This chapter explores some of the ways that mechanisms are invoked in neuroscience and looks at a selection of the philosophical problems that arise when trying to understand mechanistic explanations. It introduces a series of historical case studies that illustrate how neuroscientists have depended on mechanistic metaphors in their efforts to understand the mind and brain, and how their mechanistic explanations have developed over time. The chapter highlights what contemporary philosophers have identified as the fundamental features of mechanisms and (...) mechanistic explanation. It considers some of the methodological issues that arise in neuroscience including how to integrate psychological with neural models, how to generalize findings in model organisms like the sea slug Aplysia to human learning and memory, and whether to favor top-down or bottom-up methods. Constructing multi-level mechanistic explanations involves intensive collaboration across different branches of science, and involves many challenges, both pragmatic and methodological. (shrink)

According to some philosophers, computational explanation is proprietary
to psychology—it does not belong in neuroscience. But neuroscientists routinely offer computational explanations of cognitive phenomena. In fact, computational explanation was initially imported from computability theory into the science of mind by neuroscientists, who justified this move on neurophysiological grounds. Establishing the legitimacy and importance of computational explanation in neuroscience is one thing; shedding light on it is another. I raise some philosophical questions pertaining to computational explanation (...) and outline some promising answers that are being developed by a number of authors. (shrink)

The most expressive account of explanations in neuroscience is currently the causal-mechanistic model formulated by Carl Craver. According to him, explanations in neuroscience describe mechanisms, in other words, it points out how parts organize themselves and interact to engender the phenomenon. Furthermore, neuroscience is unified as scientists from different areas that compose it work together to develop mechanisms. This model was extensively discussed in the last years and several criticisms were raised towards it. Still, it remains as (...) the soundest model for explanations in neuroscience nowadays. This paper is presented as a review of this model, as well as the critiques worked out against it and finishes with a brief consideration of the problem of explanation in neuroscience. (shrink)

Kaplan and Craver claim that all explanations in neuroscience appeal to mechanisms. They extend this view to the use of mathematical models in neuroscience and propose a constraint such models must meet in order to be explanatory. I analyze a mathematical model used to provide explanations in dynamical systems neuroscience and indicate how this explanation cannot be accommodated by the mechanist framework. I argue that this explanation is well characterized by Batterman’s account of minimal model (...) explanations and that it demonstrates how relationships between explanatory models in neuroscience and the systems they represent is more complex than has been appreciated. (shrink)

This paper examines the explanatory distinctness of wiring optimization models in neuroscience. Wiring optimization models aim to represent the organizational features of neural and brain systems as optimal (or near-optimal) solutions to wiring optimization problems. My claim is that that wiring optimization models provide design explanations. In particular, they support ideal interventions on the decision variables of the relevant design problem and assess the impact of such interventions on the viability of the target system.

The problem of fuzzy boundaries when delineating cortical areas is widely known in human brain mapping and its adjacent subdisciplines . Yet, a conceptual framework for understanding indeterminacy in neuroscience is missing, and there has been no discussion in the philosophy of neuroscience whether indeterminacy poses an issue for good neuroscientific explanations. My paper addresses both these issues by applying philosophical theories of vagueness to three levels of neuroscientific research, namely to cytoarchitectonic studies at the neuron level intra-areal (...) neuronalinteraction measured by the BOLD-signal of functional magnetic resonance imaging and inter-areal connectivity between different cortical areas. The rest of the paper explores how this framework can be extended to mechanistic explanations in neuroscience. I discuss a semantic and an ontic interpretation of vagueness in mechanistic explanations and argue how both become scientifically interesting from the perspective of a philosophy of scientific practice. (shrink)

In a recent paper, Kaplan (Synthese 183:339–373, 2011) takes up the task of extending Craver’s (Explaining the brain, 2007) mechanistic account of explanation in neuroscience to the new territory of computational neuroscience. He presents the model to mechanism mapping (3M) criterion as a condition for a model’s explanatory adequacy. This mechanistic approach is intended to replace earlier accounts which posited a level of computational analysis conceived as distinct and autonomous from underlying mechanistic details. In this paper I (...) discuss work in computational neuroscience that creates difficulties for the mechanist project. Carandini and Heeger (Nat Rev Neurosci 13:51–62, 2012) propose that many neural response properties can be understood in terms of canonical neural computations. These are “standard computational modules that apply the same fundamental operations in a variety of contexts.” Importantly, these computations can have numerous biophysical realisations, and so straightforward examination of the mechanisms underlying these computations carries little explanatory weight. Through a comparison between this modelling approach and minimal models in other branches of science, I argue that computational neuroscience frequently employs a distinct explanatory style, namely, efficient coding explanation. Such explanations cannot be assimilated into the mechanistic framework but do bear interesting similarities with evolutionary and optimality explanations elsewhere in biology. (shrink)

