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)

The sceptical positions philosophers have adopted with respect to neuroimaging data are based on detailed evaluations of subtraction, which is one of many data analysis techniques used with neuroimaging data. These positions are undermined when the epistemic implications of the use of a diversity of data analysis techniques are taken into account. I argue that different data analysis techniques reveal different patterns in the data. Through the use of multiple data analysis techniques, researchers can produce results that are locally robust. (...) Thus, the epistemology of neuroimaging must take into consideration the details of the different data analysis techniques that are used to evaluate neuroimaging data, and the specific theoretical aims those techniques are deployed towards. _1_ Introduction _2_ Scepticism about Neuroimaging _3_ Data Analysis and Evidence _4_ Deconvolution and Pattern Classification Analysis _4.1_ Deconvolution analysis _4.2_ Region of interest selection _4.3_ Pattern classification analysis _5_ The Strength of Multiple Analyses _6_ Conclusion. (shrink)

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. (shrink)

In the 1960s, theoretical biologist Richard Levins criticised modellers in his own discipline of population biology for pursuing the “brute force” strategy of building hyper-realistic models. Instead of exclusively chasing complexity, Levins advocated for the use of multiple different kinds of complementary models, including much simpler ones. In this paper, I argue that the epistemic challenges Levins attributed to the brute force strategy still apply to state-of-the-art climate models today: they have big appetites for unattainable data, they are limited by (...) computational tractability, and they are incomprehensible to the human modeller. Along the lines Levins described, this uncertainty generates a trade-off between realistic, precise models with predictive power and simple, highly idealised models that facilitate understanding. In addition to building ensembles of highly complex dynamical models, climate modellers can address model uncertainty by comparing models of different types, such as dynamical and data-driven models, and by systematically comparing models at different levels of what climate modellers call the model hierarchy. Despite its age, Levins’ paper remains incredibly insightful and should be considered an important entry into the philosophy of computational modelling. (shrink)

Driven by an appreciation of the field’s early stage of development, I apply the concept of exploratory experimentation, originally put forward in the late 90s philosophy of biology, to current research in cognitive neuroscience. I concentrate on functional magnetic resonance imaging and how this wide-spread technique is used, from experimental design to data analysis. I claim that, although subject to certain significant modifications with respect to the concept’s original rendering, the exploratory character of neuroimaging experiments can be appreciated considering their (...) goals, centered on the stabilization of experimental systems for phenomenological description, and the relevance of their methodological facet. Although I do not claim that there is a specific kind of experiment that one can single out as definitely exploratory, exploration can be seen as a general trait imbuing fMRI-based experimentation. (shrink)

The neural reuse framework developed primarily by Michael Anderson proposes that brain regions are involved in multiple and diverse cognitive tasks and that brain regions flexibly and dynamically interact in different combinations to carry out cognitive functioning. We argue that the evidence cited by Anderson and others falls short of supporting the fundamental principles of neural reuse. We map out this problem and provide solutions by drawing on recent advances in network neuroscience, and we argue that methods employed in network (...) neuroscience provide the means to fully engage in a research program operating under the principles of neural reuse. (shrink)

Since its introduction, multivariate pattern analysis, or ‘neural decoding’, has transformed the field of cognitive neuroscience. Underlying its influence is a crucial inference, which we call the decoder’s dictum: if information can be decoded from patterns of neural activity, then this provides strong evidence about what information those patterns represent. Although the dictum is a widely held and well-motivated principle in decoding research, it has received scant philosophical attention. We critically evaluate the dictum, arguing that it is false: decodability is (...) a poor guide for revealing the content of neural representations. However, we also suggest how the dictum can be improved on, in order to better justify inferences about neural representation using MVPA. (shrink)

Since its introduction, multivariate pattern analysis, or ‘neural decoding’, has transformed the field of cognitive neuroscience. Underlying its influence is a crucial inference, which we call the decoder’s dictum: if information can be decoded from patterns of neural activity, then this provides strong evidence about what information those patterns represent. Although the dictum is a widely held and well-motivated principle in decoding research, it has received scant philosophical attention. We critically evaluate the dictum, arguing that it is false: decodability is (...) a poor guide for revealing the content of neural representations. However, we also suggest how the dictum can be improved on, in order to better justify inferences about neural representation using MVPA. 1Introduction 2A Brief Primer on Neural Decoding: Methods, Application, and Interpretation 2.1What is multivariate pattern analysis? 2.2The informational benefits of multivariate pattern analysis 3Why the Decoder’s Dictum Is False 3.1We don’t know what information is decoded 3.2The theoretical basis for the dictum 3.3Undermining the theoretical basis 4Objections and Replies 4.1Does anyone really believe the dictum? 4.2Good decoding is not enough 4.3Predicting behaviour is not enough 5Moving beyond the Dictum 6Conclusion. (shrink)

