Experimentation in Science

The scientific concepts of mental imagery and hallucinations are each used independently of the other; uses that simultaneously evoke and obscure their historical connections. In this paper, I aim to illustrate the relevance of examining one of these historical connections for studying the current uses of these two concepts in neuroimaging experiments. To this end, I will highlight interdependent associations within the histories of each of the concepts that continue to contribute to their independent uses.That mental imagery and hallucinations are (...) used independently of each other is evident in the way that each concept influences investigations in isolation of the other. Within the neurosciences, mental imagery is... (shrink)

Ontology as a branch of philosophy is the science of what is, of the kinds and structures of objects, properties, events, processes and relations in every area of reality. ‘Ontology’ in this sense is often used by philosophers as a synonym of ‘metaphysics’ (a label meaning literally: ‘what comes after the Physics’), a term used by early students of Aristotle to refer to what Aristotle himself called ‘first philosophy’. But in recent years, in a development hardly noticed by philosophers, the (...) term ‘ontology’ has gained currency in the field of computer and information science, and in information-driven research in bioinformatics and related areas. We examine these new developments in applied ontology, and show what lessons they might have for both philosophers and information scientists. (shrink)

With his distinction between the "context of discovery" and the "context of justification", Hans Reichenbach gave the traditional difference between genesis and validity a modern standard formulation. Reichenbach's distinction is one of the well-known ways in which the expression "context" is used in the theory of science. My argument is that Reichenbach's concept is unsuitable and leads to contradictions in the semantic fields of genesis and validity. I would like to demonstrate this by examining the different meanings of Reichenbach's context (...) distinction. My investigation also shows how the difference between genesis and validity precedes Reichenbach's context distinction and indicates approaches for meaningful applications of the concept of context to the phenomena designated by Reichenbach. I will begin by reconstructing the way in which Reichenbach introduces the distinction between discovery and justification as a difference of contexts (I). Drawing on the numerous meanings of the term "context", I will then emphasize some chief characteristics and review, through exemplification, the usage of this term. First of all, I turn to the context of discovery as the nonrational part of all scientific knowledge and show that this meaning cannot be defined consistently (la). For the context of justification, one can distinguish two main cases: the context of justification is either contrasted with the context of discovery, or it forms a unit there with. In the first case, the use of the context term becomes paradoxical, insofar as justification separated from scientific practice does not represent a field of reference which could be specifically contrasted with another field of reference (I b). In the second case, the unifying definitions contradict the contextual meaning of discovery and justification (1 c). In the last section, I point to a useful application of the concept of context which can be found in Reichenbach's argumentation and which refers to the practical conditions of justification(2). (shrink)

This book describes how scientists bring their own interests and passions to their work, illustrates the dynamics between researchers and the research community ...

Optogenetic techniques are described as “revolutionary” for the unprecedented causal control they allow neuroscientists to exert over neural activity in awake behaving animals. In this paper, I demonstrate by means of a case study that optogenetic techniques will only illuminate causal links between the brain and behavior to the extent that their error characteristics are known and, further, that determining these error characteristics requires comparison of optogenetic techniques with techniques having well known error characteristics and consideration of the broader neural (...) and behavioral context in which the targets of optogenetic interventions are situated. (shrink)

The use of neuroscientific evidence in criminal trials has been steadily increasing. Despite progress made in recent decades in understanding the mechanisms of psychological and behavioral functioning, neuroscience is still in an early stage of development and its potential for influencing legal decision-making is highly contentious. Scholars disagree about whether or how neuroscientific evidence might impact prescriptions of criminal culpability, particularly in instances in which evidence of an accused’s history of mental illness or brain abnormality is offered to support a (...) plea of not criminally responsible. In the context of these debates, philosophers and legal scholars have identified numerous problems with admitting neuroscientific evidence in legal contexts. To date, however, less has been said about the challenges of evaluating the evidence upon which integrative mechanistic explanations that bring together evidence from different areas of neuroscience are based. As we explain, current criteria for evaluating such evidence to determine its admissibility in legal contexts are inadequate. Appealing to literature in the philosophy of scientific experimentation and theoretical work in the social, cognitive and behavioral sciences, we lay the groundwork for reforming these criteria and identify some of the implications of modifying them. (shrink)

Review: 'Half an original interpretation of Heidegger's early work and half an attempt to buttress the sociology of scientific knowledge (SSK) with a more philosophically rigorous grounding, Science as Social Existence will be of interest not only to Heidegger scholars but to anyone engaged in science and technology studies. [...] This is an informative and original book. Kochan should be praised for his clear, pleasant-to-read prose.' (Michael Butler, University of Texas Rio Grande Valley, for CHOICE).

