Neuroscience is a laboratory-based science that spans multiple levels of analysis from molecular genetics to behavior. At every level of analysis experiments are designed in order to answer empirical questions about phenomena of interest. Understanding the nature and structure of experimentation in neuroscience is fundamental for assessing the quality of the evidence produced by such experiments and the kinds of claims that are warranted by the data. This article provides a general conceptual framework for thinking about evidence and experimentation in (...) neuroscience with a particular focus on two research areas: cognitive neuroscience and cognitive neurobiology. (shrink)

This paper offers a critique of an account of explanatory integration that claims that explanations of cognitive capacities by functional analyses and mechanistic explanations can be seamlessly integrated. It is shown that achieving such explanatory integration requires that the terms designating cognitive capacities in the two forms of explanation are stable but that experimental practice in the mind-brain sciences currently is not directed at achieving such stability. A positive proposal for changing experimental practice so as to promote such stability is (...) put forward and its implications for explanatory integration are briefly considered. (shrink)

In the quest to discover the neural bases of cognition, rigorous behavioral tools are equally as important as sophisticated tools for neural intervention. This paper evaluates several episodes in the development of a novel behavioral tool for rodent cognitive testing, the rodent touchscreen operant chamber. Using conceptual tools on offer in the philosophical literature on exploratory experimentation and control, I illuminate how optimization of this behavioral tool and an understanding of the causal knowledge it may be used to generate historically (...) has involved dynamic interplay between community-driven exploratory practices operating in parallel with causal hypothesis-driven research. (shrink)

In this chapter I investigate the kinds of changes that psychiatric kinds undergo when they become explanatory targets of areas of sciences that are not “mature” and are in the early stages of discovering mechanisms. The two areas of science that are the targets of my analysis are cognitive neuroscience and cognitive neurobiology.

In this chapter, I argue that scientific practice in the neurosciences of cognition is not conducive to the discovery of natural kinds of cognitive capacities. The “neurosciences of cognition” include cognitive neuroscience and cognitive neurobiology, two research areas that aim to understand how the brain gives rise to cognition and behavior. Some philosophers of neuroscience have claimed that explanatory progress in these research areas ultimately will result in the discovery of the underlying mechanisms of cognitive capacities. Once such mechanistic understanding (...) is achieved, cognitive capacities purportedly will be relegated into natural kind categories that correspond to real divisions in the causal structure of the world. I provide reasons here, however, in support of the claim that the neurosciences of cognition currently are not on a trajectory for discovering natural kinds. As I explain, this has to do with how mechanistic explanations of cognitive capacities are developed. Mechanistic explanations and the kinds they explain are abstract representational byproducts of the conceptual, experimental and integrative practices of neuroscientists. If these practices are not coordinated towards developing mechanistic explanations that mirror the causal structure of the world, then natural kinds of cognitive capacities will not be discovered. I provide reasons to think that such coordination is currently lacking in the neurosciences of cognition and indicate where changes in these practices appropriate to the natural kinds ideal would be required if achieving this ideal is indeed the goal. However, I suggest that an evaluation of current practices in these research areas is suggestive that discovering natural kinds of cognitive capacities is not the goal. (shrink)