This paper examines the explanatory distinctness of wiring optimization models in neuroscience. Wiring optimization models aim to represent the organizational features of neural and brain systems as optimal (or near-optimal) solutions to wiring optimization problems. My claim is that that wiring optimization models provide design explanations. In particular, they support ideal interventions on the decision variables of the relevant design problem and assess the impact of such interventions on the viability of the target system.

This article examines three candidate cases of non-causal explanation in computational neuroscience. I argue that there are instances of efficient coding explanation that are strongly analogous to examples of non-causal explanation in physics and biology, as presented by Batterman, Woodward, and Lange. By integrating Lange’s and Woodward’s accounts, I offer a new way to elucidate the distinction between causal and non-causal explanation, and to address concerns about the explanatory sufficiency of non-mechanistic models in neuroscience. (...) I also use this framework to shed light on the dispute over the interpretation of dynamical models of the brain. _1_ Introduction _1.1_ Efficient coding explanation in computational neuroscience _1.2_ Defining non-causal explanation _2_ Case I: Hybrid Computation _3_ Case II: The Gabor Model Revisited _4_ Case III: A Dynamical Model of Prefrontal Cortex _4.1_ A new explanation of context-dependent computation _4.2_ Causal or non-causal? _5_ Causal and Non-causal: Does the Difference Matter? (shrink)

According to recent discussion, cross-explanatory integration in cognitive science might proceed by constraints on mechanistic and dynamic-mechanistic models provided by different research fields. However, not much attention has been given to constraints that could be provided by the study of first-person experience, which in the case of multifaceted mental phenomena are of key importance. In this paper, we fill this gap and consider the question whether information about first-person experience can constrain dynamic-mechanistic models and what the character of this relation (...) is. We discuss two cases of such explanatory models in neuroscience, namely that of migraine and of epilepsy. We argue that, in these cases, first-person insights about the target phenomena significantly contributed to explanatory models by shaping explanatory hypotheses and by indicating the dynamical properties that the explanatory models of these phenomena should account for, and thus directly constraining the space of possible explanations. (shrink)

Explanations in cognitive science and computational neuroscience rely predominantly on computational modeling. Although the scientific practice is systematic, and there is little doubt about the empirical value of numerous models, the methodological account of computational explanation is not up-to-date. The current chapter offers a systematic account of computational explanation in cognitive science and computational neuroscience within a mechanistic framework. The account is illustrated with a short case study of modeling of the mirror neuron system in terms (...) of predictive coding. (shrink)

Pseudo‐mechanistic explanations in psychology and cognitive neuroscienceThis paper focuses on the level of systems/cognitive neuroscience. It argues that the great majority of explanations in psychology and cognitive neuroscience is “pseudo‐mechanistic.” On the basis of various case studies, Hommel argues that cognitive neuroscience should move beyond what he calls an “Aristotelian phase” to become a mature “Galilean” science seeking to discover actual mechanisms of cognitive phenomena.

Causal approaches to explanation often assume that a model explains by describing features that make a difference regarding the phenomenon. Chirimuuta claims that this idea can be also used to understand non-causal explanation in computational neuroscience. She argues that mathematical principles that figure in efficient coding explanations are non-causal difference-makers. Although these principles cannot be causally altered, efficient coding models can be used to show how would the phenomenon change if the principles were modified in counterpossible situations. (...) The problem is that efficient coding models also involve difference-makers that, prima facie, cannot be characterized as non-causal in this sense. Mathematical principles always involve variables which have counterfactual relations between them. However, we cannot simply assume that these difference-makers are causal. They can also be found in paradigmatic non-causal explanations and therefore they must be characterized as non-causal in some sense. I argue that, despite appearances, Chirimuuta's view can be applied to these cases. The mentioned counterfactual relations presuppose the counterpossible conditionals that describe the modification of a relevant mathematical principle. If these conditionals are the hallmark of non-causal relations, then Chirimuuta’s criterion has the desired implication that variables in mathematical principles are non-causal difference-makers. (shrink)

The aim of this paper is to examine the usefulness of the Machamer, Darden, and Craver (2000) mechanism approach to gaining an understanding of explanation in cognitive neuroscience. We argue that although the mechanism approach can capture many aspects of explanation in cognitive neuroscience, it cannot capture everything. In particular, it cannot completely capture all aspects of the content and significance of mental representations or the evaluative features constitutive of psychopathology.

