Practice-centric and theory-centric approaches in philosophy of science are described and contrasted. The contrast is developed through an examination of their different treatments of the underdetermination problem. The practice-centric approach is illustrated by a summary of comparative research on approaches in the biology of behavior. The practice-centric approach is defended against charges that it encourages skepticism regarding the sciences.

This article offers a critique of an account of explanatory integration that claims that explanations of cognitive capacities by functional analyses and mechanistic explanations can be seamlessly integrated. It is shown that achieving such explanatory integration requires that the terms designating cognitive capacities in the two forms of explanation are stable but that experimental practice in the mind-brain sciences currently is not directed at achieving such stability. A positive proposal for changing experimental practice so as to promote such stability is put forward, and its implications for explanatory integration are briefly considered.

Neuroscience constrains psychology, but stating these constraints with precision is not simple. Here I consider whether mechanistic analysis provides a useful way to integrate models of cognitive and neural structure. Recent evidence suggests that cognitive systems map onto overlapping, distributed networks of brain regions. These highly entangled networks often depart from stereotypical mechanistic behaviors. While this casts doubt on the prospects for classical mechanistic integration of psychology and neuroscience, I argue that it does not impugn a realistic interpretation of either type of model. Cognitive and neural models may depict different, but equally real, causal structures within the mind/brain.

We provide an explicit taxonomy of legitimate kinds of abstraction within constitutive explanation. We argue that abstraction is an inherent aspect of adequate mechanistic explanation. Mechanistic explanations—even ideally complete ones—typically involve many kinds of abstraction and therefore do not require maximal detail. Some kinds of abstraction play the ontic role of identifying the specific complex components, subsets of causal powers, and organizational relations that produce a suitably general phenomenon. Therefore, abstract constitutive explanations are both legitimate and mechanistic.

Network analysis is increasingly used to discover and represent the organization of complex systems. Focusing on examples from neuroscience in particular, I argue that whether network models explain, how they explain, and how much they explain cannot be answered for network models generally but must be answered by specifying an explanandum, by addressing how the model is applied to the system, and by specifying which kinds of relations count as explanatory.

Network neuroscience provides a systems approach to the study of the brain and enables the examination of interactions measured at different temporal and spatial scales. We review current methods to quantify the structure of brain networks and compare that structure across different clinical cohorts, cognitive states, and subjects. We further introduce the emerging mathematical concept of multilayer networks and describe the advantages of this approach to model changing brain dynamics over time. We conclude by offering several concrete examples of how multilayer network approaches to neuroimaging data provide novel insights into brain structure and evolving function.

Scientific explanations must bear the proper relationship to the world: they must depict what, out in the world, is responsible for the explanandum. But explanations must also bear the proper relationship to their audience: they must be able to create human understanding. With few exceptions, philosophical accounts of explanation either ignore entirely the relationship between explanations and their audience or else demote this consideration to an ancillary role. In contrast, I argue that considering an explanation’s communicative role is crucial to any satisfactory account of explanation.

The goal of this paper is to develop a counterfactual theory of explanation (CTE). The CTE provides a monist framework for causal and noncausal explanations, according to which both causal and noncausal explanations are explanatory by virtue of revealing counterfactual dependencies between the explanandum and the explanans. I argue that the CTE is applicable to two paradigmatic examples of noncausal explanations: Euler’s explanation and renormalization group explanations of universality.

In this article I examine the perceptual metaphor at the heart of perspectivism, discussing three elements: partiality, interestedness, and interaction. I argue that perspectivists should drop the visual metaphor in favor of a haptic one. Because the sense of touch requires contact and purposeful exploration on the part of the perceiver, it is obvious that with touch one apprehends an extradermal reality in virtue of and not in spite of its interactive and interested nature. By analogy, perspectivists should investigate the thesis that scientific representations inform us about the natural world in virtue of their interactive and interested qualities.

