An adequate understanding of the ubiquitous practice of mechanistic explanation requires an account of what Craver termed “constitutive relevance.” Entities or activities are constitutively relevant to a phenomenon when they are parts of the mechanism responsible for that phenomenon. Craver’s mutual manipulability account extended Woodward’s account of manipulationist counterfactuals to analyze how interlevel experiments establish constitutive relevance. Critics of MM argue that applying Woodward’s account to this philosophical problem conflates causation and constitution, thus rendering the account incoherent. These criticisms, we (...) argue, arise from failing to distinguish the semantic, epistemic, and metaphysical aspects of the problem of constitutive relevance. In distinguishing these aspects of the problem and responding to these critics accordingly, we amend MM into a refined epistemic criterion, the “matched interlevel experiments” account. Further, we explain how this epistemological thesis is grounded in the plausible metaphysical thesis that constitutive relevance is causal betweenness. (shrink)

In this paper, we argue that several recent ‘wide’ perspectives on cognition (embodied, embedded, extended, enactive, and distributed) are only partially relevant to the study of cognition. While these wide accounts override traditional methodological individualism, the study of cognition has already progressed beyond these proposed perspectives towards building integrated explanations of the mechanisms involved, including not only internal submechanisms but also interactions with others, groups, cognitive artifacts, and their environment. The claim is substantiated with reference to recent developments in the (...) study of “mindreading” and debates on emotions. We claim that the current practice in cognitive (neuro)science has undergone, in effect, a silent mechanistic revolution, and has turned from initial binary oppositions and abstract proposals towards the integration of wide perspectives with the rest of the cognitive (neuro)sciences. (shrink)

The semantic view of computation is the claim that semantic properties play an essential role in the individuation of physical computing systems such as laptops and brains. The main argument for the semantic view rests on the fact that some physical systems simultaneously implement different automata at the same time, in the same space, and even in the very same physical properties. Recently, several authors have challenged this argument. They accept the premise of simultaneous implementation but reject the semantic conclusion. (...) In this paper, I aim to explicate the semantic view and to address these objections. I first characterize the semantic view and distinguish it from other, closely related views. Then, I contend that the master argument for the semantic view survives the counter-arguments against it. One counter-argument is that computational individuation is not forced to choose between the implemented automata but rather always picks out a more basic computational structure. My response is that this move might undermine the notion of computational equivalence. Another counter-argument is that while computational individuation is forced to rely on extrinsic features, these features need not be semantic. My reply is that the semantic view better accounts for these extrinsic features than the proposed non-semantic alternatives. (shrink)

ABSTRACT Shagrir and Sprevak explore the apparent necessity of representation for the individuation of digits in computational systems.1 1 I will first offer a response to Sprevak’s argument that does not mention Shagrir’s original formulation, which was more complex. I then extend my initial response to cover Shagrir’s argument, thus demonstrating that it is possible to individuate digits in non-representational computing mechanisms. I also consider the implications that the non-representational individuation of digits would have for the broader theory of computing (...) mechanisms. 1 The Received View: No Computation without Representation 2 Computing Mechanisms and Functional Individuation 3 Against Computational Externalism 4 Implications for the Mechanistic Account. (shrink)

Various analytical tools originally developed for theories of mechanistic explanation have recently been imported into the ongoing debate on the hypothesis of extended cognition. One such tool that appears particularly relevant to that debate is Craver’s mutual manipulability account of constitution, most of all because it promises to settle the debate on experimental grounds. This paper investigates whether it is possible to deliver on that promise. We first find that, far from grounding an experimental evaluation of HEC, MM is conceptually (...) incompatible with both internalist and externalist accounts of cognition. Next, we propose a suitable modification of MM, MM*, but it turns out that MM* presupposes rather than produces clarity on the extension of cognition. Moreover, subject to MM* the inference to constitution is radically empirically underdetermined. Finally, we argue that our results can be generalized and conclude that, for principled reasons, it is impossible to experimentally determine whether cognitive processes have extracerebral constituents. Determining the extension of cognition is an inherently pragmatic matter. (shrink)

The assumption that psychological states and processes are computational in character pervades much of cognitive science, what many call the computational theory of mind. In addition to occupying a central place in cognitive science, the computational theory of mind has also had a second life supporting “individualism”, the view that psychological states should be taxonomized so as to supervene only on the intrinsic, physical properties of individuals. One response to individualism has been to raise the prospect of “wide computational systems”, (...) in which some computational units are instantiated outside the individual. “Wide computationalism” attempts to sever the link between individualism and computational psychology by enlarging the concept of computation. However, in spite of its potential interest to cognitive science, wide computationalism has received little attention in philosophy of mind and cognitive science. This paper aims to revisit the prospect of wide computationalism. It is argued that by appropriating a mechanistic conception of computation wide computationalism can overcome several issues that plague initial formulations. The aim is to show that cognitive science has overlooked an important and viable option in computational psychology. The paper marshals empirical support and responds to possible objections. (shrink)