This volume offers a broad, philosophical discussion on mechanical explanations. Coverage ranges from historical approaches and general questions to physics and higher-level sciences . The contributors also consider the topics of complexity, emergence, and reduction. Mechanistic explanations detail how certain properties of a whole stem from the causal activities of its parts. This kind of explanation is in particular employed in explanatory models of the behavior of complex systems. Often used in biology and neuroscience, mechanistic explanation models (...) have been often overlooked in the philosophy of physics. The authors correct this surprising neglect. They trace these models back to their origins in physics. The papers present a comprehensive historical, methodological, and problem-oriented investigation. The contributors also investigate the conditions for using models of mechanistic explanations in physics. The last papers make the bridge from physics to economics, the theory of complex systems and computer science . This book will appeal to graduate students and researchers with an interest in the philosophy of science, scientific explanation, complex systems, models of explanation in physics higher level sciences, and causal mechanisms in science. (shrink)

Is Neo-Aristotelian hylomorphism compatible mechanistic science? In this essay I forge a rapprochement between Neo-Aristotelian hylomorphism and the "new mechanist philosophy" in biology, neuroscience, and psychology by drawing attention to their shared commitments concerning multilevel organization, mechanisms, and teleology. Significantly, the new mechanists endorse organization realism (a touchstone of hylomorphism). Similarly, Neo-Aristotelian hylomorphism is committed to the reality of mechanisms or causal powers that produce, underlie, or maintain the behavior of (i) phenomena that are constituted through the (ii) spatial, (...) temporal, and active organization of their (iii) component entities and (iv) component activities (the four hallmarks of the new mechanist philosophy). In the course of the essay I address potential disagreements between these two positions pertaining to emergence, downward causation, and teleology. I conclude that Neo-Aristotelian hylomorphism should not been seen as fundamentally opposed to mechanisms; rather, hylomorphism provides a rigorous ontological framework that complements the insights of the new mechanist philosophy of biology, neuroscience and psychology. (shrink)

I propose a cautionary assessment of the recent debate concerning the impact of the dynamical approach on philosophical accounts of scientific explanation in the cognitive sciences and, particularly, the cognitive neurosciences. I criticize the dominant mechanistic philosophy of explanation, pointing out a number of its negative consequences: In particular, that it doesn’t do justice to the field’s diversity and stage of development, and that it fosters misguided interpretations of dynamical models’ contribution. In order to support these arguments, I (...) analyze a case study in computational neuroethology and show why it should not be understood through a mechanistic lens; I specially focus on Zednik’s mechanistic interpretation of the case study. In addition, I argue for a greater appreciation of the relation between explanation and other epistemic goals in the field, as well as an increased sensitivity towards the associated contextual factors. (shrink)

A fundamental goal of research in neuroscience is to uncover the causal structure of the brain. This focus on causation makes sense, because causal information can provide explanations of brain function and identify reliable targets with which to understand cognitive function and prevent or change neurological conditions and psychiatric disorders. In this research, one of the most frequently used causal concepts is ‘mechanism’ — this is seen in the literature and language of the field, in grant and funding inquiries (...) that specify what research is supported, and in journal guidelines on which contributions are considered for publication. In these contexts, mechanisms are commonly tied to expressions of the main aims of the field and cited as the ‘fundamental’, ‘foundational’ and/or ‘basic’ unit for understanding the brain. Despite its common usage and perceived importance, mechanism is used in different ways that are rarely distinguished. Given that this concept is defined in different ways throughout the field — and that there is often no clarification of which definition is intended — there remains a marked ambiguity about the fundamental goals, orientation and principles of the field. Here we provide an overview of causation and mechanism from the perspectives of neuroscience and philosophy of science, in order to address these challenges. (shrink)

