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.

Adaptationism is often taken to be the thesis that most traits are adaptations. To assess this thesis, it seems we must be able to establish either an exhaustive set of all traits or a representative sample of this set. Either task requires a more systematic and principled way of individuating traits than is currently available. Moreover, different trait individuation criteria can make adaptationism turn out true or false. For instance, individuation based on natural selection may render adaptationism true, but may do so by presupposing adaptationism. In this article, we show how adaptationism depends on trait individuation and that the latter is an open and unsolved problem.

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.

Philosophy of science is expanding via the introduction of new digital data and tools for their analysis. The data comprise digitized published books and journal articles, as well as heretofore unpublished material such as images, archival text, notebooks, meeting notes, and programs. The growth in available data is matched by the extensive development of automated analysis tools. The variety of data sources and tools can be overwhelming. In this article, we survey the state of digital work in the philosophy of science, showing what kinds of questions can be answered and how one can go about answering them.

Fitness is a central theoretical concept in evolutionary theory. Despite its importance, much debate has occurred over how to conceptualize and formalize fitness. One point of debate concerns the roles of organismic and trait fitness. In a recent addition to this debate, Elliott Sober argues that trait fitness is the central fitness concept, and that organismic fitness is of little value. In this paper, by contrast, we argue that it is organismic fitness that lies at the bases of both the conceptual role of fitness and its role as a measure of evolutionary dynamics.

In this essay I examine a well-known articulation of human nature skepticism, a paper by Hull. I then review a recent reply to Hull by Machery, which argues for an account of human nature that he claims is both useful and scientifically robust. I challenge Machery’s account and introduce an alternative account—the “life-history trait cluster” conception of human nature—that I hold is scientifically sound and makes sense of (at least some of) our intuitions about—and desiderata for—human nature.

Homology is a biological sameness relation that is purported to hold in the face of changes in form, composition, and function. In spite of the centrality and importance of homology, there is no consensus on how we should understand this concept. The two leading views of homology, the genealogical and developmental accounts, have significant shortcomings. We propose a new account, the hierarchical-dependency account of homology, which avoids these shortcomings. Furthermore, our account provides for continuity between special, general, and serial homology.

The commentaries have both drawn out the implications of, and challenged, our definition and operationalization of innovation. In this response, we reply to these concerns, discuss the differences between our operationalization and the preexisting operationalization if innovation, and make suggestions for the advancement of the challenging and exciting field of animal innovation.

Innovation is a key component of most definitions of culture and intelligence. Additionally, innovations may affect a species' ecology and evolution. Nonetheless, conceptual and empirical work on innovation has only recently begun. In particular, largely because the existing operational definition (first occurrence in a population) requires long-term studies of populations, there has been no systematic study of innovation in wild animals. To facilitate such study, we have produced a new definition of innovation: Innovation is the process that generates in an individual a novel learned behavior that is not simply a consequence of social learning or environmental induction. Using this definition, we propose a new operational approach for distinguishing innovations in the field. The operational criteria employ information from the following sources: (1) the behavior's geographic and local prevalence and individual frequency; (2) properties of the behavior, such as the social role of the behavior, the context in which the behavior is exhibited, and its similarity to other behaviors; (3) changes in the occurrence of the behavior over time; and (4) knowledge of spontaneous or experimentally induced behavior in captivity. These criteria do not require long-term studies at a single site, but information from multiple populations of a species will generally be needed. These criteria are systematized into a dichotomous key that can be used to assess whether a behavior observed in the field is likely to be an innovation.