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)

The functionalist approach to kinds has suffered recently due to its association with law-based approaches to induction and explanation. Philosophers of science increasingly view nomological approaches as inappropriate for the special sciences like psychology and biology, which has led to a surge of interest in approaches to natural kinds that are more obviously compatible with mechanistic and model-based methods, especially homeostatic property cluster theory. But can the functionalist approach to kinds be weaned off its dependency on laws? Dan Weiskopf has (...) recently offered a reboot of the functionalist program by replacing its nomological commitments with a model-based approach more closely derived from practice in psychology. Roughly, Weiskopf holds that the natural kinds of psychology will be the functional properties that feature in many empirically successful cognitive models, and that those properties need not be localized to parts of an underlying mechanism. I here skeptically examine the three modeling practices that Weiskopf thinks introduce such non-localizable properties: fictionalization, reification, and functional abstraction. In each case, I argue that recognizing functional properties introduced by these practices as autonomous kinds comes at clear cost to those explanations’ counterfactual explanatory power. At each step, a tempting functionalist response is parochialism: to hold that the false or omitted counterfactuals fall outside the modeler’s explanatory aims, and so should not be counted against functional kinds. I conclude by noting the dangers this attitude poses to scientific disagreement, inviting functionalists to better articulate how the individuation conditions for functional kinds might outstrip the perspective of a single modeler. (shrink)

In artificial intelligence, recent research has demonstrated the remarkable potential of Deep Convolutional Neural Networks (DCNNs), which seem to exceed state-of-the-art performance in new domains weekly, especially on the sorts of very difficult perceptual discrimination tasks that skeptics thought would remain beyond the reach of artificial intelligence. However, it has proven difficult to explain why DCNNs perform so well. In philosophy of mind, empiricists have long suggested that complex cognition is based on information derived from sensory experience, often appealing to (...) a faculty of abstraction. Rationalists have frequently complained, however, that empiricists never adequately explained how this faculty of abstraction actually works. In this paper, I tie these two questions together, to the mutual benefit of both disciplines. I argue that the architectural features that distinguish DCNNs from earlier neural networks allow them to implement a form of hierarchical processing that I call “transformational abstraction”. Transformational abstraction iteratively converts sensory-based representations of category exemplars into new formats that are increasingly tolerant to “nuisance variation” in input. Reflecting upon the way that DCNNs leverage a combination of linear and non-linear processing to efficiently accomplish this feat allows us to understand how the brain is capable of bi-directional travel between exemplars and abstractions, addressing longstanding problems in empiricist philosophy of mind. I end by considering the prospects for future research on DCNNs, arguing that rather than simply implementing 80s connectionism with more brute-force computation, transformational abstraction counts as a qualitatively distinct form of processing ripe with philosophical and psychological significance, because it is significantly better suited to depict the generic mechanism responsible for this important kind of psychological processing in the brain. (shrink)

Functionalism about kinds is still the dominant style of thought in the special sciences, like economics, psychology, and biology. Generally construed, functionalism is the view that states or processes can be individuated based on what role they play rather than what they are constituted of or realized by. Recently, Weiskopf has posited a reformulation of functionalism on the model-based approach to explanation. We refer to this reformulation as ‘new functionalism’. In this paper, we seek to defend new functionalism and to (...) recast it in light of the concrete explanatory aims of the special sciences. In particular, we argue that the assessment of the explanatory legitimacy of a functional kind needs to take into account the explanatory purpose of the model in which the functional kind is employed. We aim at demonstrating this by appealing to model-based explanations from the social and behavioral sciences. Specifically, we focus on preferences and signals as functional kinds. Our argument is intended to have the double impact of deflecting criticisms against new functionalism from the perspective of mechanistic decomposition while also expanding the scope of new functionalism to encompass the social and behavioral sciences. (shrink)

Shapiro has suggested that the empirical plausibility of the multiple realizability of human-like minds is dubious, because a contrary thesis, the Mental Constraint Thesis, enjoys positive empirical evidence. The Mental Constraint Thesis states that, given the actual physical laws, there is only one way to realize a human-like mind. I will suggest, however, that the Mental Constraint Thesis is not a contrary to the empirical multiple realizability thesis relevant to psychological reduction or autonomy and, as a consequence, has no bearing (...) on those classificatory issues. (shrink)

Dynamical systems play a central role in explanations in cognitive neuroscience. The grounds for these explanations are hotly debated and generally fall under two approaches: non-mechanistic and mechanistic. In this paper, I first outline a neurodynamical explanatory schema that highlights the role of dynamical systems in cognitive phenomena. I next explore the mechanistic status of such neurodynamical explanations. I argue that these explanations satisfy only some of the constraints on mechanistic explanation and should be considered pseudomechanistic explanations. I defend this (...) argument against three alternative interpretations of the neurodynamical explanatory schema. The independent interpretation holds that neurodynamical explanations and mechanisms are independent. The constitutive interpretation holds that neurodynamical explanations are constitutive but otherwise non-mechanistic. Both the independent and constitutive interpretations fail to account for all the features of neurodynamical explanations. The partial interpretation assumes that the targets of dynamical systems models are mechanisms and so holds that neurodynamical explanations are incomplete because they lack mechanistic details. I contend instead that the targets of those models are dynamical systems distinct from mechanisms and defend this claim against several objections. I conclude with a defense of the pseudomechanistic interpretation and a discussion of the source of their explanatory power in relation to a causal-mechanical description of the world. (shrink)