Building upon Nancy Cartwright's discussion of models in How the Laws of Physics Lie, this paper addresses solid state research in transition metal oxides. Historical analysis reveals that in this domain models function both as the culmination of phenomenology and the commencement of theoretical explanation. Those solid state chemists who concentrate on the description of phenomena pertinent to specific elements or compounds assess models according to different standards than those who seek explanation grounded in approximate applications of the Schroedinger equation. (...) Accurate accounts of scientific debate in this field must include both perspectives. (shrink)

According to the PubMed resource from the U.S. National Library of Medicine, over 750,000 scientific articles have been published in the ~5000 biomedical journals worldwide in the year 2007 alone. The vast majority of these publications include results from hypothesis-driven experimentation in overlapping biomedical research domains. Unfortunately, the sheer volume of information being generated by the biomedical research enterprise has made it virtually impossible for investigators to stay aware of the latest findings in their domain of interest, let alone to (...) be able to assimilate and mine data from related investigations for purposes of meta-analysis. While computers have the potential for assisting investigators in the extraction, management and analysis of these data, information contained in the traditional journal publication is still largely unstructured, free-text descriptions of study design, experimental application and results interpretation, making it difficult for computers to gain access to the content of what is being conveyed without significant manual intervention. In order to circumvent these roadblocks and make the most of the output from the biomedical research enterprise, a variety of related standards in knowledge representation are being developed, proposed and adopted in the biomedical community. In this chapter, we will explore the current status of efforts to develop minimum information standards for the representation of a biomedical experiment, ontologies composed of shared vocabularies assembled into subsumption hierarchical structures, and extensible relational data models that link the information components together in a machine-readable and human-useable framework for data mining purposes. (shrink)

This monographic chapter explains how expected utility (EU) theory arose in von Neumann and Morgenstern, how it was called into question by Allais and others, and how it gave way to non-EU theories, at least among the specialized quarters of decion theory. I organize the narrative around the idea that the successive theoretical moves amounted to resolving Duhem-Quine underdetermination problems, so they can be assessed in terms of the philosophical recommendations made to overcome these problems. I actually follow Duhem's recommendation, (...) which was essentially to rely on the passing of time to make many experiments and arguments available, and evebntually strike a balance between competing theories on the basis of this improved knowledge. Although Duhem's solution seems disappointingly vague, relying as it does on "bon sens" to bring an end to the temporal process, I do not think there is any better one in the philosophical literature, and I apply it here for what it is worth. In this perspective, EU theorists were justified in resisting the first attempts at refuting their theory, including Allais's in the 50s, but they would have lacked "bon sens" in not acknowledging their defeat in the 80s, after the long process of pros and cons had sufficiently matured. This primary Duhemian theme is actually combined with a secondary theme - normativity. I suggest that EU theory was normative at its very beginning and has remained so all along, and I express dissatisfaction with the orthodox view that it could be treated as a straightforward descriptive theory for purposes of prediction and scientific test. This view is usually accompanied with a faulty historical reconstruction, according to which EU theorists initially formulated the VNM axioms descriptively and retreated to a normative construal once they fell threatened by empirical refutation. From my historical study, things did not evolve in this way, and the theory was both proposed and rebutted on the basis of normative arguments already in the 1950s. The ensuing, major problem was to make choice experiments compatible with this inherently normative feature of theory. Compability was obtained in some experiments, but implicitly and somewhat confusingly, for instance by excluding overtly incoherent subjects or by creating strong incentives for the subjects to reflect on the questions and provide answers they would be able to defend. I also claim that Allais had an intuition of how to combine testability and normativity, unlike most later experimenters, and that it would have been more fruitful to work from his intuition than to make choice experiments of the naively empirical style that flourished after him. In sum, it can be said that the underdetermination process accompanying EUT was resolved in a Duhemian way, but this was not without major inefficiencies. To embody explicit rationality considerations into experimental schemes right from the beginning would have limited the scope of empirical research, avoided wasting resources to get only minor findings, and speeded up the Duhemian process of groping towards a choice among competing theories. (shrink)

