Some scientific models and some claims about model–target relations are fruitfully diagnosed as dogwhistles. Dogwhistles, broadly speaking, are speech acts that send different, conflicting, and often differentially inflammatory messages to listeners. I distinguish two ways in which scientific models can be dogwhistles: representational dogwhistling and fit-for-purpose dogwhistling. I illustrate both kinds of dogwhistling using an example from computational social science, the diversity trumps ability theorem. I argue that dogwhistling threatens the objectivity of science, and I propose some ameliorative strategies.

Two types of formal models—landscape search tasks and two-armed bandit models—are often used to study the effects that various social factors have on epistemic performance. I argue that they can be understood within a single framework. In this unified framework, I develop a model that may be used to understand the effects of functional and demographic diversity and their interaction. Using the unified model, I find that the benefit of demographic diversity is most pronounced in a functionally homogeneous group, and decreases with increasing functional diversity.

Standard narratives about physical teleology say its death was a fait accompli of the Scientific Revolution, but the principle of least action (PLA) has been taken to instantiate teleology’s survival into Enlightenment physics. Other scholars claim this PLA-based teleological metaphysics fell to general philosophical attacks on final causes. None of these narratives fully captures the philosophical interest of its demise. It illustrates a metaphysics being refuted because it could not be coherently modeled in mathematics, hence directly through mathematization and not by philosophical argument or empirical test.

The developmental properties of organisms play important roles in the generation of variation necessary for evolutionary change. But how can individual development steer the course of evolution? To answer this question, we introduce developmental channeling as a disposition of individual organisms that shapes their possible developmental trajectories and evolutionary dappling as an evolutionary outcome in which the space of possible organismic forms is dappled—it is only partially filled. We then trace out the implications of the channeling-dappling framework for contemporary debates in the philosophy of evolution, including evolvability, reciprocal causation, and the extended evolutionary synthesis.

In noncausal explanations, some noncausal facts (such as mathematical, modal, or metaphysical) are used to explain some physical facts. However, precisely because these explanations abstract away from causal facts, they face two challenges: (1) it is not clear why one rather than the other noncausal explanantia would be relevant for the explanandum; and (2) why would standing in a particular explanatory relation (e.g., “counterfactual dependence,” “constraint,” “entailment,” “constitution,” and “grounding”), and not in some other, be explanatory. I develop an explanatory relevance account that is based on erotetic constraints and show how it addresses these two challenges.

Our thinking often uses rich memories of particular past events. Yet frequently we would do better to use other forms of memory. I show that existing accounts of the function of episodic memory cannot account for such cases, then develop an account which can. Roughly: rich representations of particular past events are required for Unrestricted Learning, learning which is not limited in how much of the world’s complexity it can capture; and episodic memory’s selection for Unrestricted Learning could explain its ubiquitous (and often inappropriate) use for other tasks. This proposal suggests many avenues for further empirical and computational research.

Among advocates and critics of the “extended evolutionary synthesis” (EES), “reciprocal causation” refers to the view that adaptive evolution is a bidirectional phenomenon, whereby organisms and environments impinge on each other through processes of niche construction and natural selection. I argue that reciprocal causation is incompatible with the view that natural selection is a metaphysically emergent causal process. The emergent character of selection places reciprocal causation on the horns of dilemma, and neither horn can rescue it. I conclude that proponents of the EES must abandon the claim that the process of natural selection features in cycles of reciprocal causation.

What is the nature of deception? What does it take for an organism to deceive another one? In this article we address these questions by appealing to the concept of mimicry. More precisely, we argue that a fruitful perspective regards deception as an instance of mimicry rather than the reverse. Conceiving of deception as an instance of mimicry has a number of interesting consequences: It draws connections between different areas of research, vindicates a functional approach to deception by providing a satisfactory answer to some recent objections, and suggests some worries for game-theoretic approaches to deception.

There is a major debate as to whether there are non-causal mathematical explanations of physical facts that show how the facts under question arise from a degree of mathematical necessity considered stronger than that of contingent causal laws. We focus on Marc Lange’s account of distinctively mathematical explanations to argue that purported mathematical explanations are essentially causal explanations in disguise and are no different from ordinary applications of mathematics. This is because these explanations work not by appealing to what the world must be like as a matter of mathematical necessity but by appealing to various contingent causal facts.

Benchmark tests are employed when testing for racial discrimination by police. Neil and Winship (2019) emphasize that such tests are threatened by Simpson’s paradox, but they avoid analyzing the paradox causally. They consequently cannot elucidate the link between statistical quantities and discrimination hypotheses. Simpson’s paradox reveals that the statistics given by benchmark tests are not invariant to conditioning on additional variables. On this basis, I argue that benchmark statistics should not by themselves be taken to provide any evidence regarding discrimination, absent additional assumptions. Causal models can represent these assumptions.

