It is commonly held that research efforts in the cognitive and behavioral sciences are mainly directed toward providing explanations and that phenomena figure into scientific practice qua explananda. I contend that these assumptions convey a skewed picture of the research practices in question and of the role played by phenomena. I argue that experimental research often aims at exploring and describing “objects of research” and that phenomena can figure as components of, and as evidence for, such objects. I situate my (...) analysis within the existing literature and illustrate it with examples from memory research. (shrink)

Explanations in the life sciences frequently involve presenting a model of the mechanism taken to be responsible for a given phenomenon. Such explanations depart in numerous ways from nomological explanations commonly presented in philosophy of science. This paper focuses on three sorts of differences. First, scientists who develop mechanistic explanations are not limited to linguistic representations and logical inference; they frequently employ diagrams to characterize mechanisms and simulations to reason about them. Thus, the epistemic resources for presenting mechanistic explanations are (...) considerably richer than those suggested by a nomological framework. Second, the fact that mechanisms involve organized systems of component parts and operations provides direction to both the discovery and testing of mechanistic explanations. Finally, models of mechanisms are developed for specific exemplars and are not represented in terms of universally quantified statements. Generalization involves investigating both the similarity of new exemplars to those already studied and the variations between them. (shrink)

In the face of causal complexity, scientists reconstitute phenomena in order to arrive at a more simplified and partial picture that ignores most of the 'bigger picture.' This paper will distinguish between two modes of reconstituting phenomena: one moving down to a level of greater decomposition (toward organizational parts of the original phenomenon), and one moving up to a level of greater abstraction (toward different differences regarding the phenomenon). The first aim of the paper is to illustrate that phenomena are (...) moving targets, i.e., they are not fixed once and for all, but are adapted, if necessary, on the basis of the preferred perspective adopted for pragmatic reasons. The second aim is to analyze in detail the second mode of reconstituting phenomena. This includes an exposition of the kind of pragmatic-pluralistic picture resulting from the fact that phenomena are reconstituted by a move up to a level of greater abstraction. (shrink)

An analysis of two heuristic strategies for the development of mechanistic models, illustrated with historical examples from the life sciences. In Discovering Complexity, William Bechtel and Robert Richardson examine two heuristics that guided the development of mechanistic models in the life sciences: decomposition and localization. Drawing on historical cases from disciplines including cell biology, cognitive neuroscience, and genetics, they identify a number of "choice points" that life scientists confront in developing mechanistic explanations and show how different choices result in divergent (...) explanatory models. Describing decomposition as the attempt to differentiate functional and structural components of a system and localization as the assignment of responsibility for specific functions to specific structures, Bechtel and Richardson examine the usefulness of these heuristics as well as their fallibility—the sometimes false assumption underlying them that nature is significantly decomposable and hierarchically organized. When Discovering Complexity was originally published in 1993, few philosophers of science perceived the centrality of seeking mechanisms to explain phenomena in biology, relying instead on the model of nomological explanation advanced by the logical positivists (a model Bechtel and Richardson found to be utterly inapplicable to the examples from the life sciences in their study). Since then, mechanism and mechanistic explanation have become widely discussed. In a substantive new introduction to this MIT Press edition of their book, Bechtel and Richardson examine both philosophical and scientific developments in research on mechanistic models since 1993. (shrink)

This paper aims to identify the key characteristics of model organisms that make them a specific type of model within the contemporary life sciences: in particular, we argue that the term “model organism” does not apply to all organisms used for the purposes of experimental research. We explore the differences between experimental and model organisms in terms of their material and epistemic features, and argue that it is essential to distinguish between their representational scope and representational target. We also examine (...) the characteristics of the communities who use these two types of models, including their research goals, disciplinary affiliations, and preferred practices to show how these have contributed to the conceptualization of a model organism. We conclude that model organisms are a specific subgroup of organisms that have been standardized to fit an integrative and comparative mode of research, and that it must be clearly distinguished from the broader class of experimental organisms. In addition, we argue that model organisms are the key components of a unique and distinctively biological way of doing research using models.Keywords: Experimental organism; Genetics; Model organism; Modeling; Philosophy of biology; Representation. (shrink)

The Morris water maze has been put forward in the philosophy of neuroscience as an example of an experimental arrangement that may be used to delineate the cognitive faculty of spatial memory (e.g., Craver and Darden, Theory and method in the neurosciences, University of Pittsburgh Press, Pittsburgh, 2001; Craver, Explaining the brain: Mechanisms and the mosaic unity of neuroscience, Oxford University Press, Oxford, 2007). However, in the experimental and review literature on the water maze throughout the history of its use, (...) we encounter numerous responses to the question of “what” phenomenon it circumscribes ranging from cognitive functions (e.g., “spatial learning”, “spatial navigation”), to representational changes (e.g., “cognitive map formation”) to terms that appear to refer exclusively to observable changes in behavior (e.g., “water maze performance”). To date philosophical analyses of the water maze have not been directed at sorting out what phenomenon the device delineates nor the sources of the different answers to the question of what. I undertake both of these tasks in this paper. I begin with an analysis of Morris’s first published research study using the water maze and demonstrate that he emerged from it with an experimental learning paradigm that at best circumscribed a discrete set of observable changes in behavior. However, it delineated neither a discrete set of representational changes nor a discrete cognitive function. I cite this in combination with a reductionist-oriented research agenda in cellular and molecular neurobiology dating back to the 1980s as two sources of the lack of consistency across the history of the experimental and review literature as to what is under study in the water maze. (shrink)

