This article, the first of a two-part essay, presents an account of Aristotelian hylomorphic animalism that engages with recent work on neuroscience and philosophy of mind. I show that Aristotelian hylomorphic animalism is compatible with the new mechanist approach to neuroscience and psychology, but that it is incompatible with strong emergentism in the philosophy of mind. I begin with the basic claims of Aristotelian hylomorphic animalism and focus on its understanding of psychological powers embodied in the nervous system. (...) Next, I introduce the new mechanist approach to neuroscience and psychology and illustrate how it can enrich the more abstract ontological framework of Aristotelian hylomorphic animalism. In the third section of this article I establish in detail the many ways Aristotelian hylomorphic animalism is incompatible with strong emergentism in the philosophy of mind. Based on these fundamental differences I show why a criticism leveled against emergentism by the new mechanist philosophy does not hamper my proposed rapprochement between hylomorphism and the new mechanist philosophy. This conclusion, however, leaves untouched the problem I address in the second article, namely, is the new mechanist philosophy compatible with Aristotelian philosophical anthropology’s contention that intellectual operations are immaterial and interact with the psychosomatic operations of the rational animal? (shrink)

Philosophers of neuroscience have traditionally described interfield integration using reduction models. Such models describe formal inferential relations between theories at different levels. I argue against reduction and for a mechanistic model of interfield integration. According to the mechanistic model, different fields integrate their research by adding constraints on a multilevel description of a mechanism. Mechanistic integration may occur at a given level or in the effort to build a theory that oscillates among several levels. I develop this alternative (...) model using a putative exemplar of reduction in contemporary neuroscience: the relationship between the psychological phenomena of learning and memory and the electrophysiological phenomenon known as Long-Term Potentiation. A new look at this historical episode reveals the relative virtues of the mechanistic model over reduction as an account of interfield integration. (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)

This paper offers a novel view of unity in neuroscience. I set out by discussing problems with the classical account of unity-by-reduction, due to Oppenheim and Putnam. That view relies on a strong notion of levels, which has substantial problems. A more recent alternative, the mechanistic “mosaic” view due to Craver, does not have such problems. But I argue that the mosaic ideal of unity is too minimal, and we should, if possible, aspire for more. Relying on a number (...) of recent works in theoretical neuroscience—network motifs, canonical neural computations and design-principles—I then present my alternative: a “flat” view of unity, i.e. one that is not based on levels. Instead, it treats unity as attained via the identification of recurrent explanatory patterns, under which a range of neuroscientific phenomena are subsumed. I develop this view by recourse to a causal conception of explanation, and distinguish it from Kitcher’s view of explanatory unification and related ideas. Such a view of unity is suitably ambitious, I suggest, and has empirical plausibility. It is fit to serve as an appropriate working hypothesis for 21st century neuroscience. (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)

In this paper, I evaluate recently defended mechanistic accounts of the unity of neuroscience from a metaphysical point of view. Considering the mechanistic framework in general , I argue that explanations of this kind are essentially reductive . The reductive character of mechanistic explanations provides a sufficiency criterion, according to which the mechanism underlying a certain phenomenon is sufficient for the latter. Thus, the concept of supervenience can be used in order to describe the relation between mechanisms and (...) phenomena . Against this background, I show that the mechanistic framework is subject to the causal exclusion problem and faces the classical metaphysical options when it comes to the relations obtaining between different levels of mechanisms . Finally, an attempt to improve the metaphysics of mechanisms is made and further difficulties are pointed out. (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)

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)

Previously published studies have reported that 150 min of short-term monocular deprivation temporarily changes perceptual eye dominance. However, the possible mechanisms underlying monocular deprivation-induced perceptual eye dominance plasticity remain unclear. Using a binocular phase and contrast co-measurement task and a multi-pathway contrast-gain control model, we studied the effect of 150 min of monocular pattern deprivation in normal adult subjects. The perceived phase and contrast varied significantly with the interocular contrast ratio, and after MPD, the patched eye became dominant. Most importantly, (...) we focused on the potential mechanisms of the deprivation effect. The data of an averaged subject was best fitted by a model, which assumed a monocular signal enhancement of the PE after the MPD. The present findings might have important implications for investigations of binocular vision in both normal and amblyopic populations. (shrink)

