This article sketches the main features of traditional philosophical models of evidence, indicating idealizations in such models that it regards as doing more harm than good. It then proceeds to elaborate on an alternative model of evidence that is functionalist, complex, dynamic, and contextual, a view the author calls dynamic evidential functionalism (DEF). This alternative builds on insights from philosophy of scientific practice, Kuhnian philosophy of science, pragmatist epistemology, philosophy of experimentation, and functionalist philosophy of mind. Along the way, the (...) article raises concerns about the total evidence condition, requirements of certainty or incorrigibility on evidence, and accounts that restrict the type of things that can serve as evidence (to, for example, sense data, facts about particulars). DEF can also help us see the special value of novel predictions and experiments as evidence, as well as help us think about how to critically evaluate the putative evidence to determine whether it is evidence. (shrink)

This paper proposes a set of criteria for an appropriate experiment on the issue of the theory ladenness of perception. These criteria are used to select a number of experiments that use: belief-based ambiguous figures, fragmented figures, or memory color. Crucially, the data in experiments of this type are based on the participant’s qualitative visual experience. Across many different types of experimental designs, different types of stimuli, and different types of belief manipulation, these experiments show the impact of belief/theory on (...) visual perception. Using an ecological validity argument, I conclude that attention-based interpretations of these findings are not successful because the relevant epistemological issues only require that we examine final perception, in the sense of visual awareness of the everyday world. Finally, I argue that the epistemological consequences of theory-ladenness can be reduced by using a top-down/bottom-up theoretical framework and by adopting appropriate methodological procedures to data derived from individual observations. (shrink)

Bogen and Woodward’s distinction between data and phenomena raises the need to understand the structure of the data-to-phenomena and theory-to-phenomena inferences. I suggest that one way to study the structure of these inferences is to analyze the role of the assumptions involved in the inferences: What kind of assumptions are they? How do these assumptions contribute to the practice of identifying phenomena? In this paper, using examples from atmospheric dynamics, I develop an account of the practice of identifying the target (...) in the data-to-phenomena and theory-to-phenomena inferences in which assumptions about spatiotemporal scales play a central role in the identification of parameters that describe the target system. I also argue that these assumptions are not only empirical but they are also idealizing and abstracting. I conclude the paper with a reflection on the role of idealizations in modeling. (shrink)

In this paper I offer an appraisal of James Bogen and James Woodward’s distinction between data and phenomena which pursues two objectives. First, I aim to clarify the notion of a scientific phenomenon. Such a clarification is required because despite its intuitive plausibility it is not exactly clear how Bogen and Woodward’s distinction has to be understood. I reject one common interpretation of the distinction, endorsed for example by James McAllister and Bruce Glymour, which identifies phenomena with patterns in data (...) sets. Furthermore, I point out that other interpretations of Bogen and Woodward’s distinction do not specify the relationship between phenomena and theories in a satisfying manner. In order to avoid this problem I propose a contextual understanding of scientific phenomena according to which phenomena are states of affairs which play specific roles in scientific practice and to which we adopt a special epistemic attitude. Second, I evaluate the epistemological significance of Bogen and Woodward’s distinction with respect to the debate between scientific realists and constructive empiricists. Contrary to what Bogen and Woodward claim, I argue that the distinction does not provide a convincing argument against constructive empiricism. (shrink)

This essay attempts to provide additional motivation for the data/phenomena framework advocated in Bogen and Woodward, “Saving the Phenomena”.

This paper explores how data serve as evidence for phenomena. In contrast to standard philosophical models which invite us to think of evidential relationships as logical relationships, I argue that evidential relationships in the context of data-to-phenomena reasoning are empirical relationships that depend on holding the right sort of pattern of counterfactual dependence between the data and the conclusions investigators reach on the phenomena themselves.

This paper provides a restatement and defense of the data/ phenomena distinction introduced by Jim Bogen and me several decades ago (e.g., Bogen and Woodward, The Philosophical Review, 303–352, 1988). Additional motivation for the distinction is introduced, ideas surrounding the distinction are clarified, and an attempt is made to respond to several criticisms.

In a recent paper James Bogen and James Woodward denounce a set of views on confirmation that they collectively brand 'IRS'. The supporters of these views cast confirmation in terms of Inferential Relations between observational and theoretical Sentences. Against 1RS accounts of confirmation, Bogen and Woodward unveil two main objections: (a) inferential relations are not necessary to model confirmation relations since many data are neither in sentential form nor can they be put in such a form and (b) inferential relations (...) are not sufficient to model confirmation relations because the former cannot capture evidentially relevant factors about the detection processes and instruments that generate the data. In this paper I have a two-fold aim: (i) to show that Bogen and Woodward fail to provide compelling grounds for the rejection of IRS models and (ii) to highlight some of the models' neglected merits. (shrink)

Contrary to the assumptions of empiricist philosophies of science, the theory-laden character of data will not imply the inherent failure (subjectivity, circularity, or rationalization) of instruments to expose nature's secrets. The success of instruments is credited to scientists' capacity to create artificial technological analogs to familiar physical systems. The design of absorption spectrometers illustrates the point: Progress in designing many modern instruments is generated by analogically projecting theoretical insights from known physical systems to unknown terrain. An experimental realism is defended.