There is widespread recognition at universities that a proper understanding of science is needed for all undergraduates. Good jobs are increasingly found in fields related to Science, Technology, Engineering, and Medicine, and science now enters almost all aspects of our daily lives. For these reasons, scientific literacy and an understanding of scientific methodology are a foundational part of any undergraduate education. Recipes for Science provides an accessible introduction to the main concepts and methods of scientific reasoning. With the help of (...) an array of contemporary and historical examples, definitions, visual aids, and exercises for active learning, the textbook helps to increase students’ scientific literacy. The first part of the book covers the definitive features of science: naturalism, experimentation, modeling, and the merits and shortcomings of both activities. The second part covers the main forms of inference in science: deductive, inductive, abductive, probabilistic, statistical, and causal. The book concludes with a discussion of explanation, theorizing and theory-change, and the relationship between science and society. The textbook is designed to be adaptable to a wide variety of different kinds of courses. In any of these different uses, the book helps students better navigate our scientific, 21st-century world, and it lays the foundation for more advanced undergraduate coursework in a wide variety of liberal arts and science courses. Selling Points Helps students develop scientific literacy—an essential aspect of _any_ undergraduate education in the 21 st century, including a broad understanding of scientific reasoning, methods, and concepts Written for all beginning college students: preparing science majors for more focused work in particular science; introducing the humanities’ investigations of science; and helping non-science majors become more sophisticated consumers of scientific information Provides an abundance of both contemporary and historical examples Covers reasoning strategies and norms applicable in all fields of physical, life, and social sciences, _as well as_ strategies and norms distinctive of specific sciences Includes visual aids to clarify and illustrate ideas Provides text boxes with related topics and helpful definitions of key terms, and includes a final Glossary with all key terms Includes Exercises for Active Learning at the end of each chapter, which will ensure full student engagement and mastery of the information include earlier in the chapter Provides annotated ‘For Further Reading’ sections at the end of each chapter, guiding students to the best primary and secondary sources available Offers a Companion Website, with: For Students: direct links to many of the primary sources discussed in the text, student self-check assessments, a bank of exam questions, and ideas for extended out-of-class projects For Instructors: a password-protected Teacher’s Manual, which provides student exam questions with answers, extensive lecture notes, classroom-ready Power Point presentations, and sample syllabi Extensive Curricular Development materials, helping any instructor who needs to create a Scientific Reasoning Course, ex nihilo. (shrink)

Monoclonal antibodies are essential biomedical research and clinical reagents that are produced by companies and research laboratories. The NIAID ImmPort (Immunology Database and Analysis Portal) resource provides a long-term, sustainable data warehouse for immunological data generated by NIAID, DAIT and DMID funded investigators for data archiving and re-use. A variety of immunological data is generated using techniques that rely upon monoclonal antibody reagents, including flow cytometry, immunofluorescence, and ELISA. In order to facilitate querying, integration, and reuse of data, standardized terminology (...) for describing monoclonal antibody reagents and their targets needs to be used for annotating data submitted to ImmPort. (shrink)

Helmholtz's public reflection about the nature of the experiment and its role in the sciences is a historically important description, which also helps to analyze his own works. It is a part of his conception of science and nature, which can be seen as an ideal type of science and its goals. But its historical reach seems to be limited in an important respect. Helmholtz's understanding of experiments is based on the idea that their planning, realization and evaluation lies in (...) the hands of a person or group acting according to decisions of free will. In my opinion this idea is characteristic for the foundation of the experimental method in early modern times, not however for several forms of its present structures. Above all, the increasing technization of producing knowledge reduces the roIe of the subject in conducting experiments. My lecture consists of three parts. In its first part I would like to present a summary of Helmholtz's own theory of experiment and the change of his conception of science and nature. In the second part I would like to discuss three examples of his experimental practice, which were taken in chronological order from three different periods of his work; in the third part I would like to compare the examples with the change of his conception of science and nature. (shrink)

Statistics play a critical role in biological and clinical research. However, most reports of scientific results in the published literature make it difficult for the reader to reproduce the statistical analyses performed in achieving those results because they provide inadequate documentation of the statistical tests and algorithms applied. The Ontology of Biological and Clinical Statistics (OBCS) is put forward here as a step towards solving this problem. Terms in OBCS, including ‘data collection’, ‘data transformation in statistics’, ‘data visualization’, ‘statistical data (...) analysis’, and ‘drawing a conclusion based on data’, cover the major types of statistical processes used in basic biological research and clinical outcome studies. OBCS is aligned with the Basic Formal Ontology (BFO) and extends the Ontology of Biomedical Investigations (OBI), an OBO (Open Biological and Biomedical Ontologies) Foundry ontology supported by over 20 research communities. We discuss two examples illustrating how the ontology is being applied. In the first (biological) use case, we describe how OBCS was applied to represent the high throughput microarray data analysis of immunological transcriptional profiles in human subjects vaccinated with an influenza vaccine. In the second (clinical outcomes) use case, we applied OBCS to represent the processing of electronic health care data to determine the associations between hospital staffing levels and patient mortality. Our case studies were designed to show how OBCS can be used for the consistent representation of statistical analysis pipelines under two different research paradigms. By representing statistics-related terms and their relations in a rigorous fashion, OBCS facilitates standard data analysis and integration, and supports reproducible biological and clinical research. (shrink)