Success-to-truth inferences have been the realist stronghold for a long time. Scientific success is the parameter by which realists claim to discern approximately true theories from false ones. But scientific success needs to be probed a bit deeper. In this article, I tell three tales of scientific success, by considering in turn success from nowhere, success from here now, and success from within. I argue for a suitable version of success from within that can do justice to the historically situated nature of our scientific knowledge. The outcome is a new way of thinking about success-to-truth inferences along perspectivalist lines.

There is danger in stressing commonalities among methods because the differences matter in fixing the meaning of our claims. Different methods can, and often do, test the same claim. But it takes a strong network of theory and empirical results to ensure that. Failing that, we are likely to fall into inference by pun. We use one set of methods to establish a claim and then draw inferences licensed by a similar-sounding claim that calls for different methods of testing. Our inferences fail, and bridges we build (or policies we set) depending on them fall down.

For centuries scientists have claimed that women are intellectually inferior to men and blacks are inferior to whites. Although these claims have been contested and corrected for centuries, they still continue to be made. Meanwhile, scientists have documented the harm done to women and blacks by the publication of such claims. Can anything be done to improve this situation? Freedom of research is universally recognized to be of first-rate importance. Yet, constraints on that freedom are also universally recognized. I consider three of these constraints and argue for tighter restrictions on race- and gender-related cognitive differences research on their basis.

In this article I show that population geneticists are acknowledging a kind of biological population that has hitherto been unappreciated by philosophers. The new population talk occurs when population geneticists call continent-level human genetic clusters ‘populations’ in population structure research. My theory is that the kind of population being referred to is the K population, which is, roughly, a biological population whose members are united by common genomic ancestry and in which population membership is graded. After presenting and defending the theory, I show that the K population is indeed a kind of biological population. Finally, I address likely objections.

Biological reproduction is a material process of intertwined, recursive propagule generation and development, assuming that development produces simple life cycles. Most organisms, however, have more or less complex life cycles. Here, I attempt to reconcile recent articulations of a reproducer account with traditional approaches to complex life cycles by generalizing genetic demarcation criteria for life cycle generations in terms of the “scaffolded” development of hybrid reproducers. I argue that scaffolding provides a general method for identifying developmental bottlenecks and suggests in turn a new way of understanding developmental reaction norms.

Problems raised by complex life cycles for standard summaries of evolutionary processes, and for concepts of individuality in biology, are described. I then outline a framework that can be used to compare life cycles. This framework treats reproduction as a combination of production and recurrence and organizes life cycles according to the distribution of steps in which multiplication, bottlenecks, and sex occur. I also discuss fitness and its measurement in complex life cycles and consider some phenomena that raise complications and difficulties for my framework.

Complex life cycles are common in the eukaryotic world, and they complicate the question of how to define individuality. Using a bottom-up, gene-centric approach, I consider the concept of fitness in the context of complex life cycles. I analyze the fitness effects of an allele (or a trait) on different biological units within a complex life history and how these effects drive evolutionary change within populations. Based on these effects, I attempt to construct a concept of fitness that accurately predicts evolutionary change in the context of complex life cycles.

Reproduction is central to biology and evolution. Standard concepts of reproduction are drawn from animals. Nonstandard examples of reproduction can be found in unicellular eukaryotes that distribute their reproductive strategies across multiple generations, and in mutualistic systems that combine different modes of reproduction across multiple lineages. Examining multigenerational and multilineal reproducers and how they align fitness has implications for conceptualizing units of evolution.

Hamilton introduced two conceptions of social fitness, which he called neighbor-modulated fitness and inclusive fitness. Although he regarded them as formally equivalent, a reanalysis of his own argument for their equivalence brings out two important assumptions on which it rests: weak additivity and actor’s control. When weak additivity breaks down, neither fitness concept is appropriate in its original form. When actor’s control breaks down, neighbor-modulated fitness may be appropriate, but inclusive fitness is not. Yet I argue that, despite its more limited domain of application, inclusive fitness provides a distinctively valuable perspective on social evolution.