Experiments in cognitive neuroscience build a setup whose set of controlled stimuli and rules elicits a cognitive process in a participant. This setup requires researchers to decide the value of quite a few parameters along several dimensions. We call ‘’contextual factors’’ the parameters often assumed not to change the cognitive process elicited and are free to vary across the experiment’s repetitions. Against this assumption, empirical evidence shows that many of these contextual factors can significantly influence cognitive performance. Nevertheless, it is (...) not entirely clear what it means for a cognitive phenomenon to be context-sensitive and how to identify context-sensitivity experimentally. We claim that a phenomenon can be context-sensitive either because it is only triggered within a specific context or because different contexts change its manifestation conditions. Assessing which of these forms of context-sensitivity is present in a given phenomenon requires a criterion for individuating it across contextual variations. We argue that some inter-level experiments that, within the mechanistic approach to explanation, are required to identify relations of constitutive relevance between a phenomenon and a mechanism, are also necessary for individuating the phenomenon across its contextual variations. We articulate a criterion according to which behavioral variations across contexts indicate different phenomena if and only if the mechanistic activities, components and/or organizational properties recruited in each context are different. We support this approach by showing how it is applied in paradigmatic studies addressing cognitive performance differences resulting from contextual variations of task features, such as stimulus type and response modality. Finally, we address the challenge that a form of context-sensitivity possessed by the so-called ‘multifunctional mechanisms’ is incompatible with our proposal because it entails that the same mechanism can be recruited in different contexts to produce different phenomena. We examine key cases of multifunctionality and argue that they are consistent with our proposal because a single mechanism can have different components, activities and/or organizational properties in different contexts. Thus, these modifications may not affect the identity of a mechanism, and they could explain how it produced different phenomena in those contexts. (shrink)

According to public health data, the US Hispanic population is far healthier than would be expected for a population with low socioeconomic status. Ever since Kyriakos Markides and Jeannine Coreil highlighted this in a seminal 1986 article, public health researchers have sought to explain the so-called “Hispanic paradox.” Several candidate explanations have been offered over the years, but the debate goes on. This article offers a philosophical analysis that clarifies how two sets of obstacles make it particularly difficult to explain (...) the Hispanic paradox. First, different research projects define the Hispanic paradox phenomenon in substantially different ways. Moreover, using Bas van Fraassen’s pragmatic theory of explanation and Sean Valles’s extension of it with the concept of “phenomenon choice,” it also becomes clear that there are also multiple ways of explaining each individual definition of the phenomenon. A second set of philosophical and methodological challenges arises during any attempt to study “Hispanic” phenomena, with one key challenge being that the “Hispanic” panethnic concept was intentionally made vague as it was developed and popularized during the 1960s–1970s. After comparing this case with similar cases in the philosophical literature, the article concludes with observations on what makes this problem unique, particularly its ethical features. (shrink)

Recently, some mechanists have embraced reductionism and some reductionists have endorsed mechanism. However, the two camps disagree sharply about the extent to which mechanistic explanation is a reductionistic enterprise. Reductionists maintain that cellular and molecular mechanisms can explain mental phenomena without necessary appeal to higher-level mechanisms. Mechanists deny this claim. I argue that this dispute turns on whether reduction is a transitive relation. I show that it is. Therefore, mechanistic explanations at the cellular and molecular level explain mental phenomena. I (...) make my case in part by noting that mechanisms at higher levels are composed of mechanisms at lower levels. Compositional relations are transitive. In addition, they are explanatory. I conclude that there are explanatory linkages from cellular and molecular mechanisms to mental phenomena within a hierarchy of nested mechanisms. (shrink)

In this article, I explain why stabilizing constructs is important to the success of the Research Domain Criteria Project and identify one measure for facilitating such stability.

Optogenetic techniques are described as “revolutionary” for the unprecedented causal control they allow neuroscientists to exert over neural activity in awake behaving animals. In this paper, I demonstrate by means of a case study that optogenetic techniques will only illuminate causal links between the brain and behavior to the extent that their error characteristics are known and, further, that determining these error characteristics requires comparison of optogenetic techniques with techniques having well known error characteristics and consideration of the broader neural (...) and behavioral context in which the targets of optogenetic interventions are situated. (shrink)

The past decade has witnessed a growing awareness of conceptual and methodological hurdles within psychology and neuroscience that must be addressed for taxonomic and explanatory progress in understanding psychological functions to be possible. In this paper, I evaluate several recent knowledge-building initiatives aimed at overcoming these obstacles. I argue that while each initiative offers important insights about how to facilitate taxonomic and explanatory progress in psychology and neuroscience, only a “coordinated pluralism” that incorporates positive aspects of each initiative will have (...) the potential for success. (shrink)