The last two decades have seen a rising interest in (a) the notion of a scientific phenomenon as distinct from theories and data, and (b) the intricacies of experimentally producing and stabilizing phenomena. This paper develops an analysis of the stabilization of phenomena that integrates two aspects that have largely been treated separately in the literature: one concerns the skills required for empirical work; the other concerns the strategies by which claims about phenomena are validated. I argue that in order (...) to make sense of the process of stabilization, we need to distinguish between two types of phenomena: phenomena as patterns in the data ( surface regularities ) and phenomena as underlying (or hidden ) regularities. I show that the epistemic relationships that data bear to each of these types of phenomena are different: Data patterns are instantiated by individual data, whereas underlying regularities are indicated by individual data, insofar as they instantiate a data pattern. Drawing on an example from memory research, I argue that neither of these two kinds of phenomenon can be stabilized in isolation. I conclude that what is stabilized when phenomena are stabilized is the fit between surface regularities and hidden regularities. (shrink)

Harms of medical interventions are systematically underestimated in clinical research. Numerous factors—conceptual, methodological, and social—contribute to this underestimation. I articulate the depth of such underestimation by describing these factors at the various stages of clinical research. Before any evidence is gathered, the ways harms are operationalized in clinical research contributes to their underestimation. Medical interventions are first tested in phase 1 ‘first in human’ trials, but evidence from these trials is rarely published, despite the fact that such trials provide the (...) foundation for assessing the harm profile of medical interventions. If a medical intervention is deemed safe in a phase 1 trial, it is tested in larger phase 2 and 3 clinical trials. One way to think about the problem of underestimating harms is in terms of the statistical ‘power’ of a clinical trial—the ability of a trial to detect a difference of a certain effect size between the experimental group and the control group. Power is normally thought to be pertinent to detecting benefits of medical interventions. It is important, though, to distinguish between the ability of a trial to detect benefits and the ability of a trial to detect harms. I refer to the former as power-B and the latter as power-H. I identify several factors that maximize power-B by sacrificing powerH in phase 3 clinical trials. If a medical intervention is approved for general use, it is evaluated by phase 4 post-market surveillance. Phase 4 surveillance of harms further contributes to underestimating the harm profile of medical interventions. At every stage of clinical research the hunt for harms is shrouded in secrecy, which further contributes to the underestimation of the harm profiles of medical interventions. (shrink)

I praise Franklin's full descriptions of important and exemplary experiments, and wish that he had said more about why they are exemplary.

Throughout the biological and biomedical sciences there is a growing need for, prescriptive ‘minimum information’ (MI) checklists specifying the key information to include when reporting experimental results are beginning to find favor with experimentalists, analysts, publishers and funders alike. Such checklists aim to ensure that methods, data, analyses and results are described to a level sufficient to support the unambiguous interpretation, sophisticated search, reanalysis and experimental corroboration and reuse of data sets, facilitating the extraction of maximum value from data sets (...) them. However, such ‘minimum information’ MI checklists are usually developed independently by groups working within representatives of particular biologically- or technologically-delineated domains. Consequently, an overview of the full range of checklists can be difficult to establish without intensive searching, and even tracking thetheir individual evolution of single checklists may be a non-trivial exercise. Checklists are also inevitably partially redundant when measured one against another, and where they overlap is far from straightforward. Furthermore, conflicts in scope and arbitrary decisions on wording and sub-structuring make integration difficult. This presents inhibit their use in combination. Overall, these issues present significant difficulties for the users of checklists, especially those in areas such as systems biology, who routinely combine information from multiple biological domains and technology platforms. To address all of the above, we present MIBBI (Minimum Information for Biological and Biomedical Investigations); a web-based communal resource for such checklists, designed to act as a ‘one-stop shop’ for those exploring the range of extant checklist projects, and to foster collaborative, integrative development and ultimately promote gradual integration of checklists. (shrink)