The traditional approach to explanation in cognitive neuroscience is realist about psychological constructs, and treats them as explanatory. On the “standard framework,” cognitive neuroscientists explain behavior as the result of the instantiation of psychological functions in brain activity. This strategy is questioned by results suggesting the distribution of function in the brain, the multifunctionality of individual parts of the brain, and the overlap in neural realization of purportedly distinct psychological constructs. One response to this in the field has (...) been to employ the tools of databasing and machine learning to attempt to find and quantify specific correlations between psychological kinds such as ‘memory’ or ‘attention’ (or sub-kinds thereof) and patterns of activity in the brain. I assess the status and prospects of these projects. I argue that current proponents of the project are vague about their aims, vis-à-vis the standard framework, sometimes suggesting substantiation of the framework, sometimes suggesting retaining the framework but revising the ontology of mental constructs, and sometimes suggesting abandonment of the framework. I argue that extant results from within the projects fail to substantiate the standard framework, and propose an alternative. On my view, psychological constructs should not be viewed as explanantia, but instead as heuristic concepts that help us uncover ways that behaviors can vary and the ways that the brain implements those distinctions. I then discuss the normative upshot of these views for databasing and brain mapping projects. (shrink)

This paper examines tracer techniques in neuroscience, which are used to identify neural connections in the brain and nervous system. These connections capture a type of “structural connectivity” that is expected to inform our understanding of the functional nature of these tissues. This is due to the fact that neural connectivity constrains the flow of signal propagation, which is a type of causal process in neurons. This work explores how tracers are used to identify causal information, what standards they (...) are expected to meet, the forms of causal information they provide, and how an analysis of these techniques contributes to the philosophical literature, in particular, the literature on mark transmission and mechanistic accounts of causation. (shrink)

This book examines the fundamental issues in neuroscience from methodological and philosophical perspectives. The reader will learn about the methodological difficulties connected with the use of neuroscientific experiments in philosophical argumentation and about the nature of scientific explanation in neuroscience. In addition, the book includes case studies of several issues lying at the intersection of neuroscience and philosophy, such as theory of mind, self-consciousness, self-deception, depression, and morality.

The central aim of this paper is to shed light on the nature of explanation in computational neuroscience. I argue that computational models in this domain possess explanatory force to the extent that they describe the mechanisms responsible for producing a given phenomenon—paralleling how other mechanistic models explain. Conceiving computational explanation as a species of mechanistic explanation affords an important distinction between computational models that play genuine explanatory roles and those that merely provide accurate descriptions or (...) predictions of phenomena. It also serves to clarify the pattern of model refinement and elaboration undertaken by computational neuroscientists. (shrink)

The philosophical literature on scientific explanation in neuroscience has been dominated by the idea of mechanisms. The mechanist philosophers often claim that neuroscience is in the business of finding mechanisms. This view has been challenged in numerous ways by showing that there are other successful and widespread explanatory strategies in neuroscience. However, the empirical evidence for all these claims was hitherto lacking. Empirical evidence about the pervasiveness and uses of various explanatory strategies in neuroscience is (...) particularly needed because examples and case studies that are used to illustrate philosophical claims so far tend to be hand-picked. The risk of confirmation bias is therefore considerable: when looking for white swans, all one finds is that swans are white. The more systematic quantitative and qualitative bibliometric study of a large body of relevant literature that we present in this paper can put such claims into perspective. Using bibliometric tools, we identify the typical linguistic patterns used in the alleged mechanistic, dynamical, and topological explanations in the literature, their preponderance and how they change over time. Our findings show abundant use of mechanistic language, but also the presence of a significant neuroscience literature using topological and dynamical explanatory language, which grows over time and increasingly differentiates from each other and from mechanistic explanations. (shrink)

Despite the scientific revolutions of the twentieth century, mechanistic explanations show a striking methodological continuity from early modern science to current scientific practice. They are rooted in the traditional method of analysis and synthesis, which was the background of Galileo’s resolutive-compositive method and Newton’s method of deduction from the phenomena. In early modern science as well as in current scientific practice, analysis aims at tracking back from the phenomena to the principles, i.e., from wholes to parts, and from effects to (...) causes. Vice versa, synthesis aims at explaining the phenomena from the parts and their interactions. Today, mechanistic explanations are atomistic in a generalized sense. They have in common to explain higher-level phenomena in terms of lower-level components and their causal actions or activities. In quantum physics, the lower-level components are subatomic particles, and the causes are their quantum interactions. After the quantum revolution, the approach continues to work in terms of the sum rules which hold for conserved properties of the parts and the whole. My paper focuses on the successes and limitations of this approach, with a side glance at the recent generalization of mechanistic explanations in cognitive neuroscience. (shrink)