Arguments from explanation, i.e. arguments in which the explanatory value of a hypothesis or premise is appealed to, are common in science, and explanatory considerations are becoming more popular in metaphysics. The paper begins by arguing that explanatory arguments in science—even when these are metaphysical explanations— may fail to be explanatory in metaphysics; there is a distinction to be drawn between metaphysical explanation and explanation in metaphysics. This makes it potentially problematic to deploy arguments from explanation in, for instance, metaphysics (...) of science. Part of this problem has its source in that the explanatory concept differs between contexts. The paper discusses a few explanatory concepts and their corresponding arguments from explanation. Towards the end of the paper, I identify two allegedly explanatory arguments in metaphysical discourse by the concluding decisions they give rise to: the rejection of X as a metaphysical fact if X does not explain anything (the argument from explanatory inability) and the rejection of X as a metaphysical fact if X can be non-metaphysically explained (the argument from the non-metaphysically explained). I ask: What kind of concept of explanation do these arguments rely upon, and is that concept suited to the metaphysical task? Two recent examples of these arguments are used as illustration. The preliminary conclusion is that several of the strengths of arguments from explanation in science seem not to be present in metaphysical contexts. (shrink)

Bogen and Woodward claim that the function of scientific theories is to account for 'phenomena', which they describe both as investigator-independent constituents of the world and as corresponding to patterns in data sets. I argue that, if phenomena are considered to correspond to patterns in data, it is inadmissible to regard them as investigator-independent entities. Bogen and Woodward's account of phenomena is thus incoherent. I offer an alternative account, according to which phenomena are investigator-relative entities. All the infinitely many patterns (...) that data sets exhibit have equal intrinsic claim to the status of phenomenon: each investigator may stipulate which patterns correspond to phenomena for him or her. My notion of phenomena accords better both with experimental practice and with the historical development of science. (shrink)

The aim of this article is to contribute to the discussion about the so-called “empirical claim” and “empirical basis” of theory testing. First, the proposals of reconceptualization of the standard notions of partial potential model, intended application and empirical claim of a theory made by Balzer (1982, 1988, 1997a, 1997b, 2006, Balzer, Lauth & Zoubek 1993) and Gähde (1996, 2002, 2008) will be first discussed. Then, the distinction between “global” and “local empirical basis” will be introduced, linking it with that (...) of “global” and “local or particular empirical claim”. After that, following Balzer (1997a), along the lines of Suppes (1962), I will present a way in which what count as ‘data’ for a theory can be modeltheoretically represented and conceptualized. Finally, the proposed analysis will be exemplified with the case of classical genetics (reconstructed in Balzer & Lorenzano 2000, Lorenzano, 1995, 2000, 2002). (shrink)

Cognitive scientists claim to have discovered a large number of cognitive biases, which have a tendency to mislead reasoners. Might cognitive scientists themselves be subject to the very biases they purport to discover? And how should this alter the way they evaluate their research as evidence for the existence of these biases? In this paper, we posit a new paradox (the ‘Self-Reflexive Bias Paradox’), which bears a striking resemblance to some classical logical paradoxes. Suppose that research R appears to be (...) good evidence for the existence of bias B, but if B exists, then R would have been subject to B. Thus, it seems sensible for the researcher to reject R as good evidence for the existence of B. However, rejecting R for this reason admits the existence of B. We examine four putative cognitive biases and criticisms of them, each of which seem to be subject to self-reflexivity. In two cases, we argue, paradox is avoidable. In the remaining two, we cannot find a way to avoid the paradox, which poses a practical obstacle to scientific inquiry and results in an intriguing theoretical quandary. (shrink)

There exists a problem of the circularity in measurement: construction of theories requires reliable data, but obtaining reliable data requires reliable measurement devices whose construction requires a theory. I argue that adapting Anil Gupta's empiricist epistemology to a scientific context yields a possible solution. One can consider the role of data not as providing a foundation for a theory, but as acting functionally, licensing revisions of a previous theory. Data provide scientists with entitlement to their claims conditional on their background (...) theory. Unconditional entitlement is obtained when different starting theories converge to the same view over the course of experimentation. I explain this idea using two examples, one in thermometry and one involving experiments on the weak neutral current. (shrink)

