several aspects of farmers’ experiments are shown to have a direct bearing on central topics in the philosophy of science. (shrink)

Farmers all over the world perform experiments, and have done so since long before modern experimental science and its recognized forerunners. There is a rich anthropological literature on these experiments, but the philosophical issues that they give rise to have not received much attention. Based on the anthropological literature, this study investigates methodological and philosophical issues pertaining to farmers’ experiments, including the choice of interventions to be tested, the planning of experiments, and the use of control fields and other means (...) to deal with confounding factors. Farmers’ experiments have some advantages over the field trials of agricultural scientists, but also some comparative disadvantages. The two experimental traditions are complementary, and neither of them can replace the other. Several aspects of farmers’ experiments are shown to have a direct bearing on central topics in the philosophy of science. (shrink)

Farmers all over the world perform experiments, and have done so since long before modern experimental science and its recognized forerunners. There is a rich anthropological literature on these experiments, but the philosophical issues that they give rise to have not received much attention. Based on the anthropological literature, this study investigates methodological and philosophical issues pertaining to farmers’ experiments, including the choice of interventions to be tested, the planning of experiments, and the use of control fields and other means (...) to deal with confounding factors. Farmers’ experiments have some advantages over the field trials of agricultural scientists, but also some comparative disadvantages. The two experimental traditions are complementary, and neither of them can replace the other. Several aspects of farmers’ experiments are shown to have a direct bearing on central topics in the philosophy of science. (shrink)

The life sciences are said to be in the midst of a replication crisis because a majority of published results are irreproducible, and scientists rarely replicate existing data. Here I argue that point 2 of this assessment is flawed because there is a hitherto unidentified form of replication in the experimental life sciences, which I call ‘microreplications’. Using a case study from biochemistry, I illustrate how MRs depend on a key element of experimentation, namely, experimental controls. I end by reflecting (...) on what MRs mean for the broader debate about the replication crisis. (shrink)

The 2011 Nobel Prize in Physics was awarded to researchers from the Supernova Cosmology Project and the High-z Supernova Search Team for the discovery of the accelerating expansion of the universe. In this paper, I provide a historical analysis of the supernova cosmology evidence put forward by these teams for the accelerating universe, in terms of an iterative model of scientific progress developed by Hasok Chang in the context of his study of the development of measurement standards. I argue, using (...) the key concept of epistemic iteration, that the iterative model adequately accounts for evidence production in experimental science as well. In order to apply Chang’s model to the experimental evidence for the accelerating universe, I introduce the concept of endorsement as a particular mode of progress, and argue that supernova scientists produced an endorsed measurement system to claim evidence for their discovery. Furthermore, I show that the credibility of the evidence was not based on a particular measurement, rather, what proved to be decisive was the “robust consistency” of many individual results. (shrink)

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

This paper explores the relation between scientific knowledge and common sense intuitions as a complement to Hoyningen-Huene’s account of systematicity. On one hand, Hoyningen-Huene embraces continuity between these in his characterization of scientific knowledge as an extension of everyday knowledge, distinguished by an increase in systematicity. On the other, he argues that scientific knowledge often comes to deviate from common sense as science develops. Specifically, he argues that a departure from common sense is a price we may have to pay (...) for increased systematicity. I argue that to clarify the relation between common sense and scientific reasoning, more attention to the cognitive aspects of learning and doing science is needed. As a step in this direction, I explore the potential for cross-fertilization between the discussions about conceptual change in science education and philosophy of science. Particularly, I examine debates on whether common sense intuitions facilitate or impede scientific reasoning. While contending that these debates can balance some of the assumptions made by Hoyningen-Huene, I suggest that a more contextualized version of systematicity theory could supplement cognitive analysis by clarifying important organizational aspects of science. (shrink)

The aim of this paper is to grasp the relevant distinctions between various ways in which models and simulations in Artificial Intelligence (AI) relate to cognitive phenomena. In order to get a systematic picture, a taxonomy is developed that is based on the coordinates of formal versus material analogies and theory-guided versus pre-theoretic models in science. These distinctions have parallels in the computational versus mimetic aspects and in analytic versus exploratory types of computer simulation. The proposed taxonomy cuts across the (...) traditional dichotomies between symbolic and embodied AI, general intelligence and symbol and intelligence and cognitive simulation and human/non-human-like AI. -/- According to the taxonomy proposed here, one can distinguish between four distinct general approaches that figured prominently in early and classical AI, and that have partly developed into distinct research programs: first, phenomenal simulations (e.g., Turing’s “imitation game”); second, simulations that explore general-level formal isomorphisms in pursuit of a general theory of intelligence (e.g., logic-based AI); third, simulations as exploratory material models that serve to develop theoretical accounts of cognitive processes (e.g., Marr’s stages of visual processing and classical connectionism); and fourth, simulations as strictly formal models of a theory of computation that postulates cognitive processes to be isomorphic with computational processes (strong symbolic AI). -/- In continuation of pragmatic views of the modes of modeling and simulating world affairs, this taxonomy of approaches to modeling in AI helps to elucidate how available computational concepts and simulational resources contribute to the modes of representation and theory development in AI research—and what made that research program uniquely dependent on them. (shrink)