One reason for the popularity of Craver’s mutual manipulability account of constitutive relevance is that it seems to make good sense of the experimental practices and constitutive reasoning in the life sciences. Two recent papers propose a theoretical alternative to in light of several important conceptual objections. Their alternative approach, the no de-coupling account, conceives of constitution as a dependence relation that once postulated provides the best explanation of the impossibility of breaking the common cause coupling of a macro-level mechanism (...) and its micro-level components. This entails an abductive view of constitutive inference. Proponents of the NDC or abductive account recognize that their discussion leaves open a big question concerning the practical dimension of the notion of constitutive relevanssssce: Is it possible to faithfully reconstruct constitutional reasoning in science in terms of a failure to de-couple, via interlevel experiments, phenomena from their mechanistic constituents? Focusing on the field of memory and long-term potential research, this article argues that the abductive account provides a more adequate description of interlevel experiments in neuroscience. We also suggest that the account highlights some significant practical recommendations of how to interpret the findings of interlevel experiments. 1Introduction 2Mutual Manipulability and Constitutive Relevance 2.1Constitutive relevance through an interventionist lens 2.2Mutual manipulability: A methodological application 3Trouble for the Mutual Manipulability Account 4The Abductive Account of Constitution 5The Abductive Account: A Methodological Application 5.1Long-term potential and memory experiments 5.2A comparative summary 6Conclusions. (shrink)

Constructivists about memory argue that memory is a capacity for building representations of past events from a generalized information store. The view is motivated by the memory errors discovered in cognitive psychology. Little has been known about the neural mechanisms by which false memories are produced. Recently, using a method I call the Optogenetic False Memory Technique, neuroscientists have created false memories in mice. In this paper, I examine how Constructivism fares in light of O-FaMe results. My aims are two-fold. (...) First, I argue that errors found in O-FaMe and cognitive psychology are similar behaviorally. Second, Constructivists should be able to explain the former since they purport to explain the latter, but they cannot. I conclude that O-FaMe studies reveal details about the mechanism by which false memories are produced that are incompatible with the explanatory approach to false memories favored by Constructivism. (shrink)

Animal models have received particular attention as key examples of material models. In this paper, we argue that the specificities of establishing animal models—acknowledging their status as living beings and as epistemological tools—necessitate a more complex account of animal models as materialised models. This becomes particularly evident in animal-based models of diseases that only occur in humans: in these cases, the representational relation between animal model and human patient needs to be generated and validated. The first part of this paper (...) presents an account of how disease-specific animal models are established by drawing on the example of transgenic mice models for Alzheimer’s disease. We will introduce an account of validation that involves a three-fold process including from human being to experimental organism; from experimental organism to animal model; and from animal model to human patient. This process draws upon clinical relevance as much as scientific practices and results in disease-specific, yet incomplete, animal models. The second part of this paper argues that the incompleteness of models can be described in terms of multi-level abstractions. We qualify this notion by pointing to different experimental techniques and targets of modelling, which give rise to a plurality of models for a specific disease. (shrink)

The philosophical interest in experimental practice in neuroscience has brought renewed attention to the study of the development and use of techniques and tools for data production. John Bickle has argued that the construction and progression of theories in neuroscience are entirely dependent on the development and ingenious use of research tools. In Bickle's account, theory plays a tertiary role, as it depends on what the tools allow researchers to manipulate, and the tools, in turn, are developed not in order (...) to test theories but as solutions to engineering problems. However, Bickle's account is not entirely precise in explaining what informs researchers' decision-making in their atheoretical laboratory tinkering. Identifying the sources that guide researchers in tool development and use is crucial if one wishes to contribute to the philosophical or meta-scientific understanding of experimental practice in neuroscience. In the following paper, I claim that decision-making in tools' development and use in neuroscience is doubly guided. Pre-existing theory and concepts determine information's relevance, whereas tools' functioning in controlled situations determines information's reliability. Accordingly, experimenters' decision-making is situated both in the context of analysing, modelling or interpreting information and in the context of producing information. I study the case of the tungsten microelectrode developed by David Hubel during the 1950s. First, I show that pre-existing theory and concepts determine in advance what information would be relevant to obtain from the microelectrode. Second, I show that Hubel's tinkering follows the guidelines derived from the very structure of what we recognise as reliable experimentally produced information. Finally, I suggest that data-production processes allow experimenters to assess what to expect from an experimental system in terms of concept- and theory-generation and confirmation, thereby endorsing Bickle's tenet on the tertiary role of theory in neuroscience. (shrink)

