Previous simulation models have found positive effects of cognitive diversity on group performance, but have not explored effects of diversity in demographics (e.g., gender, ethnicity). In this paper, we present an agent-based model that captures two empirically supported hypotheses about how demographic diversity can improve group performance. The results of our simulations suggest that, even when social identities are not associated with distinctive task-related cognitive resources, demographic diversity can, in certain circumstances, benefit collective performance by counteracting two types of conformity that can arise in homogeneous groups: those relating to group-based trust and those connected to normative expectations toward in-groups.

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.

The operations of deep networks are widely acknowledged to be inscrutable. The growing field of Explainable AI (XAI) has emerged in direct response to this problem. However, owing to the nature of the opacity in question, XAI has been forced to prioritise interpretability at the expense of completeness, and even realism, so that its explanations are frequently interpretable without being underpinned by more comprehensive explanations faithful to the way a network computes its predictions. While this has been taken to be a shortcoming of the field of XAI, I argue that it is broadly the right approach to the problem.

The use of ethnography to understand archaeology is both prevalent and controversial. This paper develops an alternative approach, using ethnography to build and test a general theory of forager behaviors, and their variations in different conditions, one which can then be applied even to prehistoric sites differing from contemporary experience. Human behavioral ecology is chosen as the framework theory, and forager social learning as a case study. The argument is then applied to social learning in the late Pleistocene, and hence to a famous archaeological puzzle: the late Pleistocene acceleration of technical innovation and regional differentiation.

Guilt poses a unique evolutionary problem. Unlike other dysphoric emotions, it is not immediately clear what its adaptive significance is. One can imagine thriving despite or even because of a lack of guilt. In this article, we review solutions offered by Scott James, Richard Joyce, and Robert Frank and show that although their solutions have merit, none adequately solves the puzzle. We offer an alternative solution, one that emphasizes the role of empathy and posttransgression behavior in the evolution of guilt. Our solution, we contend, offers a better account of why guilt evolved to play its distinctive social role.

Many epidemics consist in individuals spreading infection to others. From the population perspective, they also have population characteristics important in modeling, explaining, and intervening in epidemics. I analyze epidemiology’s contemporary population perspective through the example of epidemics by examining two central principles attributed to Geoffrey Rose: a distinction between the causes of cases and the causes of incidence, and between “high-risk” and “population” strategies of prevention. Both principles require revision or clarification to capture the sense in which they describe distinct perspectives on the same phenomenon (such as an epidemic), each perspective capturing a different level of contrastive analysis.

First, I show that the semantic thesis of scientific realism may be relaxed significantly—to allow that some scientific discourse is not truth-valued—without making any concessions concerning the epistemic or methodological theses that lie at realism’s core. Second, I illustrate how relaxing the semantic thesis allows realists to avoid positing abstract entities and to fend off objections to the “no miracles” argument from positions such as cognitive instrumentalism. Third, I argue that the semantic thesis of scientific realism should be relaxed because it is possible for scientific statements to be partially true, and hence approximately true, without being false.

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 argues that a notion of statistical explanation, based on Salmon’s statistical relevance model, can help us better understand deep neural networks. It is proved that homogeneous partitions, the core notion of Salmon’s model, are equivalent to minimal sufficient statistics, an important notion from statistical inference. This establishes a link to deep neural networks via the so-called Information Bottleneck method, an information-theoretic framework, according to which deep neural networks implicitly solve an optimization problem that generalizes minimal sufficient statistics. The resulting notion of statistical explanation is general, mathematical, and subcausal.

Life on Earth descends from a common ancestor. However, it is likely that there are other instances of life in the universe. If so, each abiogenesis event will have given rise to an independently originated life clade (IOLC), of which Earth-life is an example. In this paper, I argue that the set of all IOLCs in the universe forms a Darwinian population subject to natural selection, with more widely dispersed IOLCs being less likely to face extinction. As a result, we should expect that, over time, more planets will become inhabited by fewer, more successful IOLCs.

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 model scientific theories as Bayesian networks. Nodes carry credences and function as abstract representations of propositions within the structure. Directed links carry conditional probabilities and represent connections between those propositions. Updating is Bayesian across the network as a whole. The impact of evidence at one point within a scientific theory can have a very different impact on the network than does evidence of the same strength at a different point. A Bayesian model allows us to envisage and analyze the differential impact of evidence and credence change at different points within a single network and across different theoretical structures.

Several philosophers propose probabilistic measures of how well a potential scientific explanation would explain the given evidence. These measures could elaborate “best” in “inference to the best explanation”. This paper argues that none of these measures (and no other measure built exclusively from such probabilities) succeeds. The paper considers the various rival explanations that scientists proposed for the parallelogram of forces. Scientists regarded various features of these proposals as making them more or less “lovely” (in Lipton’s sense). None of these probabilistic measures of loveliness can reflect these features. The paper concludes by considering the kinds of probabilities that could reflect these features.

This paper explores a distinction among causal relationships that has yet to receive attention in the philosophical literature, namely, whether causal relationships are reversible or irreversible. We provide an analysis of this distinction and show how it has important implications for causal inference and modeling. This work also clarifies how various familiar puzzles involving preemption and over-determination play out differently depending on whether the causation involved is reversible.

Substrate independence and mind-body functionalism claim that thinking does not depend on any particular kind of physical implementation. But real-world information processing depends on energy, and energy depends on material substrates. Biological evidence for these claims comes from ecology and neuroscience, while computational evidence comes from neuromorphic computing and deep learning. Attention to energy requirements undermines the use of substrate independence to support claims about the feasibility of artificial intelligence, the moral standing of robots, the possibility that we may be living in a computer simulation, the plausibility of transferring minds into computers, and the autonomy of psychology from neuroscience.

Some New Mechanists have proposed that claims of compositional relations are justified by combining the results of top-down and bottom-up interlevel interventions. But what do scientists do when they can perform, say, a cellular intervention, but not a subcellular detection? In such cases, paired interlevel interventions are unavailable. We propose that scientists use abduction and we illustrate its use through a case study of the ionic theory of resting and action potentials.

Standard defenses of ontological individualism are challenged by arguments that exploit the dependence of social facts on material facts—that is, facts that are not about human individuals. In this article, I discuss Brian Epstein’s “materialism” in The Ant Trap: granting Epstein’s strict definition of individualism, I show that his arguments depend crucially on a generous conception of social properties and social facts. Individualists, however, are only committed to the claim that projectible properties are individualistically realized, and materialists have not undermined this claim.

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.

The task of Philosophy in Science (PinS) is to use philosophical tools to help solve scientific problems. This article describes how I stumbled into this line of work and then addressed several topics in philosophy of biology—units of selection, cladistic parsimony, robustness and trade-offs in model building, adaptationism, and evidence for common ancestry—often in collaboration with scientists. I conclude by offering advice for would-be PinS practitioners.