Constitutive mechanistic explanations are said to refer to mechanisms that constitute the phenomenon-to-be-explained. The most prominent approach of how to understand this constitution relation is Carl Craver’s mutual manipulability approach to constitutive relevance. Recently, the mutual manipulability approach has come under attack (Leuridan 2012; Baumgartner and Gebharter 2015; Romero 2015; Harinen 2014; Casini and Baumgartner 2016). Roughly, it is argued that this approach is inconsistent because it is spelled out in terms of interventionism (which is an approach to causation), whereas (...) constitutive relevance is said to be a non-causal relation. In this paper, I will discuss a strategy of how to resolve this inconsistency, so-called fat-handedness approaches (Baumgartner and Gebharter 2015; Casini and Baumgartner 2016; Romero 2015). I will argue that these approaches are problematic. I will present a novel suggestion of how to consistently define constitutive relevance in terms of interventionism. My approach is based on a causal interpretation of mutual manipulability, where manipulability is interpreted as a causal relation between the mechanism’s components and temporal parts of the phenomenon. (shrink)

The first part of this paper finds Craver’s (2007) mutual manipulability theory (MM) of constitution inadequate, as it definitionally ties constitution to the feasibility of idealized experiments, which, however, are unrealizable in principle. As an alternative, the second part develops an abductive theory of constitution (NDC), which exploits the fact that phenomena and their constituents are unbreakably coupled via common causes. The best explanation for this common-cause coupling is the existence of an additional dependence relation, viz. constitution. Apart from adequately (...) capturing the essential characteristics of constitution missed by MM, NDC has important ramifications for constitutional discovery—most notably, that there is no experimentum crucis for constitution, not even under ideal discovery circumstances. (shrink)

This paper is concerned with the notions of supervenience and mechanistic constitution as they have been discussed in the philosophy of neuroscience. Since both notions essentially involve specific dependence and determination relations among properties and sets of properties, the question arises whether the notions are systematically connected and how they connect to science. In a first step, some definitions of supervenience and mechanistic constitution are presented and tested for logical independence. Afterwards, certain assumptions fundamental to neuroscientific inquiry are made explicit (...) in order to show that the presented definitions of supervenience are virtually uninteresting for theory construction in this field. In a third step, a new formulation of supervenience is developed that makes explicit reference to the notion of constitution and that bridges the gap between the philosophical concepts and explanatory practice in neuroscience. (shrink)

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)

Vision constitutes an interesting domain, or range of domains, for debate over the extended mind thesis, the idea that minds physically extend beyond the boundaries of the body. In part this is because vision and visual experience more particularly are sometimes presented as a kind of line in the sand for what we might call externalist creep about the mind: once all reasonable concessions have been made to externalists about the mind, visual experience marks a line beyond which lies a (...) safe haven for individualists. Here I want to put a little more pressure on such a view of visual experience, as well as to offer a more constructive, positive argument in defense of the idea of extended vision. (shrink)

Computation is central to the foundations of modern cognitive science, but its role is controversial. Questions about computation abound: What is it for a physical system to implement a computation? Is computation sufficient for thought? What is the role of computation in a theory of cognition? What is the relation between different sorts of computational theory, such as connectionism and symbolic computation? In this paper I develop a systematic framework that addresses all of these questions. Justifying the role of computation (...) requires analysis of implementation, the nexus between abstract computations and concrete physical systems. I give such an analysis, based on the idea that a system implements a computation if the causal structure of the system mirrors the formal structure of the computation. This account can be used to justify the central commitments of artificial intelligence and computational cognitive science: the thesis of computational sufficiency, which holds that the right kind of computational structure suffices for the possession of a mind, and the thesis of computational explanation, which holds that computation provides a general framework for the explanation of cognitive processes. The theses are consequences of the facts that (a) computation can specify general patterns of causal organization, and (b) mentality is an organizational invariant, rooted in such patterns. Along the way I answer various challenges to the computationalist position, such as those put forward by Searle. I close by advocating a kind of minimal computationalism, compatible with a very wide variety of empirical approaches to the mind. This allows computation to serve as a true foundation for cognitive science. (shrink)

