Most philosophical debate over mental causation has been concerned with reconciling commonsense intuitions that there are causal interactions between the mental and the physical with philosophical theories of the nature of the mental that seem to suggest otherwise. My concern is with a different and more practical problem. We often confront some cognitive, affective, or bodily phenomenon, and wonder about its source – its etiology or its underlying causal basis. For instance, you might wonder whether your queasiness due to something (...) you ate, or whether it is just nervousness, or whether your aunt’s memory loss is a neurological problem or a psychological response to trauma. Such questions attempt to localize the causes of a phenomenon at some level in the complex multi-level systems that we human animals are. In this paper I will attempt to tease out the sense of level implicit in such questions, and to show how it is related to current mechanistic accounts of levels. I will argue that the explanation of our practices of level attribution is deeply pragmatic. Such attributions are often attempts to locate the causes of problems, and to identify interventions that could solve those problems. (shrink)

Craver’s (2007) account of explanation in neurobiology offers one of the most sophisticated explications of the mechanism concept. This paper argues that despite groundbreaking advances in understanding mechanistic explanation, serious challenges remain. The first goal of this paper is to address the notorious problem of explanatory relevance concerning mechanistic explanation. I argue that Craver underestimates the importance of pragmatic constraints on the individuation of mechanisms, and that his suggestion for a solution of the explanatory relevance problem is therefore insufficient on (...) several counts. My second goal is to develop an alternative that explicitly incorporates both pragmatic and ontic aspects of mechanism individuation. (shrink)

Neuroscience is a multidisciplinary effort to understand the structures and functions of the brain and brain-mind relations. This effort results in an increasing amount of data, generated by sophisticated technologies. However, these data enhance our descriptive knowledge, rather than improve our understanding of brain functions. This is caused by methodological gaps both within and between subdisciplines constituting neuroscience, and the atomistic approach that limits the study of macro- and mesoscopic issues. Whole-brain measurement technologies do not resolve these issues, but rather (...) aggravate them by the complexity problem. The present article is devoted to methodological and epistemic problems that obstruct the development of human neuroscience. We neither discuss ontological questions nor review data, except when it is necessary to demonstrate a methodological issue. As regards intradisciplinary methodological problems, we concentrate on those within neurobiology and psychology. As regards interdisciplinary problems, we suggest that core disciplines of neuroscience can be integrated using systemic concepts that also entail human-environment relations. We emphasize the necessity of a meta-discussion that should entail a closer cooperation with philosophy as a discipline of systematic reflection. The atomistic reduction should be complemented by the explicit consideration of the embodiedness of the brain and the embeddedness of humans. The discussion is aimed at the development of an explicit methodology of integrative human neuroscience, which will not only link different fields and levels, but also help in understanding clinical phenomena. (shrink)

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

According to the sensorimotor account, vision does not imply theconstruction of internally generated representations of the environment, butit isthe skillful exercise of the sensorimotor contingencies obeying sense-specific laws. In this short study, I focus on the notion of “sensorimotor law” and characterize the kind of explanation providedby the sensorimotor theory as a form of covering law model. I then question the nature of such sensorimotor laws and describe them as mechanisms. I show that a mechanistic interpretation provides a better account (...) of the sensorimotor invariances, which fosters us to rebalance the explanatory burden of sensorimotor action and information. Finally, I show that the question of the roleof representations within the sensorimotor theory should be reconsidered. (shrink)

I argue for a manipulationist-mechanistic framework for content-NCC research in the case of visual consciousness (Bechtel 2008; Neisser 2012). Reference to mechanisms is common in the NCC research. Furthermore, recent developments in non-invasive brain stimulation techniques (NIBS) lend support to a manipulationist standpoint. The crucial question is to understand what is changed after manipulation of a brain mechanism. In the second part of the paper I review the literature on intentionalism, and argue that intervention on the neural mechanism is likely (...) to change the intentional content of consciousness. This urges us to shift from content-NCC to what I call “intentional mechanisms”. Such mechanisms, it is argued, should be understood as neural prerequisites of conscious visual experience. (shrink)

Philosophical theories of explanation characterize the difference between correct and incorrect explanations. While remaining neutral as to which of these ‘first-order’ theories is right, this paper asks the ‘meta-explanatory’ question: is the difference between correct and incorrect explanation real, i.e., objective or mind-independent? After offering a framework for distinguishing realist from anti-realist views, I sketch three distinct paths to explanatory anti-realism.

