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The Disunity of Disease
Get PDFIn a recent paper, Harriet Fagerberg argues that the disease debate in the philosophy of medicine makes little sense as conceptual analysis but instead should proceed on the assumption that disease is a real kind. I propose an alternative view. The history and practice of medicine give us reasons to doubt that the category of disease forms a real kind. Instead, drawing on work by Quill R. Kukla, I argue that the disease debate makes good sense on an understanding of disease as an institutional kind. As well as explaining key features of the disease debate, this can facilitate a philosophical understanding of disease that captures the eclectic scope of medicine and the complex reasons why conditions get classified as diseases
Logical Dependence of Physical Determinism on Set-theoretic Metatheory
Get PDFBaroque questions of set-theoretic foundations are widely assumed to be irrelevant to physics. In this article, I challenge this assumption. I argue that even the fundamental physical question of whether a theory is deterministic—whether it fixes a unique future given the present—can depend on one’s choice of set-theoretic axiom candidates over which there is intractable disagreement. Suppose, as is customary (Earman 1986), that a deterministic theory is one whose mathematical formulation yields a unique solution to its governing equations. Then the question of whether a physical theory is deterministic becomes the question of whether there exists a unique solution to its mathematical model—typically a system of differential equations. I argue that competing axiom candidates extending standard mathematics—in particular, the Axiom of Constructibility (V = L) and large cardinal axioms strong enough to prove Projective Determinacy—can diverge on all the core dimensions of physical determinism. First, they may disagree about whether a given physical system is well-posed, and so whether a solution exists. Second, even when they agree that a solution exists, they can differ on whether that solution is unique. Finally, even when they agree that a system has a solution, and agree that this solution is unique, they may still dispute what that solution is. Whether a theory is deterministic—and even which outcome it deterministically predicts—can depend on one’s choice of set-theoretic metatheory. I indicate how the conclusions extend to discrete systems and suggest directions for future research. One upshot of the discussion is that either physical theories must be relativized to set-theoretic metatheories (in which case physics itself becomes relative), or, as Quine (1951) controversially argued, the search for new axioms to settle undecidables may admit of empirical input
Strong Novelty Regained: High-Impact Outcomes of Machine Learning for Science
Get PDFA general class of presupposition arguments holds that the background knowledge and theory required to design, develop, and interpret a machine learning (ML) system imply a strong upper limit to ML’s impact on science. I consider two proposals for how to assess the scientific impact of ML predictions, and I argue that while these accounts prioritize conceptual change, the presuppositions they take to be disqualifying for strong novelty are too restrictive. I characterize a general form of their arguments I call the Concept-free Design Argument: that strong novelty is curtailed by utilizing prior conceptualizations of target phenomena in model design. However, I argue that if ML design choices (such as ground-truth labels for supervised ML and inductive biases) are based on prior conceptualizations of phenomena, it need not impede conceptual change. Furthermore, while their accounts focus narrowly on conceptual change, a variety of learning outcomes also contribute to strong scientific change. Thus, I present a variety of types of strong novelty from philosophy of creativity, epistemology, and philosophy of science that paint a more varied picture of how ML advances science. One of these is a form of local theory-independent learning from data that signals an aim to substantially revise existing theory, but it is not easily undermined by prior assumptions about target phenomena. Furthermore, generating surprise, reducing utility blindness, and eliminating deep ignorance also indicate high impact to scientific knowledge or research direction. I illustrate these types of strong novelty with several cases of scientific discovery with algorithms. My taxonomy clarifies several desiderata for machine-based exploration and should inform choices in designing for scientific change
Between Myth and History: von Neumann on Consciousness in Quantum Mechanics
Get PDFThe von Neumann attitude on such a deep interpretational question as the role of a human observer in order for the quantum description of measurement to be consistent has been long misrepresented. The large majority of the subsequent literature ascribed to von Neumann a radical view, according to which not only the collapse was in itself a truly physical process, but also the only way to accomodate it within a quantum description of a typical measurement was the introduction of human consciousness as a kind of ‘causal’ factor. Inspired by the work of reconstruction pursued by the phenomenological reading of the London-Bauer approach, started by Steven French more than twenty years ago, the account I propose substantiates a significantly more cautious attitude by von Neumann: the time seems then ripe to tell a more balanced story on the relation between the notion of consciousness and the foundations of quantum mechanics in the work of the first scientist – János von Neumann – who explicitly and rigorously addressed the implication of a really universal formulation of quantum physics
Perception, Qualities, and Concepts
Get PDFIt’s widely held that we perceive not only low-level properties, such as colors and shapes, but also high-level properties, such as the property of being a dog or of being a moving train. Debate about which types of property we perceive has recently eclipsed the question of how perceiving itself operates. We focus here on that latter question, proposing an account on which perception of low-level properties occurs by way of mental qualities alone, whereas perception of high-level properties occurs by way of mental qualities together with conceptual content of the type that figures in thinking. It is central to our account that mental qualities have a type of representational character unique to them, so that mental qualities can interact representationally with conceptual content in perceiving. We present a number of advantages of this account, including how it fits with a range of experimental findings, and address several objections to it