There have been recent disagreements in the philosophy of neuroscience regarding which sorts of scientific models provide mechanistic explanations, and which do not. These disagreements often hinge on two commonly adopted, but conflicting, ways of understanding mechanistic explanations: what I call the “representation-as” account, and the “representation-of” account. In this paper, I argue that neither account does justice to neuroscientific practice. In their place, I offer a new alternative that can defuse some of these disagreements. I argue that individual (...) models do not provide mechanistic explanations by themselves. Instead, individual models are always used to complement a huge body of background information and pre-existing models about the target system. With this in mind, I argue that mechanistic explanations are distributed across sets of different, and sometimes contradictory, scientific models. Each of these models contributes limited, but essential, information to the same mechanistic explanation, but none can be considered a mechanistic explanation in isolation of the others. (shrink)

We present a theoretical view of the cellular foundations for network-level processes involved in producing our conscious experience. Inputs to apical synapses in layer 1 of a large subset of neocortical cells are summed at an integration zone near the top of their apical trunk. These inputs come from diverse sources and provide a context within which the transmission of information abstracted from sensory input to their basal and perisomatic synapses can be amplified when relevant. We argue that apical amplification (...) enables conscious perceptual experience and makes it more flexible, and thus more adaptive, by being sensitive to context. Apical amplification provides a possible mechanism for recurrent processing theory that avoids strong loops. It makes the broadcasting hypothesized by global neuronal workspace theories feasible while preserving the distinct contributions of the individual cells receiving the broadcast. It also provides mechanisms that contribute to the holistic aspects of integrated information theory. As apical amplification is highly dependent on cholinergic, aminergic, and other neuromodulators, it relates the specific contents of conscious experience to global mental states and to fluctuations in arousal when awake. We conclude that apical dendrites provide a cellular mechanism for the context-sensitive selective amplification that is a cardinal prerequisite of conscious perception. (shrink)

Descriptive accounts of the nature of explanation in neuroscience and the global goals of such explanation have recently proliferated in the philosophy of neuroscience and with them new understandings of the experimental practices of neuroscientists have emerged. In this paper, I consider two models of such practices; one that takes them to be reductive; another that takes them to be integrative. I investigate those areas of the neuroscience of learning and memory from which the examples used to (...) substantiate these models are culled, and argue that the multiplicity of experimental protocols used in these research areas presents specific challenges for both models. In my view, these challenges have been overlooked largely because philosophers have hitherto failed to pay sufficient attention to fundamental features of experimental practice. I demonstrate that when we do pay attention to such features, evidence for reduction and integrative unity in neuroscience is simply not borne out. I end by suggesting some new directions for the philosophy of neuroscience that pertain to taking a closer look at the nature of neuroscientific experiments. (shrink)

Decision-making is an advanced cognitive function that promotes information processes in complex motor situations. In recent years, many neuroimaging studies have assessed the effects of long-term motor training on athletes’ brain activity while performing decision-making tasks, but the findings have been inconsistent and a large amount of data has not been quantitatively summarized until now. Therefore, this study aimed to identify the neural mechanism of long-term motor training affecting the decision-making function of athletes by using activation likelihood estimation meta-analysis. (...) Altogether, 10 studies were included and comprised a total of 350 people. The ALE meta-analysis showed that more brain regions were activated for novices including the bilateral occipital lobe, left posterior cerebellar lobe, and left middle temporal gyrus in decision-making tasks compared to motor experts. Our results possibly suggested the association between long-term motor training and neural efficiency in athletes, which provided a reference for further understanding the neural mechanisms of motor decision-making. (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.