Comparisons of rival explanations or theories often involve vague appeals to explanatory power. In this paper, we dissect this metaphor by distinguishing between different dimensions of the goodness of an explanation: non-sensitivity, cognitive salience, precision, factual accuracy and degree of integration. These dimensions are partially independent and often come into conflict. Our main contribution is to go beyond simple stipulation or description by explicating why these factors are taken to be explanatory virtues. We accomplish this by using the contrastive-counterfactual approach (...) to explanation and the view of understanding as an inferential ability. By combining these perspectives, we show how the explanatory power of an explanation in a given dimension can be assessed by showing the range of answers it provides to what-if-things-had-been-different questions and the theoretical and pragmatic importance of these questions. Our account also explains intuitions linking explanation to unification or to exhibition of a mechanism. (shrink)

Not all models are explanatory. Some models are data summaries. Some models sketch explanations but leave crucial details unspecified or hidden behind filler terms. Some models are used to conjecture a how-possibly explanation without regard to whether it is a how-actually explanation. I use the Hodgkin and Huxley model of the action potential to illustrate these ways that models can be useful without explaining. I then use the subsequent development of the explanation of the action potential to show what is (...) required of an adequate mechanistic model. Mechanistic models are explanatory. (shrink)

This paper offers an account of what it is for a physical system to be a computing mechanism—a system that performs computations. A computing mechanism is a mechanism whose function is to generate output strings from input strings and (possibly) internal states, in accordance with a general rule that applies to all relevant strings and depends on the input strings and (possibly) internal states for its application. This account is motivated by reasons endogenous to the philosophy of computing, namely, doing (...) justice to the practices of computer scientists and computability theorists. It is also an application of recent literature on mechanisms, because it assimilates computational explanation to mechanistic explanation. The account can be used to individuate computing mechanisms and the functions they compute and to taxonomize computing mechanisms based on their computing power. (shrink)

To clarify the notion of computation and its role in cognitive science, we need an account of implementation, the nexus between abstract computations and physical systems. I provide such an account, based on the idea that a physical system implements a computation if the causal structure of the system mirrors the formal structure of the computation. The account is developed for the class of combinatorial-state automata, but is sufficiently general to cover all other discrete computational formalisms. The implementation relation is (...) non-vacuous, so that criticisms by Searle and others fail. This account of computation can be extended to justify the foundational role of computation in artificial intelligence and cognitive science. (shrink)

The computational argument for individualism, which moves from computationalism to individualism about the mind, is problematic, not because computationalism is false, but because computational psychology is, at least sometimes, wide. The paper provides an early, or perhaps predecessor, version of the thesis of extended cognition.

The dispute between individualism and anti-individualism is about the individuation of psychological states, and individualism, on some accounts, is committed to the claim that psychological subjects together with their environments do not constitute integrated computational systems. Hence on this view the computational states that explain psychological states in computational accounts of mind will not involve the subject''s natural and social environment. Moreover, the explanation of a system''s interaction with the environment is, on this view, not the primary goal of computational (...) theorizing. Recent work in computational developmental psychology (by A. Karmiloff-Smith and J. Rutkowska) as well as artificial agents or embedded artificial systems (by L.P. Kaelbling, among others) casts doubt on these claims. In these computational models, the environment does not just trigger and sustain input for computational operations, but some computational operations actually involve environmental structures. (shrink)

Advocates of extended cognition argue that the boundaries of cognition span brain, body, and environment. Critics maintain that cognitive processes are confined to a boundary centered on the individual. All participants to this debate require a criterion for distinguishing what is internal to cognition from what is external. Yet none of the available proposals are completely successful. I offer a new account, the mutual manipulability account, according to which cognitive boundaries are determined by relationships of mutual manipulability between the properties (...) and activities of putative components and the overall behavior of the cognitive mechanism in which they figure. Among its main advantages, this criterion is capable of (a) distinguishing components of cognition from causal background conditions and lower-level correlates, and (b) showing how the core hypothesis of extended cognition can serve as a legitimate empirical hypothesis amenable to experimental test and confirmation. Conceiving the debate in these terms transforms the current clash over extended cognition into a substantive empirical debate resolvable on the basis of evidence from cognitive science and neuroscience. (shrink)

What is the relation between coherence and truth? This paper rejects numerous answers to this question, including the following: truth is coherence; coherence is irrelevant to truth; coherence always leads to truth; coherence leads to probability, which leads to truth. I will argue that coherence of the right kind leads to at least approximate truth. The right kind is explanatory coherence, where explanation consists in describing mechanisms. We can judge that a scientific theory is progressively approximating the truth if it (...) is increasing its explanatory coherence in two key respects: broadening by explaining more phenomena and deepening by investigating layers of mechanisms. I sketch an explanation of why deepening is a good epistemic strategy and discuss the prospect of deepening knowledge in the social sciences and everyday life. (shrink)