In this essay, we examine the use of resting state fMRI data for psychological inferences. We argue that resting state studies hold the paired promises of discovering novel functional brain networks, and of avoiding some of the limitations of task-based fMRI. However, we argue that the very features of experimental design that enable resting state fMRI to support exploratory science also generate a novel confound. We argue that seemingly key features of resting state functional connectivity networks may be artefacts resulting (...) from sampling a ‘mixture distribution’ of diverse brain networks active at different times during the scan. We explore the consequences of this ‘mixture view’ for attempts to theorize about the cognitive or psychological functions of resting state networks, as well as the value of exploratory experiments. (shrink)

Many take the possibility of hallucinations to imply that a relationalist account, according to which perceptual experiences are constituted by direct relations to ordinary mind-independent objects, is false. The common reaction among relationalists is to adopt a disjunctivist view that denies that hallucinations have the same nature as perceptual experiences. This paper proposes a non-disjunctivist response to the argument from hallucination by arguing that the alleged empirical and a priori evidence in support of the possibility of hallucinations is inconclusive. A (...) corollary upshot of the article is that whether hallucinations are possible or not is still an open empirical question. (shrink)

The role of culture in shaping folk psychology and mindreading has been neglected in the philosophical literature. This paper shows that there are significant cultural differences in how psychological states are understood and used by drawing on Spaulding’s recent distinction between the ‘goals’ and ‘methods’ of mindreading to argue that the relations between these methods vary across cultures; and arguing that differences in folk psychology cannot be dismissed as irrelevant to the cognitive architecture that facilitates our understanding of psychological states. (...) The paper concludes that any good account of social cognition must have the conceptual resources to explain how culture affects our understanding of psychological states, and that this explanandum should not be an after-thought but instead a guiding feature for those accounts. (shrink)

Our analysis of microbiota-gut-brain research took MGB to task for some of its methods, concepts, and interpretations. Commentators then raised numerous issues about the neuroscientific and microbiome aspects of MGB and how it can be understood as a field. We respond by addressing the dimensionality and causal focus of MGB.

Empirical research into moral decision-making is often taken to have normative implications. For instance, in his recent book, Greene (2013) relies on empirical findings to establish utilitarianism as a superior normative ethical theory. Kantian ethics, and deontological ethics more generally, is a rival view that Greene attacks. At the heart of Greene’s argument against deontology is the claim that deontological moral judgments are the product of certain emotions and not of reason. Deontological ethics is a mere rationalization of these emotions. (...) Accordingly Greene maintains that deontology should be abandoned. This paper is a defense of deontological ethical theory. It argues that Greene’s argument against deontology needs further support. Greene’s empirical evidence is open to alternative interpretations. In particular, it is not clear that Greene’s characterization of alarm-like emotions that are relative to culture and personal experience is empirically tenable. Moreover, it is implausible that such emotions produce specifically deontological judgments. A rival sentimentalist view, according to which all moral judgments are determined by emotion, is at least as plausible given the empirical evidence and independently supported by philosophical theory. I therefore call for an improvement of Greene’s argument. (shrink)

Since the mid‐1980s, psychologists and neuroscientists have used brain imaging to test hypotheses about human thought processes and their neural instantiation. In just three decades, functional neuroimaging has been transformed from a crude clinical tool to a widely used research method for understanding the human brain and mind. Such rapidly achieved success is bound to evoke skepticism. A degree of skepticism toward new methods and ideas is both inevitable and useful in any field. It is especially valuable in a science (...) as young as cognitive neuroscience and its even younger siblings, social and affective neuroscience. Healthy skepticism encourages us to check our assumptions, recognize the limitations of our methods, and proceed thoughtfully. Skepticism itself, however, also must be examined.In this article, I review the most commonly voiced criticisms of functional neuroimaging. In the spirit of healthy skepticism, I will critically examine these criticisms themselves. Each contains at least a kernel of truth, although I will argue that in some cases the kernel has been overextended in ways that are inaccurate or misleading. (shrink)