I recommend replacing Piccinini's elaborate metaphysics that grounds his approach in Neurocognitive Mechanisms with metascience. Reconceived as metascience, Piccinini's discussion of numerous case studies from recent neuroscience in his book's final chapters makes a strong case for his proposal that current neuroscience trades in neural representations and a special kind of computation over them. But I contrast this account with what a metascience focused on recent developments in 'molecular and cellular cognition' reveals, namely an account that no longer (...) has use for 'levels', a notion that has long infected new mechanism and remains prominent in Piccinini's latest contribution. (shrink)

The use of neuroscientific evidence in criminal trials has been steadily increasing. Despite progress made in recent decades in understanding the mechanisms of psychological and behavioral functioning, neuroscience is still in an early stage of development and its potential for influencing legal decision-making is highly contentious. Scholars disagree about whether or how neuroscientific evidence might impact prescriptions of criminal culpability, particularly in instances in which evidence of an accused’s history of mental illness or brain abnormality is offered to support (...) a plea of not criminally responsible. In the context of these debates, philosophers and legal scholars have identified numerous problems with admitting neuroscientific evidence in legal contexts. To date, however, less has been said about the challenges of evaluating the evidence upon which integrative mechanistic explanations that bring together evidence from different areas of neuroscience are based. As we explain, current criteria for evaluating such evidence to determine its admissibility in legal contexts are inadequate. Appealing to literature in the philosophy of scientific experimentation and theoretical work in the social, cognitive and behavioral sciences, we lay the groundwork for reforming these criteria and identify some of the implications of modifying them. (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)

Conscience is often referred to yet not understood. This text develops a theory of cognition around a model of conscience, the ACTWith model. It represents a synthesis of results from contemporary neuroscience with traditional philosophy, building from Jamesian insights into the emergence of the self to narrative identity, all the while motivated by a single mechanism as represented in the ACTWith model. Emphasis is placed on clarifying historical expressions and demonstrations of conscience - Socrates, Heidegger, Kant, M.L. King (...) - in light of the ACTWith model, while at once turning these resources to developing the basic architecture. In the end, this text aims to enrich moral theory by improving our understanding of moral cognition, while at once providing a useful tool in everyday moral practice and self-development. (shrink)

Autonomist accounts of cognitive science suggest that cognitive model building and theory construction (can or should) proceed independently of findings in neuroscience. Common functionalist justifications of autonomy rely on there being relatively few constraints between neural structure and cognitive function (e.g., Weiskopf, 2011). In contrast, an integrative mechanistic perspective stresses the mutual constraining of structure and function (e.g., Piccinini & Craver, 2011; Povich, 2015). In this paper, I show how model-based cognitive neuroscience (MBCN) epitomizes the integrative mechanistic perspective (...) and concentrates the most revolutionary elements of the cognitive neuroscience revolution (Boone & Piccinini, 2016). I also show how the prominent subset account of functional realization supports the integrative mechanistic perspective I take on MBCN and use it to clarify the intralevel and interlevel components of integration. (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 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)

This volume brings together new papers advancing contemporary debates in foundational, conceptual, and methodological issues in cognitive neuroscience. The different perspectives presented in each chapter have previously been discussed between the authors, as the volume builds on the experience of Neural Mechanisms Online – webinar series on the philosophy of neuroscience organized by the editors of this volume. The contributed chapters pertain to five core areas in current philosophy of neuroscience. It surveys the novel forms of explanation (...) developed in cognitive neuroscience, and looks at new concepts, methods and techniques used in the field. The book also highlights the metaphysical challenges raised by recent neuroscience and demonstrates the relation between neuroscience and mechanistic philosophy. Finally, the book dives into the issue of neural computations and representations. Assembling contributions from leading philosophers of neuroscience, this work draws upon the expertise of both established scholars and promising early career researchers. (shrink)