After briefly discussing the relevance of the notions computation and implementation for cognitive science, I summarize some of the problems that have been found in their most common interpretations. In particular, I argue that standard notions of computation together with a state-to-state correspondence view of implementation cannot overcome difficulties posed by Putnam's Realization Theorem and that, therefore, a different approach to implementation is required. The notion realization of a function, developed out of physical theories, is then introduced as a replacement (...) for the notional pair computation-implementation. After gradual refinement, taking practical constraints into account, this notion gives rise to the notion digital system which singles out physical systems that could be actually used, and possibly even built. (shrink)

The mechanistic account of computation offers one promising and influential theory of computational implementation. The aim of this paper is to shore up its conceptual foundations by responding to several recent challenges. After outlining and responding to a recent proposal from Kuokkanen (2022a), I suggest that computational description should be conceptualised as a form of idealisation (selectively attending to modified subsets of model features) rather than abstraction (selectively attending to subsets of features within a target system). I argue that this (...) conceptualisation not only offers the best way of making sense of computational implementation, but also a way of resolving each of the outstanding challenges facing the mechanistic account. The idealisation view allows the mechanistic account to make sense of the omission process found in computational descriptions without leaving the relationship between physical and computational properties mysterious. (shrink)

The mechanistic view of computation contends that computational explanations are mechanistic explanations. Mechanists, however, disagree about the precise role that the environment – or the so-called “contextual level” – plays for computational explanations. We advance here two claims: Contextual factors essentially determine the computational identity of a computing system ; this means that specifying the “intrinsic” mechanism is not sufficient to fix the computational identity of the system. It is not necessary to specify the causal-mechanistic interaction between the system and (...) its context in order to offer a complete and adequate computational explanation. While the first claim has been discussed before, the second has been practically ignored. After supporting these claims, we discuss the implications of our contextualist view for the mechanistic view of computational explanation. Our aim is to show that some versions of the mechanistic view are consistent with the contextualist view, whilst others are not. (shrink)

Mechanistic approaches are very common in the causal interpretation of biological and neuroscientific experimental work in today’s philosophy of science. In the mechanistic literature a strict distinction is often made between causal relations and constitutive relations, where the latter cannot be causal. One of the typical reasons for this strict distinction is that constitutive relations are supposedly synchronic whereas most if not all causal relations are diachronic. This strict distinction gives rise to a number of problems, however. Our end goal (...) in this paper is to argue that it should be given up, at least in the context of the biological and the psychological sciences. To that effect, we argue that constitutive relations in this context are diachronic, thus undermining the aforementioned reason. We offer two cases from scientific practice in which constitutive relations are regarded as both diachronic and causally efficacious, review three existing ways of dealing with the apparent diachronic nature of interlevel relations in mechanisms and propose a new account of diachronic, causal constitutive relevance. (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)

This paper takes up a recent challenge to mechanistic approaches to computational implementation, the view that computational implementation is best explicated within a mechanistic framework. The challenge, what has been labelled “the abstraction problem”, claims that one of MAC’s central pillars – medium independence – is deeply confused when applied to the question of computational implementation. The concern is that while it makes sense to say that computational processes are abstract (i.e. medium-independent), it makes considerably less sense to say that (...) they are also concrete processes of a mechanism. After outlining the problem and its effect on MAC, I examine a recent response from Kuokkanen and Rusanen [2018. “Making Too Many Enemies: Hutto and Myin’s Attack on Computationalism.” Philosophical Explorations 21 (2): 282–294. doi:10.1080/13869795.2018.1477980]. I argue that Kuokkanen and Rusanen’s response comes up short insofar as it makes problematic trade-offs among various desiderata we have for a theory of implementation. This leads to a general dilemma for MAC: either give up being an objective theory of implementation or concede the abstraction problem and so reintroduce triviality concerns. In response, I argue that conceiving of computations as abstracta rather than illata provides a way to avoid the proposed dilemma and articulate a notion of medium independence that addresses the abstraction problem. (shrink)

Some have suggested that there is no fact to the matter as to whether or not a particular physical system relaizes a particular computational description. This suggestion has been taken to imply that computational states are not real, and cannot, for example, provide a foundation for the cognitive sciences. In particular, Putnam has argued that every ordinary open physical system realizes every abstract finite automaton, implying that the fact that a particular computational characterization applies to a physical system does not (...) tell oneanything about the nature of that system. Putnam''s argument is scrutinized, and found inadequate because, among other things, it employs a notion of causation that is too weak. I argue that if one''s view of computation involves embeddedness (inputs and outputs) and full causality, one can avoid the universal realizability results. Therefore, the fact that a particular system realizes a particular automaton is not a vacuous one, and is often explanatory. Furthermore, I claim that computation would not necessarily be an explanatorily vacuous notion even if it were universally realizable. (shrink)