So far, the methodological debate about neuroeconomics rarely refers to original methodological positions in the neurosciences. I confront one of the most influential ones, the constitutive explanations or mechanism approach, with methodological claims that directly relate the economic model of choice with neuronal embodiments, represented by Glimcher’s influential work. Constitutive explanations are composite and non-reductionist, therefore allow for recognizing complex causal interactions between basal neuronal phenomena and cognitive structures, also involving external symbolic media. I demonstrate the power of this methodology (...) in discussing the example of money, which Glimcher posits as one of the most important research tasks in further developing neuroeconomics. Whereas Glimcher justifies neuroscientific reductionism by presenting an evolutionary rationale for deploying formal economic models on particular modules enabling choice, the case of money reveals the need for mechanistic explanations that include a substantial role for cultural evolution, though recognizing causal interactions with basal neuronal mechanisms. (shrink)

Hodgkin and Huxley’s model of the action potential is an apparent dream case of covering‐law explanation in biology. The model includes laws of physics and chemistry that, coupled with details about antecedent and background conditions, can be used to derive features of the action potential. Hodgkin and Huxley insist that their model is not an explanation. This suggests either that subsuming a phenomenon under physical laws is insufficient to explain it or that Hodgkin and Huxley were wrong. I (...) defend Hodgkin and Huxley against Weber’s heteronomy thesis and argue that explanations are descriptions of mechanisms. †To contact the author, please write to: Department of Philosophy, Philosophy‐Neuroscience‐Psychology Program, Washington University in St. Louis, One Brookings Drive, Wilson Hall, St. Louis, MO 63130; e‐mail: [email protected]. (shrink)

In the last two decades, philosophy of neuroscience has predominantly focused on explanation. Indeed, it has been argued that mechanistic models are the standards of explanatory success in neuroscience over, among other things, topological models. However, explanatory power is only one virtue of a scientific model. Another is its predictive power. Unfortunately, the notion of prediction has received comparatively little attention in the philosophy of neuroscience, in part because predictions seem disconnected from interventions. In contrast, we (...) argue that topological predictions can and do guide interventions in science, both inside and outside of neuroscience. Topological models allow researchers to predict many phenomena, including diseases, treatment outcomes, aging, and cognition, among others. Moreover, we argue that these predictions also offer strategies for useful interventions. Topology-based predictions play this role regardless of whether they do or can receive a mechanistic interpretation. We conclude by making a case for philosophers to focus on prediction in neuroscience in addition to explanation alone. (shrink)

Proponents of mechanistic explanations have recently proclaimed that all explanations in the neurosciences appeal to mechanisms. The purpose of the paper is to critically assess this statement and to develop an integrative account that connects a large range of both mechanistic and dynamical explanations. I develop and defend four theses about the relationship between dynamical and mechanistic explanations: that dynamical explanations are structurally grounded, that they are multiply realizable, possess realizing mechanisms and provide a powerful top-down heuristic. Four examples shall (...) support my points: the harmonic oscillator, the Haken–Kelso–Bunz model of bimanual coordination, the Watt governor and the Gierer–Meinhardt model of biological pattern formation. I also develop the picture of “horizontal” and “vertical” directions of explanations to illustrate the different perspectives of the dynamical and mechanistic approach as well as their potential integration by means of intersection points. (shrink)

Until recently, the notions of function and multiple realization were supposed to save the autonomy of psychological explanations. Furthermore, the concept of supervenience presumably allows both dependence of mind on brain and non-reducibility of mind to brain, reconciling materialism with an independent explanatory role for mental and functional concepts and explanations. Eliminativism is often seen as the main or only alternative to such autonomy. It gladly accepts abandoning or thoroughly reconstructing the psychological level, and considers reduction if successful as equivalent (...) with elimination. In comparison with the philosophy of mind, the philosophy of biology has developed more subtle and complex ideas about functions, laws, and reductive explanation than the stark dichotomy of autonomy or elimination. It has been argued that biology is a patchwork of local laws, each with different explanatory interests and more or less limited scope. This points to a pluralistic, domain-specific and multi-level view of explanations in biology. Explanatory pluralism has been proposed as an alternative to eliminativism on the one hand and methodological dualism on the other hand. It holds that theories at different levels of description, like psychology and neuroscience, can co-evolve, and mutually influence each other, without the higher-level theory being replaced by, or reduced to, the lower-level one. Such ideas seem to tally with the pluralistic character of biological explanation. In biological psychology, explanatory pluralism would lead us to expect many local and non-reductive interactions between biological, neurophysiological, psychological and evolutionary explanations of mind and behavior. This idea is illustrated by an example from behavioral genetics, where genetics, physiology and psychology constitute distinct but interrelated levels of explanation. Accounting for such a complex patchwork of related explanations seems to require a more sophisticated and precise way of looking at levels than the existing ideas on (reductive and non-reductive) explanation in the philosophy of mind. (shrink)