There exists a problem of the circularity in measurement: construction of theories requires reliable data, but obtaining reliable data requires reliable measurement devices whose construction requires a theory. I argue that adapting Anil Gupta's empiricist epistemology to a scientific context yields a possible solution. One can consider the role of data not as providing a foundation for a theory, but as acting functionally, licensing revisions of a previous theory. Data provide scientists with entitlement to their claims conditional on their background (...) theory. Unconditional entitlement is obtained when different starting theories converge to the same view over the course of experimentation. I explain this idea using two examples, one in thermometry and one involving experiments on the weak neutral current. (shrink)

Provided we agree about the thing, it is needless to dispute about the terms. —David Hume, A treatise of human nature, Book 1, section VIIMap-like representations are frequently invoked as an alternative type of representational vehicle to a language of thought. This view presupposes that map-systems and languages form legitimate natural kinds of cognitive representational systems. I argue that they do not, because the collections of features that might be taken as characteristic of maps or languages do not themselves provide (...) scientifically useful information above and beyond what the individual features provide. To bring this out, I sketch several allegedly distinctive features of maps, and show how they can easily be grafted onto a simple logical language, resulting in a hybrid “manguage.” The ease with which these linguistic and map-like properties can be integrated into a single representational system raises the question of what work broader categories like language and map are doing. I maintain.. (shrink)

Experimental data are often acclaimed on the grounds that they can be consistently generated. They are, it is said, reproducible. In this paper I describe how this feature of experimental-data (their pragmatic reliability) leads to their epistemic worth (their epistemic reliability). An important part of my description is the supposition that experimental procedures are to certain extent fixed and stable. Various illustrations from the actual practice of science are introduced, the most important coming at the end of the paper with (...) a discussion of Ray Davis' 1967 solar-neutrino detection experiment (as it is portrayed in Pinch, 1980). (shrink)

This article examines how scientists move from physical measurementsto actual observation of single-cell recordings in the brain. We highlight how easy it is to change the fundamental nature of ourobservations using accepted methodological techniques for manipulatingraw data. Collecting single-cell data is thoroughly pragmatic. Weconclude that there is no deep or interesting difference betweenaccounting for observations by measurements and accounting forobservations by theories.

According to conventional wisdom, local gauge symmetry is not a symmetry of nature, but an artifact of how our theories represent nature. But a study of the so-called theta-vacuum appears to refute this view. The ground state of a quantized non-Abelian Yang-Mills gauge theory is characterized by a real-valued, dimensionless parameter theta—a fundamental new constant of nature. The structure of this vacuum state is often said to arise from a degeneracy of the vacuum of the corresponding classical theory, which degeneracy (...) allegedly arises from the fact that “large” (but not “small”) local gauge transformations connect physically distinct states of zero field energy. If that is right, then some local gauge transformations do generate empirical symmetries. In defending conventional wisdom against this challenge I hope to clarify the meaning of empirical symmetry while deepening our understanding of gauge transformations. I distinguish empirical from theoretical symmetries. Using Galileo’s ship and Faraday’s cube as illustrations, I say when an empirical symmetry is implied by a theoretical symmetry. I explain how the theta-vacuum arises, and how “large” gauge transformations differ from “small” ones. I then present two analogies from elementary quantum mechanics. By applying my analysis of the relation between empirical and theoretical symmetries, I show which analogy faithfully portrays the character of the vacuum state of a classical non-Abelian Yang-Mills gauge theory. The upshot is that “large” as well as “small” gauge transformations are purely formal symmetries of non-Abelian Yang-Mills gauge theories, whether classical or quantized. It is still worth distinguishing between these kinds of symmetries. An analysis of gauge within the constrained-Hamiltonian formalism yields the result that “large” gauge transformations should not be classified as gauge transformations; indeed, nor should “global” gauge transformations. In a theory in which boundary conditions are modeled dynamically, “global” gauge transformations may be associated with physical symmetries, corresponding to translations of these extra dynamical variables. Such translations are symmetries if and only if charge is conserved. But it is hard to argue that these symmetries are empirical, and in any case they do not correspond to any constant phase change in a quantum state. (shrink)

A stalwart view in the philosophy of science holds that, even when broadly construed so as to include theoretical auxiliaries, theories cannot make direct contact with observations. This view owes much to Bogen and Woodward’s influential distinction between data and phenomena. According to them, data are typically the kind of things that are observable or measurable like "bubble chamber photographs, patterns of discharge in electronic particle detectors and records of reaction times and error rates in various psychological experiments". Phenomena are (...) physical processes that are typically unobservable. Examples of the latter category include "weak neutral currents, the decay of the proton, and chunking and recency effects in human memory". Theories, in Bogen and Woodward’s view, are utilised to systematically explain, infer and predict phenomena, not data. The relationship between theories and data is rather indirect. Data count as evidence for phenomena and the latter in turn count as evidence for theories. This view is becoming increasingly influential, Stathis Psillos and Mauricio Suárez ). In this paper I argue contrary to this view that in various significant and well-known cases theories do make direct contact with the help of suitable auxiliaries. (shrink)