The Ontology for Biomedical Investigations (OBI) is an ontology that provides terms with precisely defined meanings to describe all aspects of how investigations in the biological and medical domains are conducted. OBI re-uses ontologies that provide a representation of biomedical knowledge from the Open Biological and Biomedical Ontologies (OBO) project and adds the ability to describe how this knowledge was derived. We here describe the state of OBI and several applications that are using it, such as adding semantic expressivity to (...) existing databases, building data entry forms, and enabling interoperability between knowledge resources. OBI covers all phases of the investigation process, such as planning, execution and reporting. It represents information and material entities that participate in these processes, as well as roles and functions. Prior to OBI, it was not possible to use a single internally consistent resource that could be applied to multiple types of experiments for these applications. OBI has made this possible by creating terms for entities involved in biological and medical investigations and by importing parts of other biomedical ontologies such as GO, Chemical Entities of Biological Interest (ChEBI) and Phenotype Attribute and Trait Ontology (PATO) without altering their meaning. OBI is being used in a wide range of projects covering genomics, multi-omics, immunology, and catalogs of services. OBI has also spawned other ontologies (Information Artifact Ontology) and methods for importing parts of ontologies (Minimum information to reference an external ontology term (MIREOT)). The OBI project is an open cross-disciplinary collaborative effort, encompassing multiple research communities from around the globe. To date, OBI has created 2366 classes and 40 relations along with textual and formal definitions. The OBI Consortium maintains a web resource providing details on the people, policies, and issues being addressed in association with OBI. (shrink)

Experiments are actions, performed in order to gain information. Like other acts, there are virtues of performing them well. I discuss one virtue of experimentation, that of knowing how to trade its information-gaining potential against other goods.

Some experiments in perceptual psychology measure perceivers’ phenomenal experiences of objects versus their cognitive assessments of object properties. Analyzing such experiments, this article responds to Pizlo’s claim that much work on shape constancy before 1985 confused problems of shape ambiguity with problems of shape constancy. Pizlo fails to grasp the logic of experimental designs directed toward phenomenal aspects of shape constancy. In the domain of size perception, Granrud’s studies of size constancy in children and adults distinguish phenomenal from cognitive factors.

Seit Beginn der frühen Neuzeit ist das naturwissenschaftliche Verfahren maßgeblich durch ein neues Konzept geprägt: das Konzept des experimentellen, gestalterischen Eingriffs in die Natur. Es geht nun nicht mehr darum, eine Geschichte der "freien und ungebundenen Natur" (Bacon) zu erzählen, die in ihrem eigenen Lauf belassen und als vollkommene Bildung betrachtet wird. Es geht vielmehr darum, der "gebundenen und bezwungenen Natur" (Bacon) vermittels der experimentellen Tätigkeit des Menschen die Geheimnisse zu entreißen. Diese technisch-praktische Konzeption grenzt sich explizit von den klassischen (...) kontemplativen Wissenschaftsvorstellungen der Antike ab. Wie es Kant paradigmatisch in Bezug auf Bacon formuliert hat, ist diese "Revolution der Denkart" maßgeblich durch ein gewandeltes Verständnis des Verhältnisses des Menschen zur Natur geprägt. Der Mensch als Experimentator hat für Kant nicht mehr die "Qualität eines Schülers", der sich passiv von der Natur belehren läßt und an ihrem "Leitbande" (Kant) gegängelt wird. Seine neu gewonnene Autorität verleiht ihm vielmehr den Status eines Richters, der nun die Natur nötigen kann, auf gestellte Fragen zu antworten. Die Laborforschung der modernen Naturwissenschaft ist von den Formen der alten Naturwissenschaft so weit entfernt, daß sie den Vorwurf auf sich zog, sie untersuche Artekakte, aber nicht Natur. Die grundlegenden Theorien über Natur können in der Regel nur unter den künstlichen Bedingungen des Labors aufgestellt werden. Daraus darf aber nicht geschlossen werden, die anhand von Laborphänomenen aufgestellten und getesteten Theorien handelten nicht von der Natur außerhalb der Labore. Aber ihrer exakten und detaillierten Anwendung auf Prozesse außerhalb der Labore stehen eine Fülle von Schwierigkeiten entgegen. Insofern markiert das Labor sehr wohl eine Grenze exakter Naturforschung, die für den Umgang der wissenschaftlich-technischen Zivilisation mit der Natur wichtige Konsequenzen hat. Inhalt: Kristian Köchy / Gregor Schiemann: Natur im Labor Lothar Schäfer: Die Erscheinung der Natur unter Laborbedingungen Holm Tetens: Das Labor als Grenze der exakten Naturforschung Christoph Rehmann-Sutter: Genes in Labs - Concepts of Development and the Standard Environment Kristian Köchy: Lebewesen im Labor. Das Experiment in der Biologie Jutta Weber: Mannigfaltige Techno-Naturen. Von epistemischen Modellsystemen und situierten Maschinen Thomas Sören Hoffmann: Gezeigte versus sich zeigende Natur. Eine Skizze im Blick auf das Verhältnis von Labor und Natur Klaus Michael Meyer-Abich: Laborforschung im Erkenntnishandeln der Experimentiergesellschaft. Eine holistisch-pragmatische Perspektive für die Wissenschaftstheorie. (shrink)