The central task of cognitive neuroscience to map cognitive capacities to neural mechanisms faces three interlocking conceptual problems that together frame the problem of cognitive ontology. First, they must establish which tasks elicit which cognitive capacities, and specifically when different tasks elicit the same capacity. To address this operationalization problem, scientists often assess whether the tasks engage the same neural mechanisms. But to determine whether mechanisms are of the same or different kinds, we need to solve the abstraction problem by (...) determining which mechanistic differences are and are not relevant, and also the boundary problem by distinguishing the mechanism from its background conditions. Solving these problems, in turn, requires understanding how cognitive capacities are elicited in tasks. These three problems, which have been noted and discussed elsewhere in the literature, together form a ‘cycle of kinds’ that frames the central problem-space of cognitive ontology. We describe this cycle to clarify the intellectual challenges facing the cognitive ontologist and to reveal the kind of iterative process by which ontological revision in cognitive neuroscience is likely to unfold. (shrink)

This paper revisits the debate between Harry Collins and Allan Franklin, concerning the experimenters’ regress. Focusing my attention on a case study from recent psychology (regarding experimental evidence for the existence of a Mozart Effect), I argue that Franklin is right to highlight the role of epistemological strategies in scientific practice, but that his account does not sufficiently appreciate Collins’s point about the importance of tacit knowledge in experimental practice. In turn, Collins rightly highlights the epistemic uncertainty (and skepticism) surrounding (...) much experimental research. However, I will argue that his analysis of tacit knowledge fails to elucidate the reasons why scientists often are (and should be) skeptical of other researchers’ experimental results. I will present an analysis of tacit knowledge in experimental research that not only answers to this desideratum, but also shows how such skepticism can in fact be a vital enabling factor for the dynamic processes of experimental knowledge generation. (shrink)

Data production in experimental sciences depends on localised experimental systems, but the epistemic properties of data transcend the contingencies of the processes that produce them. Philosophers often believe that experimental systems instantiate but do not produce the epistemic properties of data. In this paper, we argue that experimental systems' local functioning entails intrinsic capacities to produce the epistemic properties of data. We develop this idea by applying Derrida's model of arche-writing to study a case of theory-oriented experimental practice. Derrida's model (...) relativises or dissolves the conceptual distinction between the moment of data production and a subsequent moment of data dissemination. It thus has consequences for understanding both data production (despite being intrinsically local, data production a priori generates transferrable and modellable information) and data dissemination (when modelling information, researchers needs to refer this information to the context of its production). We study a case of data production in a non-exploratory experimental system designed to test a pre-existing hypothesis in visual neuroscience. A case of theory-oriented experimental practice should allow us to identify the autonomous functioning of experimental systems in data production more clearly, insofar as it allows us to study the limits of pre-existing theory in the activities of these systems. We suggest that pre-existing concepts, hypotheses and theories condition the relevance but not the production of experimental data. (shrink)

