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Causal relations among components and activities are intentionally misrepresented in mechanistic explanations found routinely across the life sciences. Since several mechanists explicitly advocate accurately representing factors that make a difference to the outcome, these idealizations conflict with the stated rationale for mechanistic explanation. We argue that these idealizations signal an overlooked feature of reasoning in molecular and cell biology—mechanistic explanations do not occur in isolation—and suggest that explanatory practices within the mechanistic tradition share commonalities with model-based approaches prevalent in population biology.

This article presents and discusses one of the most prominent inferential strategies currently employed in cognitive neuropsychology, namely, reverse inference. Simply put, this is the practice of inferring, in the context of experimental tasks, the engagement of cognitive processes from locations or patterns of neural activation. This technique is notoriously controversial because, critics argue, it presupposes the problematic assumption that neural areas are functionally selective. We proceed as follows. We begin by introducing the basic structure of traditional “location‐based” reverse inference (§1) and discuss the influential lack of selectivity objection (§2). Next, we rehearse various ways of responding to this challenge and provide some reasons for cautious optimism (§3). The second part of the essay presents a more recent development: “pattern‐decoding reverse inference” (§4). This inferential strategy, we maintain, provides an even more convincing response to the lack of selectivity charge. Due to this and other methodological advantages, it is now a prominent component in the toolbox of cognitive neuropsychology (§5). Finally, we conclude by drawing some implications for philosophy of science and philosophy of mind (§6).

The aim of this article is to discuss the conditions under which functional neuroimaging can contribute to the study of higher-cognition. We begin by presenting two case studies-on moral and economic decision-making-which will help us identify and examine one of the main ways in which neuroimaging can help advance the study of higher cognition. We agree with critics that fMRI studies seldom 'refine' or 'confirm' particular psychological hypotheses, or even provide details of the neural implementation of cognitive functions. However, we suggest that neuroimaging can support psychology in a different way, namely, by selecting among competing hypotheses of the cognitive mechanisms underlying some mental function. One of the main ways in which neurimaging can be used for hypothesis selection is via reverse inferences, which we here examine in detail. Despite frequent claims to the contrary, we argue that successful reverse inferences do not assume any strong or objectionable form of reductionism or functional locationism. Moreover, our discussion illustrates that reverse inferences can be successful at early stages of psychological theorizing, when models of the cognitive mechanisms are only partially developed.

This article develops an analysis of disability according to which disabling conditions are properties of organisms embedded in sets of environments. We begin by presenting the three mainstream accounts of disability-the medical, social, and interactionist models-and rehearsing some known limitations. We argue that, because of their primary focus on etiology, all three models share, more or less implicitly, a problematic assumption. This is the tenet that disabilities are individual properties. The second part of the essay presents an "ecological" interpretation of disability, inspired by classic and contemporary research on biological niches. Our proposal preserves many insights underlying extant approaches, while allowing a more accurate characterization of the nature and experience of disability. We conclude by drawing some general implications of our analysis.