Descriptive accounts of the nature of explanation in neuroscience and the global goals of such explanation have recently proliferated in the philosophy of neuroscience and with them new understandings of the experimental practices of neuroscientists have emerged. In this paper, I consider two models of such practices; one that takes them to be reductive; another that takes them to be integrative. I investigate those areas of the neuroscience of learning and memory from which the examples used to substantiate these models (...) are culled, and argue that the multiplicity of experimental protocols used in these research areas presents specific challenges for both models. In my view, these challenges have been overlooked largely because philosophers have hitherto failed to pay sufficient attention to fundamental features of experimental practice. I demonstrate that when we do pay attention to such features, evidence for reduction and integrative unity in neuroscience is simply not borne out. I end by suggesting some new directions for the philosophy of neuroscience that pertain to taking a closer look at the nature of neuroscientific experiments. (shrink)

The concept of mechanism is analyzed in terms of entities and activities, organized such that they are productive of regular changes. Examples show how mechanisms work in neurobiology and molecular biology. Thinking in terms of mechanisms provides a new framework for addressing many traditional philosophical issues: causality, laws, explanation, reduction, and scientific change.

In this paper, I investigate how researchers evaluate their characterizations of scientific phenomena. Characterizing phenomena is an important – albeit often overlooked – aspect of scientific research, as phenomena are targets of explanation and theorization. As a result, there is a lacuna in the literature regarding how researchers determine whether their characterization of a target phenomenon is appropriate for their aims. This issue has become apparent for accounts of scientific explanation that take phenomena to be explananda. In particular, philosophers who (...) endorse mechanistic explanation suggest that the discovery of the mechanisms that explain a phenomenon can lead to its recharacterization. However, they fail to make clear how these explanations provide warrant for recharacterizing their explananda phenomena. Drawing from cases of neurobiological research on potentiation phenomena, I argue that attempting to explain a phenomenon may provide reason to suspend judgment about its characterization, but this cannot provide warrant to recharacterize it if researchers cannot infer a phenomenon’s characteristics from this explanation. To explicate this, I go beyond explanation – mechanistic or otherwise – to analyze why and how researchers change their epistemic commitments in light of new evidence. (shrink)

Galvani's discovery provoked an animated debate that lasted for about a decade. So far, historians have studied only the controversy between Volta and Galvani. I show that a more extensive examination of the response to Galvani's treatise reveals a number of important issues that were characteristic of the contemporary physics and physiology but have not much attracted the attention of historians. In particular, the analysis shows the need to reappraise Galvani's role in establishing animal electricity.

This biography of Emil du Bois-Reymond, the most important forgotten intellectual of the nineteenth century, received an Honorable Mention for History of Science, Medicine, and Technology at the 2013 PROSE Awards, was shortlisted for the 2014 John Pickstone Prize (Britain's most prestigious award for the best scholarly book in the history of science), and was named by the American Association for the Advancement of Science as one of the Best Books of 2014. -/- In his own time (1818–1896) du Bois-Reymond (...) grew famous for his groundbreaking research in neuroscience and his provocative addresses on politics and culture. His discovery of the electrical transmission of nerve signals, his innovations in laboratory instrumentation, and his reductionist methodology all helped lay the foundations of modern neuroscience. -/- In addition to describing the pioneering experiments that earned du Bois-Reymond a seat in the Prussian Academy of Sciences and a professorship at the University of Berlin, this book also recounts du Bois-Reymond’s family origins, private life, public service, and lasting influence. In talks that touched on science, philosophy, history, and literature, du Bois-Reymond introduced Darwin to German students (triggering two days of debate in the Prussian parliament), asked on the eve of the Franco-Prussian War whether France had forfeited its right to exist, and proclaimed the mystery of consciousness, heralding the age of doubt. The first modern biography in any language, "Emil du Bois-Reymond" recovers an important chapter in the history of science, the history of ideas, and the history of Germany. (shrink)

This paper asks (a) how new scientific objects of research are onceptualized at a point in time when little is known about them, and (b) how those conceptualizations, in turn, figure in the process of investigating the phenomena in question. Contrasting my approach with existing notions of concepts and situating it in relation to existing discussions about the epistemology of experimentation, I propose to think of concepts as research tools. I elaborate on the conception of a tool that informs my (...) account. Narrowing my focus to phenomena in cognitive neuropsychology, I then illustrate my thesis with the example of the concept of implicit memory. This account is based on an original reconstruction of the nature and function of operationism in psychology. (shrink)

In this paper, I investigate the nature of empirical findings that provide evidence for the characterization of a scientific phenomenon, and the defeasible nature of this evidence. To do so, I explore an exemplary instance of the rejection of a characterization of a scientific phenomenon: memory transfer. I examine the reason why the characterization of memory transfer was rejected, and analyze how this rejection tied to researchers’ failures to resolve experimental issues relating to replication and confounds. I criticize the presentation (...) of the case by Harry Collins and Trevor Pinch, who claim that no sufficient reason was provided to abandon research on memory transfer. I argue that skeptics about memory transfer adopted what I call a defeater strategy, in which researchers exploit the defeasibility of the evidence for a characterization of a phenomenon. (shrink)

This volume argues for a new image of science that understands both natural and social phenomena to be the product of mechanisms, casting the work of science as an effort to understand those mechanisms. Glennan offers an account of the nature of mechanisms and of the models used to represent them in physical, life, and social sciences.