In cognitive science, the concept of dissociation has been central to the functional individuation and decomposition of cognitive systems. Setting aside debates about the legitimacy of inferring the existence of dissociable systems from ‘behavioural’ dissociation data, the main idea behind the dissociation approach is that two cognitive systems are dissociable, and thus viewed as distinct, if each can be damaged, or impaired, without affecting the other system’s functions. In this article, I propose a notion of functional independence that does not (...) require dissociability, and describe an approach to the functional decomposition and modelling of cognitive systems that complements the dissociation approach. I show that highly integrated cognitive and neurocognitive systems can be decomposed into non-dissociable but functionally independent components, and argue that this approach can provide a general account of cognitive specialization in terms of a stable structure–function relationship. 1 Introduction2 Functional Independence without Dissociability3 FI Systems and Cognitive Architecture4 FI Systems and Cognitive Specialization. (shrink)

In this paper, I summarize an emerging debate in the cognitive sciences over the right taxonomy for understanding cognition – the right theory of and vocabulary for describing the structure of the mind – and the proper role of neuroscientific evidence in specifying this taxonomy. In part because the discussion clearly entails a deep reconsideration of the supposed autonomy of psychology from neuroscience, this is a debate in which philosophers should be interested, with which they should be familiar, and to (...) which they should contribute. Here, I outline some of the positions being advocated, and reflect on some of the possible implications of this work both for scientific and folk psychology. (shrink)

Several developments for diverse scientific goals, mostly in physics and physiology, had to take place, which eventually gave us fMRI as one of the central research paradigms of contemporary cognitive neuroscience. This technique stands on solid foundations established by the physics of magnetic resonance and the physiology of hemodynamics and is complimented by computational and statistical techniques. I argue, and support using concrete examples, that these foundations give rise to a productive theory-ladenness in fMRI, which enables researchers to identify and (...) control for the types of methodological and inferential errors. Consequently, this makes it possible for researchers to represent and investigate cognitive phenomena in terms of hemodynamic data and for experimental knowledge to grow independently of large scale theories of cognition. (shrink)

This PhD thesis focuses on the philosophical foundations of Neuroeconomics, an innovative research program which combines findings and modelling tools from economics, psychology and neuroscience to account for human choice behaviour. The proponents of Neuroeconomics often manifest the ambition to foster radical modifications in the accounts of choice behaviour developed by its parent disciplines. This enquiry provides a philosophically informed appraisal of the potential for success and the relevance of neuroeconomic research for economics. My central claim is that neuroeconomists can (...) help other economists to build more predictive and explanatory models, yet are unlikely to foster revolutionary modifications in the economic theory of choice. The contents are organized as follows. In chapters 1-2, I present neuroeconomists’ investigative tools, distinguish the most influential approaches to neuroeconomic research and reconstruct the case in favour of a neural enrichment of economic theory. In chapters 3-7, I combine insights from neuro-psychology, economic methodology and philosophy of science to develop a systematic critique of Neuroeconomics. In particular, I articulate four lines of argument to demonstrate that economists are provisionally justified in retaining a methodologically distinctive approach to the modelling of decision making. My first argument points to several evidential and epistemological concerns which complicate the interpretation of neural data and cast doubt on the inferences neuroeconomists often make in their studies. My second argument aims to show that the trade-offs between the modelling desiderata that neuroeconomists and other economists respectively value severely constrain the incorporation of neural insights into economic models. My third argument questions neuroeconomists’ attempts to develop a unified theory of choice behaviour by identifying some central issues on which they hold contrasting positions. My fourth argument differentiates various senses of the term ‘revolution’ and illustrates that neuroeconomists are unlikely to provide revolutionary contributions to economic theory in any of these senses. (shrink)