-/- The aim of this paper is to grasp the relevant distinctions between various ways in which models and simulations in Artificial Intelligence (AI) relate to cognitive phenomena. In order to get a systematic picture, a taxonomy is developed that is based on the coordinates of formal versus material analogies and theory-guided versus pre-theoretic models in science. These distinctions have parallels in the computational versus mimetic aspects and in analytic versus exploratory types of computer simulation. The proposed taxonomy cuts across (...) the traditional dichotomies between symbolic and embodied AI, general intelligence and symbol and intelligence and cognitive simulation and human/non-human-like AI. -/- According to the taxonomy proposed here, one can distinguish between four distinct general approaches that figured prominently in early and classical AI, and that have partly developed into distinct research programs: first, phenomenal simulations (e.g., Turing’s “imitation game”); second, simulations that explore general-level formal isomorphisms in pursuit of a general theory of intelligence (e.g., logic-based AI); third, simulations as exploratory material models that serve to develop theoretical accounts of cognitive processes (e.g., Marr’s stages of visual processing and classical connectionism); and fourth, simulations as strictly formal models of a theory of computation that postulates cognitive processes to be isomorphic with computational processes (strong symbolic AI). -/- In continuation of pragmatic views of the modes of modeling and simulating world affairs, this taxonomy of approaches to modeling in AI helps to elucidate how available computational concepts and simulational resources contribute to the modes of representation and theory development in AI research—and what made that research program uniquely dependent on them. (shrink)

Summary We offer a novel historical-philosophical framework for discussing experimental practice which we call ‘Generating Experimental Knowledge’. It combines three different perspectives: experimental systems, concept formation, and the pivotal role of error. We then present an historical account of the invention of the Scanning Tunnelling Microscope (STM), or Raster-Tunnelmikroskop, and interpret it within the proposed framework. We show that at the outset of the STM project, Binnig and Rohrer—the inventors of the machine—filed two patent disclosures; the first is dated 22 (...) December 1978 (Switzerland), and the second, two years later, 12 September 1980 (US). By studying closely these patent disclosures, the attempts to realize them, and the subsequent development of the machine, we present, within the framework of generating experimental knowledge, a new account of the invention of the STM. While the realization of the STM was still a long way off, the patent disclosures served as blueprints, marking the changes that had to be introduced on the way from the initial idea to its realization. (shrink)

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

In the late 19th century, physiologists such as David Ferrier, Eduard Hitzig, and Hermann Munk argued that cerebral brain functions are localized in discrete structures. By the early 20th century, this became the dominant position. However, another prominent physiologist, Friedrich Goltz, rejected theories of cerebral localization and argued against these physiologists until his death in 1902. I argue in this paper that previous historical accounts have failed to comprehend why Goltz rejected cerebral localization. I show that Goltz adhered to a (...) falsificationist methodology, and I reconstruct how he designed his experiments and weighted different kinds of evidence. I then draw on the exploratory experimentation literature from recent philosophy of science to trace one root of the debate to differences in how the German localizers designed their experiments and reasoned about evidence. While Goltz designed his experiments to test hypotheses about the functions of predetermined cerebral structures, the localizers explored new functions and structures in the process of constructing new theories. I argue that the localizers relied on untested background conjectures to justify their inferences about functional organization. These background conjectures collapsed a distinction between phenomena they produced direct evidence for (localized symptoms) and what they reached conclusions about (localized functions). (shrink)