We sketch a framework for building a unified science of cognition. This unification is achieved by showing how functional analyses of cognitive capacities can be integrated with the multilevel mechanistic explanations of neural systems. The core idea is that functional analyses are sketches of mechanisms , in which some structural aspects of a mechanistic explanation are omitted. Once the missing aspects are filled in, a functional analysis turns into a full-blown mechanistic explanation. By this process, functional analyses are seamlessly integrated (...) with multilevel mechanistic explanations. (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)

I argue that informational models of consciousness, including those proposed by the Integrated Information Theory, don’t presuppose or entail any particular view about the physical or metaphysical nature of consciousness. Such models only tell us how certain properties of consciousness can be mathematically described, thus providing a quantitative characterization of the phenomenon of consciousness that may contribute to the development of new methods of assessment and guide the explanatory project by supplying additional constraints on theoretical proposals. While informational models are (...) in principle compatible with multiple physical interpretations, a preliminary argument in favour of the continuity between informational approaches and the mechanistic project in neuroscience can be made based on historical precedents. (shrink)

Recent models of psychopathology and psychotherapy highlight the importance of interpersonal factors. The current review offers a biological perspective on these interpersonal processes by examining inter-brain synchrony—the coupling of brain activity between people interacting with one another. High inter-brain synchrony is associated with better relationships in therapy and in daily life, while deficits in the ability to achieve inter-brain synchrony are associated with a variety of psychological and developmental disorders. The review suggests that therapy improves patients’ ability to achieve such (...) synchrony through inter-brain plasticity—a process by which recurring exposure to high inter-brain synchrony leads to lasting change in a person’s overall ability to synchronize. Therapeutic sessions provide repeated situations with high inter-brain synchrony. This can lead to a long-term increase in the ability to synchronize, first with the therapist, then generalized to other interpersonal relationships, ultimately leading to symptom reduction. The proposed inter-brain plasticity model offers a novel biological framework for understanding relational change in psychotherapy and its links to various forms of psychopathology and provides testable hypotheses for future research. Understanding this mechanism may help improve existing psychotherapy methods and develop new ones. (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)

Carl Craver’s recent book offers an account of the explanatory and theoretical structure of neuroscience. It depicts it as centered around the idea of achieving mechanistic understanding, i.e., obtaining knowledge of how a set of underlying components interacts to produce a given function of the brain. Its core account of mechanistic explanation and relevance is causal-manipulationist in spirit, and offers substantial insight into casual explanation in brain science and the associated notion of levels of explanation. However, the focus on (...) mechanistic explanation leaves some open questions regarding the role of computation and cognition. (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)

I hypothesize here that the ability of probiotics to synthesize neuroactive compounds provides a unifying microbial endocrinology‐based mechanism to explain the hitherto incompletely understood action of commensal microbiota that affect the host's gastrointestinal and psychological health. Once ingested, probiotics enter an interactive environment encompassing microbiological, immunological, and neurophysiological components. By utilizing a trans‐disciplinary framework known as microbial endocrinology, mechanisms that would otherwise not be considered become apparent since any candidate would need to be shared among all three components. The (...) range of neurochemicals produced by probiotics includes neurochemicals for which receptor‐based targets on immune and neuronal elements (intestinal and extra‐intestinal) have been well characterized. Production of neurochemicals by probiotics therefore allows for their consideration as delivery vehicles for neuroactive compounds. This unifying microbial endocrinology‐based hypothesis, which may facilitate the selection and design of probiotics for clinical use, also highlights the largely unrecognized role of neuroscience in understanding how microbes may influence health.Editor's suggested further reading in BioEssays Harvesting the biological potential of the human gut microbiome Abstract. (shrink)

In the last 20 years or so, since the publication of a seminal paper by Watts and Strogatz :440–442, 1998), an interest in topological explanations has spread like a wild fire over many areas of science, e.g. ecology, evolutionary biology, medicine, and cognitive neuroscience. The topological approach is still very young by all standards, and even within special sciences it still doesn’t have a single methodological programme that is applicable across all areas of science. That is why this special (...) issue is important as a first systematic philosophical study of topological explanations and their relation to a well understood and widespread explanatory strategy, such as mechanistic one. (shrink)

