Lucas J. Matthews

Although the concept of modularity is pervasive across fields and disciplines, philosophers and scientists use the term in a variety of different ways. This paper identifies two distinct ways of thinking about modularity, and considers what makes them similar and different. For philosophers of mind and cognitive science, cognitive modularity helps explain the capacities of brains to process sundry and distinct kinds of informational input. For philosophy of biology and evolutionary science, biological modularity helps explain the capacity of random evolutionary processes to give rise to highly complex and sophisticated biological systems. Although these different ways of thinking about modularity are largely distinct, this paper proposes a unifying feature common to both: isolability, or the capacity of subsystems to undergo changes without resulting in substantial changes to neighboring or interconnected subsystems.

In August of 2018, the results of the largest genomic investigation in human history were published. Scanning the DNA of over one million participants, a genome‐wide association study was conducted to identify genetic variants associated with the number of years of education a person has completed. This measure, called “educational attainment,” is often treated as a proxy for intelligence and cognitive ability. The study raises a host of hard philosophical questions about study design and strength of evidence. It also sets the basis for something far more controversial. Using a new genomic method that generates “polygenic scores,” researchers are now able to use the results of the study to predict a person's educational potential from a blood or saliva sample. Going a step further, some researchers have begun to promote “precision education,” which would tailor students’ school plans to their genetic profiles. The idea of precision education provokes concerns about stigma and self‐fulfilling prophecies.

Genetic explanation of complex human behavior presents an excellent test case for pluralism. Although philosophers agree that successful scientific investigation of behavior is pluralistic, there remains disagreement regarding integration and elimination—is the plurality of approaches here to stay, or merely a waystation on the road to monism? In this paper we introduce an issue taken very seriously by scientists yet mostly ignored by philosophers—the missing heritability problem—and assess its implications for disagreement among pluralists. We argue that the missing heritability problem, which isn’t going anywhere any time soon, implies that pluralism in behavior genetics is both practically ineliminative and theoretically non-integrative.

The modern synthesis in evolutionary biology is taken to be that period in which a consensus developed among biologists about the major causes of evolution, a consensus that informed research in evolutionary biology for at least a half century. As such, it is a particularly fruitful period to consider when reflecting on the meaning and role of chance in evolutionary explanation. Biologists of this period make reference to “chance” and loose cognates of “chance,” such as: “random,” “contingent,” “accidental,” “haphazard,” or “stochastic.” Of course, what an author might mean by “chance” in any specific context varies. In the following, we first offer a historiographical note on the synthesis. Second, we introduce five ways in which synthesis authors spoke about chance. We do not take these to be an exhaustive taxonomy of all possible ways in which chance meaningfully figures in explanations in evolutionary biology. These are simply five common uses of the term by biologists at this period. They will serve to organize our summary of the collected references to chance and the analysis and discussion of the following questions: • What did synthesis authors understand by chance? • How did these authors see chance operating in evolution? • Did their appeals to chance increase or decrease over time during the synthesis? That is, was there a “hardening” of the synthesis, as Gould claimed (1983)?

A strong case has been made for the role and value of mechanistic reasoning in process-oriented sciences, such as molecular biology and neuroscience. This paper shifts focus to assess the role of mechanistic reasoning in an area where it is neither obvious nor expected: population genetics. Population geneticists abstract away from the causal-mechanical details of individual organisms and, instead, use mathematics to describe population-level, statistical phenomena. This paper, first, develops a framework for the identification of mechanistic reasoning where it is not obvious: mathematical and mechanistic styles of scientific reasoning. Second, it applies this framework to demonstrate that both styles are integrated in modern investigations of evolutionary biology. Characteristic of the former, applied population genetic techniques provide statistical evidence for associations between genotype, phenotype, and fitness. Characteristic of the latter, experimental interventions provide causal-mechanical evidence for associations between the very same relationships, often in the same model organisms. The upshot is a richer perspective of how evolutionary biologists build evidence for hypotheses regarding adaptive evolution and a general framework for assessing the scope of mechanistic reasoning across the sciences.

In addition to theorizing about the role and value of mechanisms in scientific explanation or the causal structure of the world, there is a fundamental task of getting straight what a ‘mechanism’ is in the first place. Broadly, this paper is about the challenge of application: the challenge of aligning one's philosophical account of a scientific concept with the manner in which that concept is actually used in scientific practice. This paper considers a case study of the challenge of application as it pertains to the concept of a mechanism: the debate about whether natural selection is a mechanism. By making clear what is and is not at stake in this debate, this paper considers various strategies for dealing with the challenge of application and makes a case for definitional pluralism about mechanism concepts.

A strong case has been made for the role and value of mechanistic explanation in neuroscience and molecular biology. A similar demonstration in other domains of scientific investigation, however, remains an important challenge of scope for the new mechanists. This article helps answer that challenge by demonstrating one valuable role mechanisms play in phylogenetics. Using the transition/transversion rate parameter as a case example, this article argues that models embedded with mechanisms produce stronger phylogenetic tree hypotheses, as measured by maximum likelihood logL values. Two important implications for the new mechanistic account of explanation are considered