In light of the recent credibility crisis in psychology, this paper argues for a greater emphasis on theorizing in scientific research. Although reliable experimental evidence, preregistration, methodological rigor, and new computational frameworks for modeling are important, scientific progress also relies on properly functioning theories. However, the current understanding of the role of theorizing in psychology is lacking, which may lead to future crises. Theories should not be viewed as mere speculations or simple inductive generalizations. To address this issue, the author (...) introduces a framework called “cognitive metascience,” which studies the processes and results of evaluating scientific practice. This study should proceed both qualitatively, as in traditional science and technology studies and cognitive science, and quantitatively, by analyzing scientific discourse using language technology. By analyzing theories as cognitive artifacts that support cognitive tasks, this paper aims to shed more light on their nature. This perspective reveals that multiple distinct theories serve entirely different roles, and studying these roles, along with their epistemic vices and virtues, can provide insight into how theorizing should proceed. The author urges a change in research culture to appreciate the variety of distinct theories and to systematically advance scientific progress. . (shrink)

Hasok Chang is developing a new form of pragmatic scientific realism that aims to reorient the debate away from truth and towards practice. Central to his project is replacing truth as correspondence with his new notion of ‘operational coherence’, which is introduced as: 1) A success term with probative value to judge and guide epistemic activities. 2) A more useful alternative than truth as correspondence in guiding scientific practice. I argue that, given its current construal as neither necessary nor sufficient (...) for success, operational coherence is too weak and fails to satisfy both 1) and 2). I offer a stronger construal of operational coherence which aims to improve on Chang’s account by tying it to systematic success. This makes operational coherence necessary and sufficient for success. This new account, if successful, rescues 1) but not 2). I then take a step back and try to locate Chang’s pragmatic realism within the broader pragmatist tradition by comparing his views to the founding fathers Peirce, James and Dewey. I also assess to what extent we should consider Chang’s position ‘realist’, arguing that despite the many relativists threads running through it, Chang’s pragmatic realism is deserving of the realist label because its aims to maximize our learning from reality, even if it falls short of what many traditional realist are happy to accept as realism. I finish with comments on the epistemology of science pointing out that there is nothing intrinsic about a practice-based philosophy of science that precludes having both operational coherence and correspondence and highlighting that given a proper understanding these two notions could, in fact, be understood as complementary. I suggest one way this could be done. (shrink)

Taking a cue from remarks Thomas Kuhn makes in 1990 about the historical turn in philosophy of science, I examine the history of history and philosophy of science within parts of the British philosophical context in the 1950s and early 1960s. During this time, ordinary language philosophy's influence was at its peak. I argue that the ordinary language philosophers' methodological recommendation to analyze actual linguistic practice influences several prominent criticisms of the deductive-nomological model of scientific explanation and that these criticisms (...) relate to the historical turn in philosophy of science. To show these connections, I primarily examine the work of Stephen Toulmin, who taught at Oxford from 1949 to 1954, and Michael Scriven, who completed a dissertation on explanation under Gilbert Ryle and R.B. Braithwaite in 1956. I also consider Mary Hesse's appeal to an ordinary language-influenced account of meaning in her account of the role of models and analogies in scientific reasoning, and W.H. Watson's Wittgensteinian philosophy of science, an early influence on Toulmin. I think there are two upshots to my historical sketch. First, it fills out details of the move away from logical positivism to more historical- and practice-focused philosophies of science. Second, questions about linguistic meaning and the proper targets and aims of philosophical analysis are part and parcel of the historical turn, as well as its reception. Looking at the philosophical background during which so-called linguistic philosophers also had a hand in bringing these questions to prominence helps us understand why. (shrink)

The complex societal challenges of the twenty-first Century require scientific researchers and academically educated professionals capable of conducting scientific research in complex problem contexts. Our central claim is that educational approaches inspired by a traditional empiricist epistemology insufficiently foster the required deep conceptual understanding and higher-order thinking skills necessary for epistemic tasks in scientific research. Conversely, we argue that constructivist epistemologies provide better guidance to educational approaches to promote research skills. We also argue that teachers adopting a constructivist learning theory (...) do not necessarily embrace a constructivist epistemology. On the contrary, in educational practice, novel educational approaches that adopt constructivist learning theories often maintain traditional empiricist epistemologies. Philosophers of science can help develop educational designs focused on learning to conduct scientific research, combining constructivist learning theory with constructivist epistemology. We illustrate this by an example from a bachelor’s program in Biomedical Engineering, where we introduce conceptual models and modeling as an alternative to the traditional focus on hypothesis testing in conducting scientific research. This educational approach includes the so-called B&K method for constructing scientific models to scaffold teaching and learning conceptual modeling. (shrink)

Empirical research on democracy depends upon data. The need for such data has led to the development of measures of democracy. Measurement models are evaluated in terms of their reliability and validity, both of which may be thought of as related to the objectivity of the measure. Using the Varieties of Democracy Project as an example, I consider how assessing reliability and validity of measurement models is challenging and argue that democracy might be understood as measured objectively when it is (...) subject to theoretical, empirical, and pragmatic constraints. I call this coherence objectivity. (shrink)

