Zina B. Ward

Most psychological research aims to uncover generalizations about the mind that hold across subjects. Philosophical discussions of scientific explanation have focused on such generalizations, but in doing so, have often overlooked an important phenomenon: variation. Variation is ubiquitous in psychology and many other domains, and an important target of explanation in its own right. Here I characterize explananda that concern individual differences and formulate an account of what it takes to explain them. I argue that the notion of actual difference making, the only causal concept in the literature that explicitly addresses variation, cannot be used to ground such an account. Instead, I propose a view on which explaining individual differences involves identifying causes that could be intervened on to reduce the variability in the population. This account provides criteria of success for explaining variation and deepens our understanding of causal explanation.

A dominant strand of philosophical thought holds that natural kinds are clusters of objects with shared properties. Cluster theories of natural kinds are often taken to be a late twentieth-century development, prompted by dissatisfaction with essentialism in philosophy of biology. I will argue here, however, that a cluster theory of kinds had actually been formulated by William Whewell (1794-1866) more than a century earlier. Cluster theories of kinds can be characterized in terms of three central commitments, all of which are present in Whewell’s work on classification. Like contemporary cluster theorists, Whewell claims that kinds are united by similarity, that many kinds do not have essences, and that there are “gaps” between kinds. Moreover, Whewell advises taxonomists to look for consilience (roughly, convergence) between different classificatory schemes, a recommendation that reinforces the identification of natural classes with property clusters. Thus Whewell was not only an early cluster theorist, but one with important insights into what a cluster theory of kinds means for the practice of classification.

The idea that the brain is a representational organ has roots in the nineteenth century, when neurologists began drawing conclusions about what the brain represents from clinical and experimental studies. One of the earliest controversies surrounding representation in the brain was the “muscles versus movements” debate, which concerned whether the motor cortex represents complex movements or rather fractional components of movement. Prominent thinkers weighed in on each side: neurologists John Hughlings Jackson and F.M.R. Walshe in favor of complex movements, neurophysiologist Charles Sherrington and neurosurgeon Wilder Penfield in favor of movement components. This essay examines these and other brain scientists’ evolving notions of representation during the first eighty years of the muscles versus movements debate (c. 1873–1954). Although participants agreed about many of the superficial features of representation, their inferences reveal deep-seated disagreements about its inferential role. Divergent epistemological commitments stoked conflicting conceptions of what representational attributions imply and what evidence supports them.

We do not all make choices, reason, interpret our experience, or respond to our environment in the same way. A recent surge of scientific interest has thrust these individual differences into the spotlight: researchers in cognitive psychology and neuroscience are now devoting increasing attention to cognitive variation. The philosophical dimensions of this research, however, have yet to be systematically explored. Here I make an initial foray by considering how cognitive variation is characterized. I present a central dilemma facing descriptions of individual differences, discuss several distinctions used to categorize variation, and show that these distinctions require further elaboration. Finally, by canvassing several philosophical topics for which the characterization of cognitive variation may have significant implications, I argue that philosophers should take note of how and why our minds differ.

Philosophical work on values in science is held back by widespread ambiguity about how values bear on sci entific choices. Here, I disambiguate several ways in which a choice can be value-laden and show that this disambiguation has the potential to solve and dissolve philosophical problems about values in science. First, I characterize four ways in which values relate to choices: values can motivate, justify, cause, or be impacted by the choices we make. Next, I put my proposed taxonomy to work, using it to clarify one version of the argument from inductive risk. The claim that non-epistemic values must play a role in scientific choices that run inductive risk makes most sense as a claim about values being needed to justify such choices. The argument from inductive risk is not unique: many philosophical arguments about values in science can be more clearly understood and assessed by paying close attention to how values and choices are related.

Neuroscience has become increasingly reliant on multi-subject research in addition to studies of unusual single patients. This research has brought with it a challenge: how are data from different human brains to be combined? The dominant strategy for aggregating data across brains is what I call the ‘cartographic approach’, which involves mapping data from individuals to a spatial template. Here I characterize the cartographic approach and argue that one of its key steps, registration, should be carried out in a way that is sensitive to the target of investigation. Because registration aims to align homologous brain locations, but not all homologous locations can be simultaneously aligned, a multiplicity of registration methods is required to meet the needs of researchers investigating different phenomena. I call this position ‘registration pluralism’. Registration pluralism has potential implications for neuroscientific practice, three of which I discuss here. This work shows the importance of reflecting more carefully on data aggregation methods, especially in light of the substantial individual differences that exist between brains.