Scientists and philosophers alike debate whether various systems such as plants and bacteria exercise cognition. One strategy for resolving such debates is to ground claims about nonhuman cognition in evidence from mathematical models of cognitive capacities. In this article, I show that proponents of this strategy face two major challenges: demarcating phenomenological models from process models and overcoming underdetermination by model fit. I argue that even if the demarcation problem is resolved, fitting a process model to behavioral data is, on its own, not strong evidence for any cognitive process, let alone processes shared with humans.

This paper proposes a novel view in the the philosophy of race & causation literature known as “causal agnosticism” about race. Causal agnosticism about race implies that it is reasonable to refrain from making judgments about whether race is a cause. The paper’s thesis asserts that certain conditions must be met to infer that something is a cause, according to the fundamental assumptions of causal inference. However, in the case of race, these conditions are often violated. By advocating for causal agnosticism, the paper suggests a more modest approach to understanding the role of race in causal relationships.

We present a new justification for methodological triangulation (MT), the practice of using different methods to support the same scientific claim. Unlike existing accounts, our account captures cases in which the different methods in question are associated with, and rely on, incommensurable theories. Using a nonstandard Bayesian model, we show that even in such cases, a commitment to the minimal form of epistemic conservatism, captured by the rigidity condition that stands at the basis of Jeffrey’s conditionalization, supports the practice of MT.

Psychology is a discipline that has a high number of failed replications, which has been characterized as a “crisis” on the assumption that failed replications are indicative of untrustworthy research. This article uses Chang’s concept of epistemic iteration to show how a research program can advance epistemic goals despite many failed replications. It illustrates this by analyzing an ongoing large-scale replication attempt of Southgate et al.’s work exploring infants’ understanding of false beliefs. It concludes that epistemic iteration offers a way of understanding the value of replications—both failed and successful—that contradicts the narrative centered around distrust.

This article argues against general claims for the epistemic superiority of experiment over observation. It does so by dissociating the benefits traditionally attributed to experiment from physical manipulation. In place of manipulation, we argue that other features of research methods do confer epistemic advantages in comparison to methods in which they are diminished. These features better track the epistemic successes and failures of scientific research, crosscut the observation/experiment distinction, and nevertheless explain why manipulative experiments are successful when they are.

Bayesian agents, argues Belot, are orgulous: they believe in inductive success even when guaranteed to fail on a topologically typical collection of data streams. Here we shed light on how pervasive this phenomenon is. We identify several classes of inductive problems for which Bayesian convergence to the truth is topologically typical. However, we also show that, for all sufficiently complex classes, there are inductive problems for which convergence is topologically atypical. Last, we identify specific topologically typical collections of data streams, observing which guarantees convergence to the truth across all problems from certain natural classes of effective inductive problems.

Many philosophers sympathetic with Humeanism about laws have thought that the fundamental laws will include not only the traditional dynamical equations, but also two additional principles: the Past Hypothesis (PH) and the Statistical Postulate (SP). PH says that the universe began in a particular very-low-entropy macrostate M(0), and SP posits a uniform probability distribution over the microstates compatible with M(0). This view is arguably vindicated by the orthodox Humean Best System Account (BSA). However, I argue that recent developments of the BSA render the Past Hypothesis otiose. In particular, Pragmatic Humeanism does not support the idea that PH is a law.

It is increasingly easy to acquire a large amount of data about a problem before formulating a hypothesis. The idea of exploratory data analysis (EDA) predates this situation, but many researchers find themselves appealing to EDA as an explanation of what they are doing with these new resources. Yet there has been relatively little explicit work on what EDA is or why it might be important. I canvass several positions in the literature, find them wanting, and suggest an alternative: exploratory data analysis, when done well, shows the expected value of experimentation for a particular hypothesis.

This article advances the development of a phylogeny-based psychology in which cognitive ability types are individuated as characters in the evolutionary biological sense. I explain the character concept and its utility in addressing (or dissolving) conceptual problems arising from discoveries of cognitive abilities across a wide range of species. I use the examples of stereopsis in the praying mantis, internal cell-to-cell signaling in plants, and episodic memory in scrub jays to show how anthropocentric cognitive ability types can be reformulated into cognitive characters, thereby promoting the integration of psychology with other sciences of evolved traits.

In light of recent criticisms by Woodward (2017) and Rescorla (2018), we examine the relationship between mechanistic explanation and phenomenological laws. We disambiguate several uses of the phrase “phenomenological law” and show how a mechanistic theory of explanation sorts them into those that are and are not explanatory. We also distinguish the problem of phenomenological laws from arguments about the explanatory power of purely phenomenal models, showing that Woodward and Rescorla conflate these problems. Finally, we argue that the temptation to pit mechanistic and interventionist theories of explanation against one another occludes important and scientifically relevant research questions.