What makes a high-quality biomarker experiment? The success of personalized medicine hinges on the answer to this question. In this paper, I argue that judgment about the quality of biomarker experiments is mediated by the problem of theoretical underdetermination. That is, the network of biological and pathophysiological theories motivating a biomarker experiment is sufficiently complicated that it often frustrates valid interpretation of the experimental results. Drawing on a case-study in biomarker diagnostic development from neurooncology, I argue that this problem of (...) underdetermination can be overcome with greater coordination across the biomarker research trajectory. I then sketch an account for how coordination across a research trajectory can be evaluated. I ultimate conclude that what makes a high-quality biomarker experiment must be judged by the epistemic contribution it makes to this coordinated research effort. (shrink)

Nach einer kurzen Erinnerung an einige von Keplers Hauptwerken, in denen traditionelle und moderne Elemente eingehen (Abschnitt 1), wird zwei Beispielen die Differenz zwischen diesen beiden Elementen näher untersucht. Das erste Beispiel, Keplers Naturbegriff, dient zur Diskussion der Kritik qualitativer Unterscheidungen. Hierbei stehen Keplers Verhältnis zur aristotelischen Naturauffassung und die Relevanz dieser Relation für die moderne Wissenschaftsauffassung im Mittelpunkt (Abschnitt 2). Das andere Beispiel befasst sich mit dem absoluten Wahrheitsanspruch von Keplers Wissenschaft und rückt damit exemplarisch eine Differenz zur modernen (...) Wissenschaftsauffassung in den Vordergrund (Abschnitt 3). Anschließend werden umfassender traditionelle Elemente der frühneuzeitlichen Wissenschaft, wie sie Kepler vertrat, dem modernen Wissenschaftsverständnis gegenübergestellt. Nachdem damit die Entfernung Keplers zur Gegenwart gleichsam maximiert ist, wende ich mich den Wissenschaftsauffassungen von Wolfgang Pauli und Werner Heisenberg zu, die in bemerkenswerter Nähe zu Keplers vormodernen Ansichten stehen und doch ganz im Kontext der Moderne entwickelt wurden (Abschnitt 4). Obwohl also in jüngster Zeit ganz differente Einstellungen zu Keplers Verhältnis zur modernen Wissenschaft vertreten wurden, lässt sich doch eine Tendenz zur Abstandsvergrößerung in dieser Relation ausmachen (Abschnitt 5). (shrink)

According to Ian Hacking’s Entity Realism, unobservable entities that scientists carefully manipulate to study other phenomena are real. Although Hacking presents his case in an intuitive, attractive, and persuasive way, his argument remains elusive. I present five possible readings of Hacking’s argument: a no-miracle argument, an indispensability argument, a transcendental argument, a Vichian argument, and a non-argument. I elucidate Hacking’s argument according to each reading, and review their strengths, their weaknesses, and their compatibility with each other.

In this chapter, I argue that scientific practice in the neurosciences of cognition is not conducive to the discovery of natural kinds of cognitive capacities. The “neurosciences of cognition” include cognitive neuroscience and cognitive neurobiology, two research areas that aim to understand how the brain gives rise to cognition and behavior. Some philosophers of neuroscience have claimed that explanatory progress in these research areas ultimately will result in the discovery of the underlying mechanisms of cognitive capacities. Once such mechanistic understanding (...) is achieved, cognitive capacities purportedly will be relegated into natural kind categories that correspond to real divisions in the causal structure of the world. I provide reasons here, however, in support of the claim that the neurosciences of cognition currently are not on a trajectory for discovering natural kinds. As I explain, this has to do with how mechanistic explanations of cognitive capacities are developed. Mechanistic explanations and the kinds they explain are abstract representational byproducts of the conceptual, experimental and integrative practices of neuroscientists. If these practices are not coordinated towards developing mechanistic explanations that mirror the causal structure of the world, then natural kinds of cognitive capacities will not be discovered. I provide reasons to think that such coordination is currently lacking in the neurosciences of cognition and indicate where changes in these practices appropriate to the natural kinds ideal would be required if achieving this ideal is indeed the goal. However, I suggest that an evaluation of current practices in these research areas is suggestive that discovering natural kinds of cognitive capacities is not the goal. (shrink)