This Research Topic aimed at deepening our understanding of the levels and explanations that are of interest for cognitive sci- entists, neuroscientists, psychologists, behavioral scientists, and philosophers of science. Indeed, contemporary developments in neuroscience and psy- chology suggest that scientists are likely to deal with a multiplicity of levels, where each of the different levels entails laws of behavior appropriate to that level (Berntson et al., 2012). Also, gathering and modeling data at the different levels of analysis is not (...) suffi- cient: the integration of information across levels of analysis is a crucial issue. Given such state of affairs, a number of interesting questions arise. How can the autonomy of explanatory levels be properly understood in behavioral explanation? Is reductionism a satis- factory strategy? How can high-level and low-level models be constrained in order to be actually explanatory of both behav- ioral and neurological or molecular evidence? What is the kind of relationship between those models? (shrink)

I show that the recent account of levels in neuroscience proposed by Craver and Bechtel is unsatisfactory since it fails to provide a plausible criterion for being at the same level and is incompatible with Craver and Bechtel’s account of downward causation. Furthermore, I argue that no distinct notion of levels is needed for analyzing explanations and causal issues in neuroscience: it is better to rely on more well-defined notions such as composition and scale. One outcome of this (...) is that apparent cases of downward causation can be analyzed away. (shrink)

Neuroscientists have in recent years turned to building models that aim to generate predictions rather than explanations. This “predictive turn” has swept across domains including law, marketing, and neuropsychiatry. Yet the norms of prediction remain undertheorized relative to those of explanation. I examine two styles of predictive modeling and show how they exemplify the normative dynamics at work in prediction. I propose an account of how predictive models, conceived of as technological devices for aiding decision-making, can come to be (...) adequate for purposes that are defined by both their guiding research questions and their larger social context of application. (shrink)

According to a growing trend in theoretical neuroscience, the human perceptual system is akin to a Bayesian machine. The aim of this article is to clearly articulate the claims that perception can be considered Bayesian inference and that the brain can be considered a Bayesian machine, some of the epistemological challenges to these claims; and some of the implications of these claims. We address two questions: (i) How are Bayesian models used in theoretical neuroscience? (ii) From the use (...) of Bayesian models in theoretical neuroscience, have we learned or can we hope to learn that perception is Bayesian inference or that the brain is a Bayesian machine? From actual practice in theoretical neuroscience, we argue for three claims. First, currently Bayesian models do not provide mechanistic explanations; instead they are useful devices for predicting and systematizing observational statements about people's performances in a variety of perceptual tasks. That is, currently we should have an instrumentalist attitude towards Bayesian models in neuroscience. Second, the inference typically drawn from Bayesian behavioural performance in a variety of perceptual tasks to underlying Bayesian mechanisms should be understood within the three-level framework laid out by David Marr ( [1982] ). Third, we can hope to learn that perception is Bayesian inference or that the brain is a Bayesian machine to the extent that Bayesian models will prove successful in yielding secure and informative predictions of both subjects' perceptual performance and features of the underlying neural mechanisms. (shrink)

How do cognitive neuroscientists explain phenomena like memory or language processing? This book examines the different kinds of experiments and manipulative research strategies involved in understanding and eventually explaining such phenomena. Against this background, it evaluates contemporary accounts of scientific explanation, specifically the mechanistic and interventionist accounts, and finds them to be crucially incomplete. Besides, mechanisms and interventions cannot actually be combined in the way usually done in the literature. This book offers solutions to both these problems based on (...) insights from experimental practice. It defends a new reading of the interventionist account, highlights the importance of non-interventionist studies for scientific inquiry, and supplies a taxonomy of experiments that makes it easy to see how the gaps in contemporary accounts of scientific explanation can be filled. The book concludes that a truly empirically adequate philosophy of science must take into account a much wider range of experimental research than has been done to date. With the taxonomy provided, this book serves a stepping-stone leading into a new era of philosophy of science—for cognitive neuroscience and beyond. (shrink)