The “Human Brain Project” (HBP) is a large-scale European neuroscience and information communication technology (ICT) project that has been a matter of heated controversy since its inception. With its aim to simulate the entire human brain with the help of supercomputing technologies, the HBP plans to fundamentally change neuroscientific research practice, medical diagnosis, and eventually the use of computers itself. Its controversial nature and its potential impacts render the HBP a subject of crucial importance for critical studies of science and (...) society. In this paper, we provide a critical exploratory analysis of the potential mid- to long-term impacts the HBP and its ICT infrastructure could be expected to have, provided its agenda will indeed be implemented and executed to a substantive degree. We analyse how the HBP aspires to change current neuroscientific practice, what impact its novel infrastructures could have on research culture, medical practice and the use of ICT, and how, given a certain degree of successful execution of the project’s aims, potential clinical and methodological applications could even transform society beyond scientific practice. Furthermore, we sketch the possibility that research such as that projected by the HBP may eventually transform our everyday world, even beyond the scope of the HBP’s explicit agenda, and beyond the isolated ‘application’ of some novel technological device. Finally, we point towards trajectories for further philosophical, historical and sociological research on the HBP that our exploratory analysis might help to inspire. Our analysis will yield important insights regardless of the actual success of the HBP. What we drive at, for the most part, is the broader dynamics of scientific and technological development of which the HBP agenda is merely one particularly striking exemplification. (shrink)

The idea that psychiatry contains, in principle, a series of levels of explanation has been criticised both as empirically false but also, by Campbell, as unintelligible because it presupposes a discredited pre-Humean view of causation. Campbell’s criticism is based on an interventionist-inspired denial that mechanisms and rational connections underpin physical and mental causation respectively and hence underpin levels of explanation. These claims echo some superficially similar remarks in Wittgenstein’s Zettel. But attention to the context of Wittgenstein’s remarks suggests a reason (...) to reject explanatory minimalism in psychiatry and reinstate a Wittgensteinian notion of level of explanation. Only in a context broader than the one provided by interventionism is the ascription of propositional attitudes, even in the puzzling case of delusions, justified. Such a view, informed by Wittgenstein, can reconcile the idea that the ascription mental phenomena presupposes a particular level of explanation with the rejection of an a priori claim about its connection to a neurological level of explanation. (shrink)

This paper provides an analysis of Franz Brentano’s thesis that psychology employs a distinctive method, which sets it apart from physiology. The aim of the paper is two-fold: First, I situate Brentano’s thesis (and the broader metaphysical system that underwrites it) within the context of specific debates about the nature and status of psychology, arguing that we regard him as engaging in a form of boundary work. Second, I explore the relevance of Brentano’s considerations to more recent debates about autonomy (...) on the one hand and theoretical and/or methodological integration on the other. I argue that Brentano puts his finger on the idea that an integrated research process presupposes the existence of distinct methods and approaches, and that he highlights the philosophical challenge of accounting for such distinct methods. I suggest that Brentano’s ideas offer unconventional perspectives on current debates, in particular regarding first-person methods and the investigative process in cognitive science. (shrink)

As much as assumptions about mechanisms and mechanistic explanation have deeply affected psychology, they have received disproportionately little analysis in philosophy. After a historical survey of the influences of mechanistic approaches to explanation of psychological phenomena, we specify the nature of mechanisms and mechanistic explanation. Contrary to some treatments of mechanistic explanation, we maintain that explanation is an epistemic activity that involves representing and reasoning about mechanisms. We discuss the manner in which mechanistic approaches serve to bridge levels rather than (...) reduce them, as well as the different ways in which mechanisms are discovered. Finally, we offer a more detailed example of an important psychological phenomenon for which mechanistic explanation has provided the main source of scientific understanding. (shrink)