Scientists use models to understand the natural world, and it is important not to conflate model and nature. As an illustration, we distinguish three different kinds of populations in studies of ecology and evolution: theoretical, laboratory, and natural populations, exemplified by the work of R.A. Fisher, Thomas Park, and David Lack, respectively. Biologists are rightly concerned with all three types of populations. We examine the interplay between these different kinds of populations, and their pertinent models, in three examples: the notion (...) of “effective” population size, the work of Thomas Park on /Tribolium/ populations, and model-based clustering algorithms such as /Structure/. Finally, we discuss ways to move safely between three distinct population types while avoiding confusing models and reality. (shrink)

Experimental activity is traditionally identified with testing the empirical implications or numerical simulations of models against data. In critical reaction to the ‘tribunal view’ on experiments, this essay will show the constructive contribution of experimental activity to the processes of modeling and simulating. Based on the analysis of a case in fluid mechanics, it will focus specifically on two aspects. The first is the controversial specification of the conditions in which the data are to be obtained. The second is conceptual (...) clarification, with a redefinition of concepts central to the understanding of the phenomenon and the conditions of its occurrence. (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.

Starting with a discussion of what I call Koyré’s paradox of conceptual novelty, I introduce the ideas of Damerow et al. on the establishment of classical mechanics in Galileo’s work. I then argue that although the view of Damerow et al. on the nature of Galileo’s conceptual innovation is convincing, it misses an essential element: Galileo’s use of the experiments described in the first day of the Two New Sciences. I describe these experiments and analyze their function. Central to my (...) analysis is the idea that Galileo’s pendulum experiments serve to secure the reference of his theoretical models in actually occurring cases of free fall. In this way Galileo’s experiments constitute an essential part of the meaning of the new concepts of classical mechanics. (shrink)

Making sense of modeling: beyond representation Content Type Journal Article Category Original paper in Philosophy of Science Pages 335-352 DOI 10.1007/s13194-011-0032-8 Authors Isabelle Peschard, Philosophy Department, San Francisco State University, 1600 Holloway Ave, San Francisco, CA 94132, USA Journal European Journal for Philosophy of Science Online ISSN 1879-4920 Print ISSN 1879-4912 Journal Volume Volume 1 Journal Issue Volume 1, Number 3.

This paper argues that whereas philosophical discussions of first-person methods often turn on the veridicality of first-person reports, more attention should be paid to the experimental circumstances under which the reports are generated, and to the purposes of designing such experiments. After pointing to the ‘constructedness’ of first-person reports in the science of perception, I raise questions about the criteria by which to judge whether the reports illuminate something about the nature of perception. I illustrate this point with a historical (...) debate between Gestalt psychologist and atomists, both of whom used first-person methods to investigate perception. (shrink)

Experimental modeling in biology involves the use of living organisms (not necessarily so-called "model organisms") in order to model or simulate biological processes. I argue here that experimental modeling is a bona fide form of scientific modeling that plays an epistemic role that is distinct from that of ordinary biological experiments. What distinguishes them from ordinary experiments is that they use what I call "in vivo representations" where one kind of causal process is used to stand in for a physically (...) different kind of process. I discuss the advantages of this approach in the context of evolutionary biology. (shrink)

Neuroscience is a laboratory-based science that spans multiple levels of analysis from molecular genetics to behavior. At every level of analysis experiments are designed in order to answer empirical questions about phenomena of interest. Understanding the nature and structure of experimentation in neuroscience is fundamental for assessing the quality of the evidence produced by such experiments and the kinds of claims that are warranted by the data. This article provides a general conceptual framework for thinking about evidence and experimentation in (...) neuroscience with a particular focus on two research areas: cognitive neuroscience and cognitive neurobiology. (shrink)

This paper addresses concerns raised recently by Datteri (Biol Philos 24:301–324, 2009) and Craver (Philos Sci 77(5):840–851, 2010) about the use of brain-extending prosthetics in experimental neuroscience. Since the operation of the implant induces plastic changes in neural circuits, it is reasonable to worry that operational knowledge of the hybrid system will not be an accurate basis for generalisation when modelling the unextended brain. I argue, however, that Datteri’s no-plasticity constraint unwittingly rules out numerous experimental paradigms in behavioural and systems (...) neuroscience which also elicit neural plasticity. Furthermore, I propose that Datteri and Craver’s arguments concerning the limitations of prosthetic modelling in basic neuroscience, as opposed to neuroengineering, rests on too narrow a view of the ways models in neuroscience should be evaluated, and that a more pluralist approach is needed. I distinguish organisational validity of models from mechanistic validity. I argue that while prosthetic models may be deficient in the latter of these explanatory virtues because of neuroplasticity, they excel in the former since organisational validity tracks the extent to which a model captures coding principles that are invariant with plasticity. Changing the brain, I conclude, is one viable route towards explaining the brain. (shrink)