Although researchers in psychiatry have been trying for decades to elucidate the pathophysiology underlying mental disorders, relatively little progress has been made. One explanation for this failure is that diagnostic categories in psychiatry are unlikely to track underlying neurological mechanisms. Because of this, the US National Institutes of Mental Health has recently developed a novel ontology to guide research in biological psychiatry: the Research Domain Criteria. In this paper, I argue that while RDoC may lead to better neuroscientific explanations for (...) mental disorders, it is unlikely that this new knowledge will then lead to an improved diagnostic system. I therefore suggest that researchers in psychiatry should work toward the development of two new ontologies: one for research and one for clinical practice. (shrink)

Neurobiologists talk of linking mind to molecular dynamics in and between neurons. Such talk is dismissed by cognitive scientists, including many cognitive neuroscientists, due to the number of “levels” that separate behaviors from these molecular events. In this paper I explain what neurobiologists mean by such claims by describing the kinds of experiment tools that have forged these linkages, directly on lab benches. I here focus on one of these tools, gene targeting techniques, brought into behavioral neuroscience from developmental biology (...) more than a quarter-century ago. Discussion of this tool does more than illuminate these claims by neurobiologists, however. An account of its development shows the doubly dependent role that theory plays in neurobiology. Our best current theories about “how the brain works” depend entirely on the experiment tools neuroscientists have available. And these tools get developed via the solution of engineering problems, not the application of theory. Theory is thus of tertiary importance in neuroscience, not of the primary importance that many cognitive scientists assume it to occupy. (shrink)

I introduce two new tools in experimental neurobiology, optogenetics and DREADDs. These tools permit unprecedented control over activity in specific neurons in behaving animals. In addition to their inherent scientific interest, these tools make an important contribution to philosophy of science. They illustrate the very premises of Ian Hacking’s “microscope” argument for the relative independence of experiment from theory. This new example is important for generalizing Hacking’s argument because the background sciences and the fields of engineering producing these tools differ (...) significantly. (shrink)

Many people think of empathy as a powerful force for good within society and as a crucial component of moral cognition. Recently, prominent theorists in psychology and philosophy have challenged this viewpoint and mounted a case against empathy. The most compelling versions of this case rely heavily on empirical evidence from psychology and neuroscience. They contend that the inherent partiality and parochialism of empathy undermines its potential to serve moral ends. This paper argues that the argument against empathy overreaches; it (...) makes an unwarranted inference from descriptive evidence about our empathic capacities to normative claims about the proper role of empathy in culture, society, and ethics. The paper concludes with suggestions about how to use insights from the empirical literature to build a more nuanced and contextually sensitive account of the role of empathy in our ethical lives. (shrink)

Research devoted to characterizing phenomena is underappreciated in philosophical accounts of scientific inquiry. This paper develops a diachronic analysis of research over 100 years that led to the recognition of two related electrophysiological phenomena, the membrane potential and the action potential. A diachronic perspective allows for reconciliation of two threads in philosophical discussions of phenomena—Hacking’s treatment of phenomena as manifest in laboratory settings and Bogen and Woodward’s construal of phenomena as regularities in the world. The diachronic analysis also reveals the (...) epistemic tasks that contribute to establishing phenomena, including the development of appropriate investigative techniques and concepts for characterizing them. (shrink)

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. (shrink)

Over the past three decades, philosophy of science has grown increasingly “local.” Concerns have switched from general features of scientific practice to concepts, issues, and puzzles specific to particular disciplines. Philosophy of neuroscience is a natural result. This emerging area was also spurred by remarkable recent growth in the neurosciences. Cognitive and computational neuroscience continues to encroach upon issues traditionally addressed within the humanities, including the nature of consciousness, action, knowledge, and normativity. Empirical discoveries about brain structure and function suggest (...) ways that “naturalistic” programs might develop in detail, beyond the abstract philosophical considerations in their favor. -/- The literature distinguishes “philosophy of neuroscience” and “neurophilosophy.” The former concerns foundational issues within the neurosciences. The latter concerns application of neuroscientific concepts to traditional philosophical questions. Exploring various concepts of representation employed in neuroscientific theories is an example of the former. Examining implications of neurological syndromes for the concept of a unified self is an example of the latter. In this entry, we will assume this distinction and discuss examples of both. (shrink)

