According to the Argument for Autonomous Mental Disorder (AAMD), mental disorder can occur in the absence of brain disorder, just as software problems can occur in the absence of hardware problems in a computer. This article argues that the AAMD is unsound. I begin by introducing the “natural dysfunction analysis” of disorder, before outlining the AAMD. I then analyze the necessary conditions for realizer autonomous dysfunction. Building on this, I show that software functions disassociate from hardware functions in a way that mental functions do not disassociate from brain functions. It follows that mental disorders are brain disorders necessarily.

This article addresses two issues: the distinction between objective and subjective measures and the directness of such measures. It is argued that the distinction is unambiguous only when based on a methodological criterion (i.e., the threshold utilized by the measures) rather than a semantic one (i.e., their referring either to the world or to the participant’s inner states). Different senses of directness are discussed: metaphysical (which seems to rest on a category error), methodological (the only unambiguously defined one, though relating “directly” to performance rather than awareness), semantic (which appears gradable), and causal.

This paper introduces the Cummins Functions Approach to neural representations (CFA), which aims to capture the notion of representation that is relevant to contemporary neuroscientific practice. CFA shares the common view that “to be a representation of X” amounts to “having the function of tracking X,” but maintains that the relevant notion of function is defined by Robert Cummins’s account. Thus, CFA offers a notion of neural representation that is dependent on explanatory context. I argue that CFA can account for the normativity of neural representations, and defend its dependence on explanations.

We lay groundwork for applying ethnographic methods in philosophy of science. We frame our analysis in terms of two tasks: (1) to identify the benefits of an ethnographic approach in philosophy of science and (2) to structure an ethnographic approach for philosophical investigation best adapted to provide information relevant to philosophical interests and epistemic values. To this end, we advocate for a purpose-guided form of cognitive ethnography that mediates between the explanatory and normative interests of philosophy of science, while maintaining openness and independence when framing such an investigation to achieve robust unbiased results.

Several philosophers have argued that to capture the generality of certain scientific explanations, we must count mathematical facts among their explanantia. I argue that we can better understand these explanations by adopting a more nuanced stance toward mathematical representations, recognizing the role of mathematical representation schemata in representing highly abstract features of physical systems. It is by picking out these abstract but nonmathematical features that explanations appealing to mathematics achieve a high degree of generality. The result is a rich conception of the role of mathematics in scientific explanations that does not require us to treat mathematical facts as explanantia.

A phenomenon resulting from a computationally irreducible (or computationally incompressible) process is supposedly unpredictable except via simulation. This notion of unpredictability has been deployed to formulate recent accounts of computational emergence. Via a technical analysis, I show that computational irreducibility can establish the impossibility of prediction only with respect to maximum standards of precision. By articulating the graded nature of prediction, I show that unpredictability to maximum standards is not equivalent to being unpredictable in general. I conclude that computational irreducibility fails to fulfill its assigned philosophical roles in theories of computational emergence.

Since Euclid defined a point as “that which has no part” it has been widely assumed that points are necessarily unextended. It has also been assumed that this is equivalent to saying that points or, more properly speaking, degenerate segments, have length zero. We challenge these assumptions by providing models of Euclidean geometry where the points are extended despite the fact that the degenerate segments have null lengths, and observe that whereas the extended natures of the points are not recognizable in the given models, they can be recognized and characterized by structures that are suitable expansions of the models.

Modern science’s ability to produce, store, and analyze big datasets is changing the way that scientific research is practiced. Philosophers have only begun to comprehend the changed nature of scientific reasoning in this age of “big data.” We analyze data-focused practices in biology and climate modeling, identifying distinct species of data-centric science: phenomena-laden in biology and phenomena-agnostic in climate modeling, each better suited for its own domain of application, though each entail trade-offs. We argue that data-centric practices in science are not monolithic because the opportunities and challenges presented by big data vary across scientific domains.

A number of philosophers have thought that fair lotteries over countably infinite sets of outcomes are conceptually incoherent by virtue of violating countable additivity. In this article, I show that a qualitative analogue of this argument generalizes to an argument against the conceptual coherence of a much wider class of fair infinite lotteries—including continuous uniform distributions. I argue that this result suggests that fair lotteries over countably infinite sets of outcomes are no more conceptually problematic than continuous uniform distributions. Along the way, I provide a novel argument for a weak qualitative, epistemic version of regularity.

Formal and social epistemologists have devoted significant attention to the question of how to aggregate the credences of a group of agents who disagree about the probabilities of events. Moss (2011) and Pettigrew (2019) argue that group credences can be a linear mean of the credences of each individual in the group. By contrast, I argue that if the epistemic value of a credence function is determined solely by its accuracy, then we should, where possible, aggregate the underlying statistical models that individuals use to generate their credence functions, using “stacking” techniques from statistics and machine learning first developed by Wolpert (1992).