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)

Human beings enjoy a wide range of mental capacities: we learn, remember, and think. And we have these capacities largely in virtue of the brain. But how does the brain work? Carl Craver’s Explaining the Brain provides a careful, detailed examination of the explanatory framework employed in contemporary neuroscience and how this framework revolves around the notion of a mechanism. Moreover, he argues that an adequate appreciation of this framework requires reconsidering a number of issues in the philosophy (...) of science. (shrink)

This paper discusses the role of levels and level-bound theoretical terms in neurobiological explanations under the presupposition of a regularity theory of constitution. After presenting the definitions for the constitution relation and the notion of a mechanistic level in the sense of the regularity theory, the paper develops a set of inference rules that allow to determine whether two mechanisms referred to by one or more accepted explanations belong to the same level, or to different levels. The rules are characterized (...) as underlying level distinctions in neuroscience research. (shrink)

Long-Term Potentiation (LTP) is a kind of synaptic plasticity that many contemporary neuroscientists believe is a component in mechanisms of memory. This essay describes the discovery of LTP and the development of the LTP research program. The story begins in the 1950's with the discovery of synaptic plasticity in the hippocampus (a medial temporal lobe structure now associated with memory), and it ends in 1973 with the publication of three papers sketching the future course of the LTP research program. The (...) making of LTP was a protracted affair. Hippocampal synaptic plasticity was initially encountered as an experimental tool, then reported as a curiosity, and finally included in the ontic store of the neurosciences. Early researchers were not investigating the hippocampus in search of a memory mechanism; rather, they saw the hippocampus as a useful experimental model or as a structure implicated in the etiology of epilepsy. The link between hippocampal synaptic plasticity and learning or memory was a separate conceptual achievement. That link was formulated in at least three different ways at different times: reductively (claiming that plasticity is identical to learning), analogically (claiming that plasticity is an example or model of learning), and mechanistically (claiming that plasticity is a component in learning or memory mechanisms). The hypothesized link with learning or memory, coupled with developments in experimental techniques and preparations, shaped how researchers understood LTP itself. By 1973, the mechanistic formulation of the link between LTP and memory provided an abstract framework around which findings from multiple perspectives could be integrated into a multifield research program. (shrink)

Cognitive neuroscience has come to be viewed as the flagship of the cognitive sciences and is transforming our understanding of the nature of mind. In this paper we survey several research fields in cognitive neuroscience (lateralization, neuroeconomics, and cognitive control) and note that they are making rapid progress on fundamental issues. Lateralization research is developing a comparative framework for evolutionary analysis, and is identifying individual- and population-level factors that favor brain asymmetries. Neuroeconomics is creating a research framework for (...) studying valuation mechanisms in the brain that allows investigation of more complex phenomena than traditional reward paradigms. And cognitive control research has cast light on the mechanisms of task learning and top-down control that enable fluid goal-directed behavior in complex situations. Reasons for the success of cognitive neuroscience are varied: technical advances (e.g., in molecular genetics and imaging) are part of the story, but the more important factors have to do with the power of integrating a bottom-up mechanistic approach with larger functional and evolutionary perspectives. The incorporation of a bottom-up component differentiates cognitive neuroscience from the multidisciplinary ambitions of classical cognitive science, which suffered from having limited access to mechanisms. (shrink)

A strong case has been made for the role and value of mechanistic reasoning in process-oriented sciences, such as molecular biology and neuroscience. This paper shifts focus to assess the role of mechanistic reasoning in an area where it is neither obvious nor expected: population genetics. Population geneticists abstract away from the causal-mechanical details of individual organisms and, instead, use mathematics to describe population-level, statistical phenomena. This paper, first, develops a framework for the identification of mechanistic reasoning where it (...) is not obvious: mathematical and mechanistic styles of scientific reasoning. Second, it applies this framework to demonstrate that both styles are integrated in modern investigations of evolutionary biology. Characteristic of the former, applied population genetic techniques provide statistical evidence for associations between genotype, phenotype, and fitness. Characteristic of the latter, experimental interventions provide causal-mechanical evidence for associations between the very same relationships, often in the same model organisms. The upshot is a richer perspective of how evolutionary biologists build evidence for hypotheses regarding adaptive evolution and a general framework for assessing the scope of mechanistic reasoning across the sciences. (shrink)