The psychiatric category of addiction has recently been broadened to include new behaviors. This has prompted critical discussion about the value of a concept that covers so many different substances and activities. Many of the debates surrounding the notion of addiction stem from different views concerning what kind of a thing addiction fundamentally is. In this essay, we put forward an account that conceptualizes different addictions as sharing a cluster of relevant properties (the syndrome) that is supported by a matrix (...) of causal mechanisms. According to this “addiction-as-a-kind” hypothesis, several different kinds of substance and behavioral addictions can be thought of as instantiations of the same thing – addiction. We show how a clearly articulated account of addiction can facilitate empirical research and the theoretical integration of different perspectives on addiction. The causal matrix approach provides a promising alternative to existing accounts of the nature of psychiatric disorders, the traditional disease model, and its competitors. It is a positive addition to discussions about diagnostic criteria, and sheds light on how psychiatric classification may be integrated with research done in other scientific fields. We argue that it also provides a plausible approach to understanding comorbidity. (shrink)

Modeling mechanisms is central to the biological sciences – for purposes of explanation, prediction, extrapolation, and manipulation. A closer look at the philosophical literature reveals that mechanisms are predominantly modeled in a purely qualitative way. That is, mechanistic models are conceived of as representing how certain entities and activities are spatially and temporally organized so that they bring about the behavior of the mechanism in question. Although this adequately characterizes how mechanisms are represented in biology textbooks, contemporary biological research practice (...) shows the need for quantitative, probabilistic models of mechanisms, too. In this paper we argue that the formal framework of causal graph theory is well-suited to provide us with models of biological mechanisms that incorporate quantitative and probabilistic information. On the ba-sis of an example from contemporary biological practice, namely feedback regulation of fatty acid biosynthesis in Brassica napus, we show that causal graph theoretical models can account for feedback as well as for the multi-level character of mechanisms. However, we do not claim that causal graph theoretical representations of mechanisms are advantageous in all respects and should replace common qualitative models. Rather, we endorse the more balanced view that causal graph theoretical models of mechanisms are useful for some purposes, while being insufficient for others. (shrink)

The standard objection against naturalised epistemology is that it cannot account for normativity in epistemology (Putnam 1982; Kim 1988). There are different ways to deal with it. One of the obvious ways is to say that the objection misses the point: It is not a bug; it is a feature, as there is nothing interesting in normative principles in epistemology. Normative epistemology deals with norms but they are of no use in prac-tice. They are far too general to be guiding (...) principles of research, up to the point that they even seem vacuous (see Knowles 2003). In this chapter, my strategy will be different and more in spirit of the founding father of naturalized epistemology, Quine, though not faithful to the letter. I focus on methodological prescriptions supplied by cogni-tive science in re-engineering of cognitive architectures. Engineering norms based on mechanism design weren’t treated as seriously as they should in epistemology, and that is why I will develop a sketch of a framework for researching them, starting from analysing cognitive sci-ence as engineering in section 3, then showing functional normativity in section 4, to eventually present functional engineering models of cogni-tive mechanisms as normative in section 5. Yet before showing the kind of engineering normativity specific for these prescriptions, it is worth-while to review briefly the role of normative methodology and the levels of norm complexity in it, and show how it follows Quine’s steps. (shrink)

In this paper, the role of the environment and physical embodiment of computational systems for explanatory purposes will be analyzed. In particular, the focus will be on cognitive computational systems, understood in terms of mechanisms that manipulate semantic information. It will be argued that the role of the environment has long been appreciated, in particular in the work of Herbert A. Simon, which has inspired the mechanistic view on explanation. From Simon’s perspective, the embodied view on cognition seems natural but (...) it is nowhere near as critical as its proponents suggest. The only point of difference between Simon and embodied cognition is the significance of body-based off-line cognition; however, it will be argued that it is notoriously over-appreciated in the current debate. The new mechanistic view on explanation suggests that even if it is critical to situate a mechanism in its environment and study its physical composition, or realization, it is also stressed that not all detail counts, and that some bodily features of cognitive systems should be left out from explanations. (shrink)

In this paper I consider possible causation, specifically, would-cause counterfactuals of the form ‘had an event of kind A occurred, it would have caused an event of kind B’. I outline some difficulties for the Lewis program for understanding would-cause counterfactuals, and canvass an alternative. I then spell out a view on their significance, in relation to (i) absence causation, where claims such as ‘A’s not occurring caused B’s not occurring’ seem to make sense when understood in terms of the (...) would-cause counterfactual ‘had an event of kind A occurred, it would have caused an event of kind B’; (ii) contrastive causal explanation, where to explain why E rather than E* occurred we might appeal to the causal history of E and the counterfactual causal history of E*, an approach which appeals directly to would-cause counterfactuals ‘had an event of kind C* occurred, it would have caused an event of kind E*’; and (iii) dispositions, where the claim ‘the glass is fragile’ clearly has some connection or other with would-cause counterfactuals such as ‘were the glass to be struck, the striking would cause the glass to break’. (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)

This paper discusses three shortcomings of the current state of the debate regarding historical evidence against scientific realism. Attending to these issues will direct the debate away from over-generalising wholesale arguments.