The aim of this paper is to refer basic philosophical approaches to the problem of musical meaning and, on the other hand, to describe some examples of the research on musical meaning found in the field of cognitive neuroscience. By looking at those two approaches together it can be seen that there is still no agreement on how musical meaning should be understood, often due to several methodological problems of which the most important seem to be the possibility of (...) inter-theoretical reduction and application of an accurate theory of explanation. I am suggesting that the application of some form of the mechanistic model of explanation might be found useful for clarifying reductionism-antireductionism dispute concerning musical meaning, and more importantly, for providing some answers for the debate in music-as-language controversy. (shrink)

We argue that dynamical and mathematical models in systems and cognitive neuro- science explain (rather than redescribe) a phenomenon only if there is a plausible mapping between elements in the model and elements in the mechanism for the phe- nomenon. We demonstrate how this model-to-mechanism-mapping constraint, when satisfied, endows a model with explanatory force with respect to the phenomenon to be explained. Several paradigmatic models including the Haken-Kelso-Bunz model of bimanual coordination and the difference-of-Gaussians model of visual receptive fields are (...) explored. (shrink)

BackgroundNeuroethics describes several interdisciplinary topics exploring the application and implications of engaging neuroscience in societal contexts. To explore this topic, we present Part 3 of a four-part bibliography of neuroethics’ literature focusing on the “ethics of neuroscience.”MethodsTo complete a systematic survey of the neuroethics literature, 19 databases and 4 individual open-access journals were employed. Searches were conducted using the indexing language of the U.S. National Library of Medicine. A Python code was used to eliminate duplications in the final (...) bibliography.ResultsThis bibliography consists of 1137 papers, 56 books, and 134 book chapters published from 2002 through 2014, covering ethical issues in neuroimaging, neurogenetics, neurobiomarkers, neuro-psychopharmacology, brain stimulation, neural stem cells, neural tissue transplants, pediatric-specific issues, dual-use, and general neuroscience research issues. These works contain explanations of recent research regarding neurotechnology, while exploring ethical issues in future discoveries and use. (shrink)

Over the past century or more, cognitive neuropsychologists have discussed many of the issues raised in this volume. On the basis of this literature, we argue that autism is not a single homogeneous condition, and so can have no single cause. Instead, each of its symptoms has a cause, and the proper study of autism is the separate study of each of these symptoms and its cause. We also offer evidence to support the radical view advanced by Stoljar and Gold (...) in this volume, namely, that in explaining such syndromes the cognitive level of explanation has priority over the neuroscientific. (shrink)

In the article author analyses the extent to which it is possible to regard the Aristotelian conception of the soul as actually necessary and applicable for modern neuroscience. The framework in which this objective is going to be accomplished is provided by the idea of the coexistence of the “manifest” and “scientific” images of the world and persons, as introduced by Wilfrid Sellars. In subsequent sections, author initially formulates an answer to the questions of what it is that Aristotle (...) sought to explain with his conception of soul as formal cause, and how this notion could be portrayed in terms drawn from contemporary neurological science. It is author’s intention to show that no other concepts—be they the Cartesian “mind” or a Chalmers-style “self”—come anywhere close to matching the breadth of scope of Aristotelian “soul”. At the same time, though, analysis carried out here do not intend to undertake any kind of defence of the soul as a spiritual entity of the sort popular where religious faith is concerned, namely as an entity separable from the body, immortal, and inimitably tied to God. Then, in the second major part of the article, author analyses the extent to which the Aristotelian conception of the soul can be thought of as involving criteria of the sort that we encounter in the contemporary theory of strong emergence. According to the author the theory of strong emergence has, as a heuristic theory, quite general predictive and explanatory power as regards the field of human thought and behaviour. (shrink)

Whereas most branches of neuroscience are thought to provide mechanistic explanations, systems neuroscience is not. Two reasons are traditionally cited in support of this conclusion. First, systems neuroscientists rarely, if ever, rely on the dual strategies of decomposition and localization. Second, they typically emphasize organizational properties over the properties of individual components. In this paper, I argue that neither reason is conclusive: researchers might rely on alternative strategies for mechanism discovery, and focusing on organization is often appropriate and (...) consistent with the norms of mechanistic explanation. Thus, many explanations in systems neuroscience can also be viewed as mechanistic explanations. (shrink)