Knowledge-making practices in biology are being strongly affected by the availability of data on an unprecedented scale, the insistence on systemic approaches and growing reliance on bioinformatics and digital infrastructures. What role does theory play within data-intensive science, and what does that tell us about scientific theories in general? To answer these questions, I focus on Open Biomedical Ontologies, digital classification tools that have become crucial to sharing results across research contexts in the biological and biomedical sciences, and argue that (...) they constitute an example of classificatory theory. This form of theorizing emerges from classification practices in conjunction with experimental know-how and expresses the knowledge underpinning the analysis and interpretation of data disseminated online. (shrink)

In accordance with the ideas of I.Hacking and P.Galison, and the “theoretical-operational” structure of experiment of Fock-Lipkin, a symbolic language is developed for the description of structure of a contemporary complex experiment. With its help a particle accelerator-based experiment is analysed as an example of this kind of experiments, where explication and analysis of the following essential features is performed: the roles of instrument, background, data analysis, and their theoretical components. An attempt is made to clarify the concepts of “instrument” (...) and “complexity” of experiment. (shrink)

Par un procédé d'objections/réponses, nous passons d'abord en revue certains des arguments en faveur ou en défaveur du caractère empirique de la simulation informatique. A l'issue de ce chemin clarificateur, nous proposons des arguments en faveur du caractère concret des objets simulés en science, ce qui légitime le fait que l'on parle à leur sujet d'une expérience, plus spécifiquement d'une expérience concrète du second genre.

The thesis of theory-ladenness of observations, in its various guises, is widely considered as either ill-conceived or harmless to the rationality of science. The latter view rests partly on the work of the proponents of New Experimentalism who have argued, among other things, that experimental practices are efficient in guarding against any epistemological threat posed by theory-ladenness. In this paper I show that one can generate a thesis of theory-ladenness for experimental practices from an influential New Experimentalist account. The notion (...) I introduce for this purpose is the concept of ‘theory-driven data reliability judgments’, according to which theories which are sought to be tested with a particular set of data guide reliability judgments about those very same data. I provide various prominent historical examples to show that TDRs are used by scientists to resolve data conflicts. I argue that the rationality of the practices which employ TDRs can be saved if the independent support of the theories driving TDRs is construed in a particular way. (shrink)

This paper discusses Immanuel Kant’s views on the role of experiments in natural science, focusing on their relationship with hypotheses, laws of nature, and the heuristic principles of scientific enquiry. Kant’s views are contrasted with the philosophy of experiment that was first sketched by Francis Bacon and later developed by Robert Boyle and Robert Hooke. Kant holds that experiments are always designed and carried out in the light of hypotheses. Hypotheses are derived from experience on the basis of a set (...) of heuristic principles. The function of experiments is testing hypotheses in order to either reject them as false, or else to transform them into empirical laws of nature. To this end, we must integrate the hypotheses that are confirmed by experiments with the a priori principles which are the foundations of natural science. Compared with Bacon, Boyle, and Hooke, Kant has elaborate views on the one hand, on how our theoretical and pre-theoretical assumptions bear on experimental practice, and on the other hand, on how the results of experimental activity can be integrated with theories to advance our knowledge of nature. However, Kant overstates the dependence of experiments on theories. (shrink)

It is sometimes said that simulation can serve as epistemic substitute for experimentation. Such a claim might be suggested by the fast-spreading use of computer simulation to investigate phenomena not accessible to experimentation (in astrophysics, ecology, economics, climatology, etc.). But what does that mean? The paper starts with a clarification of the terms of the issue and then focuses on two powerful arguments for the view that simulation and experimentation are ‘epistemically on a par’. One is based on the claim (...) that, in experimentation, no less than in simulation, it is not the system under study that is manipulated but a system that ‘stands-in’ for it. The other one highlights the pervasive use of models in experimentation. It will be argued that these arguments, as compelling as they might seem, are each based on a mistaken interpretation of experimentation and that, far from simulation and experimentation being epistemically on a par, they do not have the same epistemic function, do not produce the same kind of epistemic results. (shrink)