Several developments for diverse scientific goals, mostly in physics and physiology, had to take place, which eventually gave us fMRI as one of the central research paradigms of contemporary cognitive neuroscience. This technique stands on solid foundations established by the physics of magnetic resonance and the physiology of hemodynamics and is complimented by computational and statistical techniques. I argue, and support using concrete examples, that these foundations give rise to a productive theory-ladenness in fMRI, which enables researchers to identify and (...) control for the types of methodological and inferential errors. Consequently, this makes it possible for researchers to represent and investigate cognitive phenomena in terms of hemodynamic data and for experimental knowledge to grow independently of large scale theories of cognition. (shrink)

I propose a narrative fabrication thesis of dream reports, according to which dream reports are often not accurate representations of experiences that occur during sleep. I begin with an overview of anti-experience theses of Norman Malcolm and Daniel Dennett who reject the received view of dreams, that dreams are experiences we have during sleep which are reported upon waking. Although rejection of the first claim of the received view, that dreams are experiences that occur during sleep, is implausible, I evaluate (...) in more detail the second assumption of the received view, that dream reports are generally accurate. I then propose a “narrative fabrication” view of dreams as an alternative to the received view. Dream reports are often confabulated or fabricated because of poor memory, bizarre dream content, and cognitive deficits. It is well documented that narratives can be altered between initial rapid eye movement sleep awakenings and subsequent reports. I argue that we have reason to suspect that initial reports are prone to inaccuracy. Experiments demonstrate that subjects rationalize strange elements in narratives, leaving out supernatural or bizarre components when reporting waking memories of stories. Inaccuracies in dream reports are exacerbated by rapid memory loss and bizarre dream content. Waking memory is a process of reconstruction and blending of elements, but unlike waking memory, we cannot reality-test for dream memories. Dream experiences involve imaginative elements, and dream content cannot be verified with external evidence. Some dreams may involve wake-like higher cognitive functions, such as lucid dreams. Such dreams are more likely to elicit accurate reports than cognitively deficient dreams. However, dream reports are generally less accurate than waking reports. I then propose methods which could verify the narrative fabrication view, and argue that although the theory cannot be tested with current methods, new techniques and technologies may be able to do so in the future. -/- . (shrink)

Neuroimaging technology is the most widely used tool to study human cognition. While originally a promising tool for mapping the content of cognitive theories onto the structures of the brain, recently developed tools for the analysis, handling and sharing of data have changed the theoretical landscape of cognitive neuroscience. Even with these advancements philosophical analyses of evidence in neuroimaging remain skeptical of the promise of neuroimaging technology. These views often treat the analysis techniques used to make sense of data produced (...) in a neuroimaging experiment as one, attributing the inferential limitations of analysis pipelines to the technology as a whole. Situated against the neuroscientists own critical assessment of their methods and the limitations of those methods, this skepticism appears based on a misunderstanding of the role data analysis techniques play in neuroimaging research. My project picks up here, examining how data analysis techniques, such as pattern classification analysis, are used to assess the evidential value of neuroimaging data. The project takes the form of three papers. In the first I identify the use of multiple data analysis techniques as an important aspect of the data interpretation process that is overlooked by critics. In the second I develop an account of inferences in neuroimaging research that is sensitive to this use of data analysis techniques, arguing that interpreting neuroimaging data is a process of isolating and explaining a variety of data patterns. In the third I argue that the development and uptake of new techniques for analyzing data must be accompanied by changes in research practices and standards of evidence if they are to promote knowledge generation. My approach to this work is both traditionally philosophical, insofar as it involves reading and analyzing the work of philosophers and neuroscientists, and embedded insofar as most of the research was conducted while engaged in attending lab meetings and participating in the work of those scientists whose work is the object of my research. (shrink)

This chapter analyses the prospects of using neuroimaging methods, in particular functional magnetic resonance imaging (fMRI), for philosophical purposes. To do so, it will use two case studies from the field of emotion research: Greene et al. (2001) used fMRI to uncover the mental processes underlying moral intuitions, while Lindquist et al. (2012) used fMRI to inform the debate around the nature of a specific mental process, namely, emotion. These studies illustrate two main approaches in cognitive neuroscience: Reverse inference and (...) ontology testing, respectively. With regards to Greene et al.’s study, the use of Neurosynth (Yarkoni 2011) will show that the available formulations of reverse inference, although viable a priori, seem to be of limited use in practice. On the other hand, the discussion of Lindquist et al.’s study will present the so far neglected potential of ontology-testing approaches to inform philosophical questions. (shrink)