Can We Test AI Like We Test Drugs? A Generative Analogy Between Machine Learning and Clinical Translation
Get PDFIn the past few years, machine learning (ML) has been widely (and to an extent, successfully) implemented in medicine. However, uncertainties surrounding ML have made it difficult to establish the basis of its epistemic warrants. In the literature, a parallel has been drawn between medicine and ML, suggesting that we should model epistemic standards for ML on the standards of clinical translation. By developing tools from Hesse’s (1966) work, we characterise the nature of this parallel as a generative analogy between the process of clinical translation and the process of building ML systems. We identify more precisely the epistemic warrants of clinical translation that are typically only mentioned when appealing to the analogy, and we show in which sense such warrants apply analogically to the context of ML. In particular, we interpret the epistemic warrants of clinical translation in reliabilist terms, and we show how this can inform a new form of ML reliabilism, which is compatible to existing reliabilist accounts in philosophy of AI
Interpreting continuism as a mechanistic thesis
Get PDFThe (dis)continuism debate in the philosophy of memory revolves around the question of whether memory and imagination belong to the same natural kind. Continuism, on the one hand, defends that they belong to the same natural kind. Discontinuism, on the other hand, defends that they do not belong to the same natural kind. By adopting a minimal notion of natural kind, one can recognize that there are different legitimate ways of sorting kinds, which lead to different positions in the debate. In this paper, I interpret continuism as a mechanistic thesis, according to which memory and imagination belong to the same natural kind because they are underpinned by the same constitutive mechanism. I clarify the implications of this thesis and show that most of the discontinuist attacks on continuism do not constitute a challenge to the mechanistic thesis. I also present a possible challenge to mechanistic continuism. This suggests that there may be multiple (dis)continuism debates
Parallel Convergences: Cassirer and Vienna Indeterminism
Get PDFStöltzner coined the expression 'Vienna indeterminism' to describe a philosophical tradition centered on the Viennese physicist Exner, serving as the 'historical link' between Mach and Boltzmann, on the one hand, and von Mises and Frank, on the other. During the early 1930s debate on quantum mechanics, there was a 'rapprochement' between Vienna indeterminism and Schlick's work on causality. However, it was Cassirer's 1936 monograph Determinismus und Indeterminismus that shows a full 'convergence' with major tenets of Vienna indeterminism: the fundamentality of statistical laws, the frequency interpretation of probability, and the statistical interpretation of the uncertainty relations. Yet, Cassirer used these conceptual tools to pursue 'in parallel' different philosophical goals. While for the Viennese quantum mechanics represented a fatal blow to the already discredited notion of 'causality,' for Cassirer it challenged the classical notion of 'substantiality,' the ideas of 'particles' as individual substances endowed with properties. The paper concludes that this 'parallel convergence' is the most striking and overlooked aspect of Determinismus und Indeterminismus, serving as the kevstone of its arumentative structure
Mechanisms and Principles: Two Approaches to Scientific Generalization
Get PDFMany philosophers have explored the extensive use of non-universal generalizations in different sciences for inductive and explanatory purposes, analyzing properties such as how widely a generalization holds in space and time. In the present paper, we concentrate on developmental biology to distinguish and characterize two common approaches to scientific generalization—mechanism generalization and principle generalization. The former approach seeks detailed descriptions of causal relationships among specific types of biological entities that produce a characteristic phenomenon across some range of different biological entities; the latter approach abstractly describes relations or interactions that occur during ontogeny and are exemplified in a wide variety of different biological entities. These two approaches to generalization correspond to different investigative aims. Our analysis shows why each approach is sought in a research context, thereby accounting for how practices of inquiry are structured. It also diagnoses problematic assumptions in prior discussions, such as abstraction always being correlated positively with generalizations of wide scope
Mapping Epistemic Priors of Hyperscanning Psychotherapy: The Asymmetry and Reciprocity Blindspots
Get PDFHyperscanning has been increasingly used to quantify the quality of social relationships by tracking the neural correlates of interpersonal interactions. Specifically, this paper critically examines the use of hyperscanning to track the neural correlates of psychotherapeutic change, e.g., the patient-therapist relationship. First, we motivate our project by diagnosing a lack of complex models at the mesoscale in this domain and, consequently, a polarization of the analysis at the micro and macroscales. Looking for the causes of this issue, we highlight the epistemic blindspots of current methodologies that prioritize neural synchrony as a marker of therapeutic success. Drawing on empirical studies and theoretical frameworks, we identify an asymmetry between the neural and behavioral conceptual toolkits, with the latter remaining underdeveloped. We argue that this imbalance stems from two key issues: the underdetermined qualitative interpretation of brain data and the neglect of strong reciprocity in neuroscientific second-person paradigms. Given our critical analysis, we suggest that further research could address the complexity of reciprocal, dynamic interactions in therapeutic contexts. Specifically, drawing on enactivism, we highlight that the autonomy of interactions is one of the factors that undermines the synchrony paradigm. This approach emphasizes the co-construction of meaning and shared experiences through embodied, reciprocal interactions, offering a more integrative understanding of therapeutic change that moves beyond static neural measures to account for the emergent and dynamic nature of social cognition