This chapter argues that much discussion between philosophers and neuroscientists is infected by philosophical assumptions about the nature of reduction. Instead we should pursue an unbiased examination of the methods used throughout relevant areas of neuroscience. The chapter focuses on reductionist work in the neurobiological discipline of molecular and cellular cognition. It is argued that reduction is a matter of causal intervention into low level mechanisms, and tracking of the effects of these interventions through levels. When interventions provide evidence (...) that activity in the proposed reductive mechanism co-varies reliably with activity in the target property, such that appealing to higher-level mechanisms will not add any extra explanatory power, reduction can succeed. This is explicitly contrasted with functionalisation and a posteriori approaches to reduction. (shrink)

Wegner's monograph presents the view that conscious will is a feeling that we experience when we perform an action through a mechanistic process of the brain, rather than a mental force that causes the action. The view is supported by several lines of evidence in which conscious will is dissociated from the actual performance of voluntary movements, as in automatism. The book further extends an insightful analysis of the mental system behind the illusion of conscious will and inspires neuroscientists to (...) reflect on its neural substrates. (shrink)

In Spinoza’s philosophy affects illustrate the way human beings interact with each other and the world, where the necessary meetings with other particular things define their being and its expressions. Most human beings don’t know themselves, are not conscious of their affects and, even less, do they know what the affects of others are. Although, they are by their definition as particular things obliged to exist in society and create a minimum of consensus. According to Spinoza, this consensus is built (...) upon the biological substrate defined by human body’s physiology, through the mechanism of imitation and is supported by empathy. Leading researchers in affective neuroscience argue for a theory of embodied cognition and recent research in neurosciences attributes human capacity for empathy to mirror neurons, recognising in Spinoza’s texts the philosophical roots of current scientific thinking on body, mind and feeling. Keeping in mind the debate concerning how different levels of explanation can be related to each other or how different disciplines can form the context for interpreting neuroscience’s data, we attempt to promote an implicit dialogue between Spinoza’s psychological theory and the neuroscientific findings, supporting that is legitimate and necessary to examine these questions from the point of view of philosophy and formulate new research questions that can promote further theoretical and empirical study of the complex phenomena concerning human nature and society. (shrink)

Accounts of mechanistic explanation, especially as applied to biology and sometimes going under the heading of “new mechanism,” provided an attractive alternative to nomological accounts that preceded them. These accounts were motivated by selected examples, drawn primarily from cell and molecular biology and neuroscience. However, the range of examples that scientists take to be mechanistic explanations is far broader. We focus on examples that differ from those traditionally recruited by Mechanists. Our contention is that attention to additional examples (...) will lead to a richer conception of mechanistic explanation, prompting a shift from what we refer to as Mechanism 1.0 to Mechanism 2.0. (shrink)

An increasing number of philosophers have promoted the idea that mechanism provides a fruitful framework for thinking about the explanatory contributions of computational approaches in cognitive neuroscience. For instance, Piccinini and Bahar :453–488, 2013) have recently argued that neural computation constitutes a sui generis category of physical computation which can play a genuine explanatory role in the context of investigating neural and cognitive processes. The core of their proposal is to conceive of computational explanations in cognitive neuroscience (...) as a subspecies of mechanistic explanations. This paper identifies several challenges facing their mechanistic account and sketches an alternative way of thinking about the epistemic roles of computational approaches used in the study of brain and cognition. Drawing on examples from both low-level and systems-level computational neuroscience, I argue that at least some computational explanations of neural and cognitive processes are partially independent from mechanistic constraints. (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)

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)