This paper responds to a recent challenge for the validity of extrapolation of neurobiological knowledge from laboratory animals to humans. According to this challenge, experimental neurobiology, and thus neuroscience, is in a state of crisis because the knowledge produced in different laboratories hardly generalizes from one laboratory to another. Presumably, this is so because neurobiological laboratories use simplified animal models of human conditions that differ across laboratories. By contrast, I argue that maintaining a multiplicity of experimental protocols and simple models is well justified. It fosters rather than precludes the validity of extrapolation of neurobiological knowledge. The discipline is thriving.

I have previously argued that stem cell experiments cannot demonstrate that a single cell is a stem cell (Fagan 2013a, b). Laplane and others dispute this claim, citing experiments that identify stem cells at the singlecell level. This paper rebuts the counterexample, arguing that the alleged ‘crucial stem cell experiments’ do not measure self-renewal for a single cell, do not establish a single cell’s differentiation potential, and, if interpreted as providing results about single cells, fall into epistemic circularity. I then discuss the source of the dispute, locating it in differences between philosophical and experimental perspectives.

What makes a high-quality biomarker experiment? The success of personalized medicine hinges on the answer to this question. In this paper, I argue that judgment about the quality of biomarker experiments is mediated by the problem of theoretical underdetermination. That is, the network of biological and pathophysiological theories motivating a biomarker experiment is sufficiently complicated that it often frustrates valid interpretation of the experimental results. Drawing on a case-study in biomarker diagnostic development from neurooncology, I argue that this problem of underdetermination can be overcome with greater coordination across the biomarker research trajectory. I then sketch an account for how coordination across a research trajectory can be evaluated. I ultimate conclude that what makes a high-quality biomarker experiment must be judged by the epistemic contribution it makes to this coordinated research effort.

The replicability of experiment is routinely offered as the gold standard of evidence. I argue that it is not supported by a universal principle of replicability in inductive logic. A failure of replication may not impugn a credible experimental result; and a successful replication can fail to vindicate an incredible experimental result. Rather, employing a material approach to inductive inference, the evidential import of successful replication of an experiment is determined by the prevailing background facts. Commonly, these background facts do support successful replication as a good evidential guide and this has fostered the illusion of a deeper, exceptionless principle.

According to the Disjunction Problem, teleological theories of perceptual content are unable to explain why it is that a subject represents an F when an F causes the perception and not the disjunction F v G, given that the subject has mistaken G’s for F’s in the past. Without an adequate explanation these theories are stuckwithout an account of how non-veridical representation is possible, which would be an unsettling result. In this paper I defend Burge’s teleological theory of perception against the Disjunction Problem, arguing that a perceptual state’s representing what I call an error-prohibiting disjunctive property is incompatible with the truth of perceptual anti-individualism. And because perceptual anti-individualism is at the heart of Burge’s theory, I conclude that Burgeans need not be concerned with the Disjunction Problem.

Normative characterization is a commonplace feature of biological and cognitive explanation. Such language seems to commit the biological and cognitive sciences to the existence of natural norms, but it is also difficult to understand how such normativity fits into a natural world of physical causes and forces. I propose to map normativity onto systems stabilized by counteractive constraints. Such a mapping, I believe, can explain normativity’s causal-explanatory role in biological and cognitive inquiry. The common approach in the literature is to derive an account of natural normativity by way of a particular theory of function. I avoid that approach here and attempt to address directly the sort of physical systems that might satisfy naturalizing criteria for normativity. This has the advantages, I think, of allowing an account of normativity without first having to decide the correct theory of function as well as allowing for the theoretical possibility that normative and functional explanation might come apart within empirical explanation.

In this paper, we argue that symbols are conventional vehicles whose chief function is denotation, while models are epistemic vehicles, and their chief function is to show what their targets are like in the relevant aspects. And we explain why this is incompatible with the deflationary view on scientific modeling. Although the same object may serve both functions, the two vehicles are conceptually distinct and most models employ both elements. With the clarification of this point we offer an alternative account to the deflationary view – the Hybrid Account; and we defend our account in contrast with deflationism.