Scientists represent their world, grouping and organizing phenomena into classes by means of concepts. Philosophers of science have historically been interested in the nature of these concepts, the criteria that inform their application and the nature of the kinds that the concepts individuate. They also have sought to understand whether and how different systems of classification are related and more recently, how investigative practices shape conceptual development and change. Our aim in this paper is to provide a critical overview of (...) some of the key developments in this philosophical literature and identify some interesting issues it raises about the prospects of the so-called “special sciences”, including psychiatry, psychology, and the mind-brain sciences more generally, to discover natural kinds. (shrink)

Crime is a serious social problem, but its causes are not exclusively social. There is growing consensus that explaining and preventing it requires interdisciplinary research efforts. Indeed, the landscape of contemporary criminology includes a variety of theoretical models that incorporate psychological, biological and sociological factors. These multi-disciplinary approaches, however, have yet to radically advance scientific understandings of crime and shed light on how to manage it. In this paper, using conceptual tools on offer in the philosophy of science in combination (...) with theoretical work represented in this special volume of Psychology, Crime and Law, I provide some perspective on why explanatory progress in criminology has remained elusive and evaluate some positive proposals for attaining it. -/- . (shrink)

In 2007, ten world-renowned neuroscientists proposed “A Decade of the Mind Initiative.” The contention was that, despite the successes of the Decade of the Brain, “a fundamental understanding of how the brain gives rise to the mind [was] still lacking” (2007, 1321). The primary aims of the decade of the mind were “to build on the progress of the recent Decade of the Brain (1990-99)” by focusing on “four broad but intertwined areas” of research, including: healing and protecting, understanding, enriching, (...) and modeling the mind. These four aims were to be the result of “transdisciplinary and multiagency” research spanning “across disparate fields, such as cognitive science, medicine, neuroscience, psychology, mathematics, engineering, and computer science.” The proposal for a decade of the mind prompted many questions (See Spitzer 2008). In this chapter, I address three of them: (1) How do proponents of this new decade conceive of the mind? (2) Why should a decade be devoted to understanding it? (3) What should this decade look like? (shrink)

How can we explain the intentional nature of an expert’s actions, performed without immediate and conscious control, relying instead on automatic cognitive processes? How can we account for the differences and similarities with a novice’s performance of the same actions? Can a naturalist explanation of intentional expert action be in line with a philosophical concept of intentional action? Answering these and related questions in a positive sense, this dissertation develops a three-step argument. Part I considers different methods of explanations in (...) cognitive neuroscience (Bennett & Hacker’s philosophical, conceptual analysis; Marr’s three levels of explanation; Neural Correlates of Consciousness research; mechanistic explanation), defending ‘mechanistic explanation’ as a method that provides the necessary tools for integrating interdisciplinary insights into human action. Furthermore, a dynamic, explanatory mechanism allows the assessment of the impact of learning and development on expert action in a valuable way that other methods don’t. Part II continues by scrutinizing several cognitive neuroscientific theories of learning and development (neuroconstructivism; dual-processing theories; simulation theory; extended mind/cognition hypothesis), arguing for the complex interactions between different types of processing and different action representations involved in expert action performances. Moreover, according to our discussion of a particular ‘simulation theory’ these interactions can be influenced in several ways with the use of language, allowing an agent to configure a specific action representation for performance at a later stage. The results of Parts I and II are then applied in Part III to a parallel discussion of philosophical analyses of intentional action (discussing i.a. Frankfurt, Bratman, Pacherie and Ricoeur) and cognitive neuroscientific insights in it. Both approaches are found to converge in emphasizing the importance for an expert to develop stable patterns of actions that comply maximally not only with his intentions, but also with his motor expertise and with situational conditions. Consequently, his actions – automatic, or not – rely on this ‘sculpted space of actions’. (shrink)