To explain why cognition evolved requires, first and foremost, an analysis of what qualifies as an explanation. In terms of physics, causes are forces and consequences are changes in states of substance. Accordingly, any sequence of events, from photon absorption to focused awareness, chemical reactions to collective behavior, or from neuronal avalanches to niche adaptation, is understood as an evolution from one state to another toward thermodynamic balance where all forces finally tally each other. From this scale-free physics perspective, energy (...) flows through those means and mechanisms, as if naturally selecting them, that bring about balance in the least time. Then, cognitive machinery is also understood to have emerged from the universal drive toward a free energy minimum, equivalent to an entropy maximum. The least-time nature of thermodynamic processes results in the ubiquitous patterns in data, also characteristic of cognitive processes, i.e., skewed distributions that accumulate sigmoidally and, therefore, follow mostly power laws. In this vein, thermodynamics derived from the statistical physics of open systems explains how evolution led to cognition and provides insight, for instance, into cognitive ease, biases, dissonance, development, plasticity, and subjectivity. (shrink)

The development of nineteenth-century geodetic measurement challenges the dominant coherentist account of measurement success. Coherentists argue that measurements of a quantity are epistemically successful if their numerical outcomes converge across varying contextual constraints. Aiming at numerical convergence, in turn, offers an operational aim for scientists to solve problems of coordination. Geodesists faced such a problem of coordination between two indicators of the earth’s ellipticity, which were both based on imperfect ellipsoid models. While not achieving numerical convergence, their measurements produced novel (...) data that grounded valuable theoretical hypotheses. Consequently, they ought to be regarded as epistemically successful. This insight warrants a dynamic revision of coherentism, allowing to judge the success of a metric based on both its coherence and fruitfulness. On that view, scientific measurement aims to coordinate theoretical definitions and produce novel data and theoretical insights. (shrink)

Machine Learning (ML) is on the rise in medicine, promising improved diagnostic, therapeutic and prognostic clinical tools. While these technological innovations are bound to transform health care, they also bring new ethical concerns to the forefront. One particularly elusive challenge regards discriminatory algorithmic judgements based on biases inherent in the training data. A common line of reasoning distinguishes between justified differential treatments that mirror true disparities between socially salient groups, and unjustified biases which do not, leading to misdiagnosis and erroneous (...) treatment. In the curation of training data this strategy runs into severe problems though, since distinguishing between the two can be next to impossible. We thus plead for a pragmatist dealing with algorithmic bias in healthcare environments. By recurring to a recent reformulation of William James’s pragmatist understanding of truth, we recommend that, instead of aiming at a supposedly objective truth, outcome-based therapeutic usefulness should serve as the guiding principle for assessing ML applications in medicine. (shrink)

What is exactly the emergence relation? In which sense is irreducibility associated with it besides being assumed by definition? Although in many cases the explanatory role of emergent states does not exceed the explanatory role of more basic states, this does not speak against the fact that, for some relevant explanatory contexts, emergent states are irreducible. On this basis, an epistemic concept of the emergence relation that does not depend strictly on irreducibility is here offered.

Practitioners of science treat evidence as a separate and objective body of materials that is independent of, and possibly also prior to, all of theorizing. Philosophers of science, by contrast, are increasingly wary of the role of theory in testing and measurement contexts, and hence have problematized the notion of evidence as prior or independent, even in the context of measurement. This paper argues that there is an important sense in which empirical certification of a quantity, via measurement, is indeed (...) prior to theorizing, albeit not necessarily in order of time. The case for this priority distinguishes between the certification of the measurability of a given quantity, as a quantity appropriately measured on a specified scale, and the epistemic warrant due to an assignment of a specific magnitude to that quantity on a given occasion. The result is an account of the certification of a measurable quantity, independent of any theory in which that quantity features. The effect is to render certification of quantities theory-neutral. The aim of the essay is thus to bolster and re-establish a more nuanced empiricist view, via building a case for quantity certification as the epistemic basis (i.e., foundation) of the scientific enterprise. (shrink)

Whether we live in a world of autonomous things, or a world of interconnected processes in constant flux, is an ancient philosophical debate. Modern biology provides decisive reasons for embracing the latter view. How does one understand the practices and outputs of science in such a dynamic, ever-changing world - and particularly in an emergency situation such as the COVID-19 pandemic, where scientific knowledge has been regarded as bedrock for decisive social interventions? We argue that key to answering this question (...) is to consider the role of the activity of reification within the research process. Reification consists in the identification of more or less stable features of the flux, and treating these as constituting stable things. As we illustrate with reference to biological and biomedical research on COVID-19, reification is a necessary component of any process of inquiry and comes in at least two forms: means reification, when researchers create objects meant to capture features of the world, or phenomena, in order to be able to study them; and target reification, when researchers infer an understanding of phenomena from an investigation of the epistemic objects created to study them. We note that both objects and phenomena are dynamic processes and argue that have no reason to assume that changes in objects and phenomena track one another. We conclude that failure to acknowledge these forms of reification and their epistemic role in scientific inquiry can have dire consequences for how the resulting knowledge is interpreted and used. (shrink)