Philosophers of science diverge on the question what drives the growth of scientific knowledge. Most of the twentieth century was dominated by the notion that theories propel that growth whereas experiments play secondary roles of operating within the theoretical framework or testing theoretical predictions. New experimentalism, a school of thought pioneered by Ian Hacking in the early 1980s, challenged this view by arguing that theory-free exploratory experimentation may in many cases effectively probe nature and potentially spawn higher evidence-based theories. Because (...) theories are often powerless to envisage workings of complex biological systems, theory-independent experimentation is common in the life sciences. Some such experiments are triggered by compelling observation, others are prompted by innovative techniques or instruments, whereas different investigations query big data to identify regularities and underlying organizing principles. A distinct fourth type of experiments is motivated by a major question. Here I describe two question-guided experimental discoveries in biochemistry: the cyclic adenosine monophosphate mediator of hormone action and the ubiquitin-mediated system of protein degradation. Lacking underlying theories, antecedent data bases, or new techniques, the sole guides of the two discoveries were respective substantial questions. Both research projects were similarly instigated by theory-free exploratory experimentation and continued in alternating phases of results-based interim working hypotheses, their examination by experiment, provisional hypotheses again, and so on. These two cases designate theory-free, question-guided, stepwise biochemical investigations as a distinct subtype of the new experimentalism mode of scientific enquiry. (shrink)

Theory-ladenness is the view that observation cannot function in an unbiased way in the testing of theories because observational judgments are affected by the theoretical beliefs of the observer. Its more radical cousin, incommensurability, argues that because there is no theory-neutral language, paradigms, or worldviews, cannot be compared because in different paradigms the meaning of observational terms is different, even when the word used is the same. There are both philosophical and practical components to these problems. I argue, using a (...) procedurally-defined, theory-neutral experiment that paradigms are indeed commensurable. The practical problems of theory ladenness include experimental design, failure to interpret observations correctly, possible experimenter bias, and difficulties in data acquisition. I suggest that there are methods to deal with these problems, although sometimes they cannot be dealt with completely. I believe that the philosophical problems of theory-ladenness have been solved, although the practical problems remain. (shrink)

Philosophers of experiment have acknowledged that experiments are often more than mere hypothesis-tests, once thought to be an experiment's exclusive calling. Drawing on examples from contemporary biology, I make an additional amendment to our understanding of experiment by examining the way that `wide' instrumentation can, for reasons of efficiency, lead scientists away from traditional hypothesis-directed methods of experimentation and towards exploratory methods.

That science is more than the unilinear application of general theories to specific empirical circumstances is, one hopes, no longer something that is controversial or requires detailed argument. To be sure, there were times when devising universally applicable theories was seen as the most worthy task of science, with less lofty activities such as experimentation and scientific modeling being relegated to the underbelly of “proper science.” Arguing for a pluralistic recognition of the diversity of scientific practices, methods, and goals, might—at (...) least on the pages of this journal—amount to preaching to the converted. Yet, once the diversity and heterogeneity of science is acknowledged, the real work only starts:... (shrink)

Scientists are becoming increasingly aware of a “replicability crisis” in the behavioral, social, and biomedical sciences. Researchers have made progress identifying statistical and methodological...

This paper provides an interpretation of Hans-Jörg Rheinberger’s notions of epistemic things and historical epistemology . I argue that Rheinberger’s approach articulates a unique contribution to current debates about integrated HPS, and I propose some modifications and extensions of this contribution. Drawing on examples from memory research, I show that Rheinberger is right to highlight a particular feature of many objects of empirical research (“epistemic things”)—especially in the contexts of exploratory experimentation—namely our lack of knowledge about them. I argue that (...) this analysis needs to be supplemented with an account of what scientists do know, and in particular, how they are able to attribute rudimentary empirical contours to objects of research. These contours are closely connected to paradigmatic research designs, which in turn are tied to basic methodological rules for the exploration of the purported phenomena. I suggest that we engage with such rules in order to develop our own normative (epistemological) categories, and I tie this proposal to the idea of a methodological naturalism in philosophy of science. (shrink)

While the term “reactivity” has come to be associated with specific phenomena in the social sciences, having to do with subjects’ awareness of being studied, this paper takes a broader stance on this concept. I argue that reactivity is a ubiquitous feature of the psychological subject matter and that this fact is a precondition of experimental research, while also posing potential problems for the experimenter. The latter are connected to the worry about distorted data and experimental artifacts. But what are (...) experimental artifacts and what is the most productive way of dealing with them? In this paper, I approach these questions by exploring the ways in which experimenters in psychology simultaneously exploit and suppress the reactivity of their subject matter in order to produce experimental data that speak to the question or subject matter at hand. Highlighting the artificiality of experimental data, I raise the question of what distinguishes a genuine experimental result from an experimental artifact. My analysis construes experimental results as the outcomes of inferences from the data that take material background assumptions as auxiliary premises. Artifacts occur when one or more of these background assumptions are false, such that the data do not reliably serve the purposes they were generated for. I conclude by laying out the ways in which my analysis of data quality is relevant to, and informed by, recent debates about the replicability of experimental results. (shrink)