Explanatory problems in the philosophy of neuroscience are not well captured by the division between the radical and the trivial neuron doctrines. The actual problem is, instead, whether mechanistic biological explanations across different levels of description can be extended to account for psychological phenomena. According to cognitive neuroscience, some neural levels of description at least are essential for the explanation of psychological phenomena, whereas, in traditional cognitive science, psychological explanations are completely independent of the neural levels of (...) description. The challenge for cognitive neuroscience is to discover the levels of description appropriate for the neural explanation of psychological phenomena. (shrink)

The relation between mental states and brain states is important in computational neuroscience, and in psychiatry in which interventions with medication are made on brain states to alter mental states. The relation between the brain and the mind has puzzled philosophers for centuries. Here a neuroscience approach is proposed in which events at the sub-neuronal, neuronal, and neuronal network levels take place simultaneously to perform a computation that can be described at a high level as a mental state, (...) with content about the world. It is argued that as the processes at the different levels of explanation take place at the same time, they are linked by a non-causal supervenient relationship: causality can best be described in brains as operating within but not between levels. This allows the supervenient properties to be emergent, though once understood at the mechanistic levels they may seem less emergent, and expected. This mind-brain theory allows mental events to be different in kind from the mechanistic events that underlie them; but does not lead one to argue that mental events cause brain events, or vice versa: they are different levels of explanation of the operation of the computational system. This approach may provide a way of thinking about brains and minds that is different from dualism and from reductive physicalism, and which is rooted in the computational processes that are fundamental to understanding brain and mental events, and that mean that the mental and mechanistic levels are linked by the computational process being performed. Explanations at the different levels of operation may be useful in different ways. For example, if we wish to understand how arithmetic is performed in the brain, description at the mental level of the algorithm being computed will be useful. But if the brain operates to result in mental disorders, then understanding the mechanism at the neural processing level may be more useful, in for example, the treatment of psychiatric disorders. (shrink)

The relation between mental states and brain states is important in computational neuroscience, and in psychiatry in which interventions with medication are made on brain states to alter mental states. The relation between the brain and the mind has puzzled philosophers for centuries. Here a neuroscience approach is proposed in which events at the sub-neuronal, neuronal, and neuronal network levels take place simultaneously to perform a computation that can be described at a high level as a mental state, (...) with content about the world. It is argued that as the processes at the different levels of explanation take place at the same time, they are linked by a non-causal supervenient relationship: causality can best be described in brains as operating within but not between levels. This allows the supervenient (e.g., mental) properties to be emergent, though once understood at the mechanistic levels they may seem less emergent, and expected. This mind-brain theory allows mental events to be different in kind from the mechanistic events that underlie them; but does not lead one to argue that mental events cause brain events, or vice versa: they are different levels of explanation of the operation of the computational system. This approach may provide a way of thinking about brains and minds that is different from dualism and from reductive physicalism, and which is rooted in the computational processes that are fundamental to understanding brain and mental events, and that mean that the mental and mechanistic levels are linked by the computational process being performed. Explanations at the different levels of operation may be useful in different ways. For example, if we wish to understand how arithmetic is performed in the brain, description at the mental level of the algorithm being computed will be useful. But if the brain operates to result in mental disorders, then understanding the mechanism at the neural processing level may be more useful, in for example, the treatment of psychiatric disorders. (shrink)

Reductive explanations are psychologically seductive; when given two explanations, people prefer the one that refers to lower-level components or processes to account for the phenomena under consideration even when information about these lower levels is irrelevant (Hopkins, Weisberg, and Taylor 2016). Maybe individuals assume that a reductive explanation is what a scientific explanation should look like (e.g., neuroscience should explain psychology) or presume that information about lower-level components or processes is more explanatory (e.g., molecular detail explains better (...) than anatomical detail). Philosophers have been analyzing reduction for more than half a century (Hüttemann and Love 2016), but neither of these possibilities is a consensus view (even if psychologically applicable). Instead, there is widespread agreement that the landscape of reductionism is complicated, especially in biology (Brigandt and Love 2017). Increasing scrutiny of actual practices within biology and other sciences has often provoked the questions (paraphrasing Alasdair MacIntyre): Whose explanation? Which reductionism? Marie Kaiser’s book—Reductive Explanation in the Biological Sciences—is a decisive intervention into these discussions, offering a wealth of helpful distinctions and new analyses with a healthy focus on scientific practices of explanation. (shrink)