The philosophical conception of mechanistic explanation is grounded on a limited number of canonical examples. These examples provide an overly narrow view of contemporary scientific practice, because they do not reflect the extent to which the heuristic strategies and descriptive practices that contribute to mechanistic explanation have evolved beyond the well-known methods of decomposition, localization, and pictorial representation. Recent examples from evolutionary robotics and network approaches to biology and neuroscience demonstrate the increasingly important role played by computer simulations and mathematical (...) representations in the epistemic practices of mechanism discovery and mechanism description. These examples also indicate that the scope of mechanistic explanation must be re-examined: With new and increasingly powerful methods of discovery and description comes the possibility of describing mechanisms far more complex than traditionally assumed. (shrink)

Recently, Piccinini and Craver have stated three theses concerning the relations between functional analysis and mechanistic explanation in cognitive sciences: No Distinctness: functional analysis and mechanistic explanation are explanations of the same kind; Integration: functional analysis is a kind of mechanistic explanation; and Subordination: functional analyses are unsatisfactory sketches of mechanisms. In this paper, I argue, first, that functional analysis and mechanistic explanations are sub-kinds of explanation by scientific (idealized) models. From that point of view, we must take into account (...) the tradeoff between the representational/explanatory goals of generality and precision that govern the practice of model-building. In some modeling scenarios, it is rational to maximize explanatory generality at the expense of mechanistic precision. This tradeoff allows me to put forward a problem for the mechanist position. If mechanistic modeling endorses generality as a valuable goal, then Subordination should be rejected. If mechanists reject generality as a goal, then Integration is false. I suggest that mechanists should accept that functional analysis can offer acceptable explanations of cognitive phenomena. (shrink)

The naturalism versus interpretivism debate the in philosophy of social science is traditionally framed as the question of whether social science should attempt to emulate the methods of natural science. I show that this manner of formulating the issue is problematic insofar as it presupposes an implausibly strong unity of method among the natural sciences. I propose instead that what is at stake in this debate is the feasibility and desirability of what I call the Enlightenment ideal of social science. (...) I argue that this characterization of the issue is preferable, since it highlights the central disagreement between advocates of naturalism and interpretivism, makes connections with recent work on the topics of causal inference and social epistemology, while avoiding unfruitful comparisons between the social and natural sciences. (shrink)

In recent years, a number of research projects have been proposed whose goal is to build large-scale simulations of brain mechanisms at unprecedented levels of biological accuracy. Here it is argued that the roles these simulations are expected to play in neuroscientific research go beyond the “synthetic method” extensively adopted in Artificial Intelligence and biorobotics. In addition we show that, over and above the common goal of simulating brain mechanisms, these projects pursue various modelling ambitions that can be sharply distinguished (...) from one another, and that correspond to conceptually different interpretations of the notion of “biological accuracy”. They include the ambition to reach extremely deep levels in the mechanistic decomposition hierarchy, to simulate networks composed of extremely large numbers of neural units, to build systems able to generate rich behavioural repertoires, to simulate “complex” neuron models, to implement the “best” theories available on brain structure and function. Some questions will be raised concerning the significance of each of these modelling ambitions with respect to the various roles played by simulations in the study of the brain. (shrink)

The paper argues that a functional reduction of ordinary psychology to neuropsychology is possible by means of constructing fine-grained functional, mental sub-types that are coextensive with neuropsychological types. We establish this claim by means of considering as examples the cases of the disconnection syndrome and schizophrenia. We point out that the result is a conservative reduction, vindicating the scientific quality of the mental types of ordinary psychology by systematically linking them with neuroscience. That procedure of conservative reduction by means of (...) functional sub-types is in principle repeatable down to molecular neuroscience. (shrink)