There is a familiar tension between the two main components of non-reductive physicalism, which are physicalism, on the one hand, and the non-reducibility or autonomy of so-called special sciences (i.e., sciences other than physics), on the other. While it is often claimed that this tension can be satisfactorily resolved by adopting the subset account of realization (defended by e.g., Shoemaker (2001) and Wilson (2011)), this paper challenges that popular view, by elaborating some critical comments by Funkhouser (2014). Then, after examining (...) Funkhouser’s own position, this paper proposes a new way of resolving the aforementioned tension, which consists in refining the subset account’s analysis of realization on the basis of various dependence relations between causal powers. (shrink)

Scientists cannot devise theories, construct models, propose explanations, make predictions, or even carry out observations, without first classifying their subject matter. The goal of scientific taxonomy is to come up with classification schemes that conform to nature's own. Another way of putting this is that science aims to devise categories that correspond to 'natural kinds.' The interest in ascertaining the real kinds of things in nature is as old as philosophy itself, but it takes on a different guise when one (...) adopts a naturalist stance in philosophy, that is when one looks closely at scientific practice and takes it as a guide for identifying natural kinds and investigating their general features. This Element surveys existing philosophical accounts of natural kinds, defends a naturalist alternative, and applies it to case studies in a diverse set of sciences. This title is also available as Open Access on Cambridge Core. (shrink)

Machery denies the traditional view that concepts are constituents of thoughts, and he more provocatively argues that concepts should be eliminated from our best psychological taxonomy. I argue that the constituency view has much to recommend it (and is presupposed by much of his own theory), and that the evidence gives us grounds for pluralism, rather than eliminativism, about concepts.

Mechanistic explanation has an impressive track record of advancing our understanding of complex, hierarchically organized physical systems, particularly biological and neural systems. But not every complex system can be understood mechanistically. Psychological capacities are often understood by providing cognitive models of the systems that underlie them. I argue that these models, while superficially similar to mechanistic models, in fact have a substantially more complex relation to the real underlying system. They are typically constructed using a range of techniques for abstracting (...) the functional properties of the system, which may not coincide with its mechanistic organization. I describe these techniques and show that despite being non-mechanistic, these cognitive models can satisfy the normative constraints on good explanations. (shrink)

I note the multitude of ways in which, beginning with the classic paper by Machamer et al., the mechanists have qualify their methodological dicta, and limit the vulnerability of their claims by strategic vagueness regarding their application. I go on to generalize a version of the mechanist requirement on explanations due to Craver and Kaplan :601–627, 2011) in cognitive and systems neuroscience so that it applies broadly across the life sciences in accordance with the view elaborated by Craver and Darden (...) in In Search of Mechanisms. I then go on to explore what ramifications their mechanist requirement on explanations may have for explanatory “dependencies” reported in biology and the special sciences. What this exploration suggests is that mechanism threatens to eliminate instead of underwrite a large number of such “dependencies” reported in higher-levels of biology and the special sciences. I diagnose the source of this threat in mechanism’s demand that explanations identify nested causal differences makers in mechanisms, their components, the components further components, and so forth. Finally, I identify the “love–hate” relationship mechanism must have with functional explanation, and show how it makes mechanism an extremely interesting thesis indeed. (shrink)

Many scientific models in biology are how-possibly models. These models depict things as they could be, but do not necessarily capture actual states of affairs in the biological world. In contemporary philosophy of science, it is customary to treat how-possibly models as second-rate theoretical tools. Although possibly important in the early stages of theorizing, they do not constitute the main aim of modelling, namely, to discover the actual mechanism responsible for the phenomenon under study. In the paper it is argued (...) that this prevailing picture does not do justice to the synthetic strategy that is commonly used in biological engineering. In synthetic biology, how-possibly models are not simply speculations or eliminable scaffolds towards a single how-actually model, but indispensable design hypotheses for a field whose ultimate goal is to build novel biological systems. The paper explicates this by providing an example from the study of alternative genetic systems by synthetic biologist Steven Benner and his group. The case will also highlight how the method of synthesis, even when it fails, provides an effective way to limit the space of possible models for biological systems. (shrink)

An important strand in philosophy of science takes scientific explanation to consist in the conveyance of some kind of information. Here I argue that this idea is also implicit in some core arguments of mechanists, some of whom are proponents of an ontic conception of explanation that might be thought inconsistent with it. However, informational accounts seem to conflict with some lay and scientific commonsense judgments and a central goal of the theory of explanation, because information is relative to the (...) background knowledge of agents. Sometimes we make lay judgments about whether a model is an explanation simpliciter, not just an explanation relative to some particular agent. And as philosophers of explanation, we would like a philosophical account to tell us when a model is an explanation simpliciter, not just when a model is an explanation relative to some particular agent. Thus, even if one’s account of explanation is not concerned with explanation qua communicative or speech act, the account’s reliance on the concept of information generates a prima facie conflict between the claims that 1) explanation is the conveyance of information, 2) information is relative to the background knowledge of an agent, and 3) some models are explanations not relative to the background knowledge of any particular agent. I sketch a solution to this puzzle by distinguishing informationally what I call “explanation simpliciter” from what I call “explanation-to,” relativizing the latter to an individual’s background knowledge and the former to what I call “total scientific background knowledge”. (shrink)

The debate between the defenders of explanatory unification and explanatory pluralism has been ongoing from the beginning of cognitive science and is one of the central themes of its philosophy. Does cognitive science need a grand unifying theory? Should explanatory pluralism be embraced instead? Or maybe local integrative efforts are needed? What are the advantages of explanatory unification as compared to the benefits of explanatory pluralism? These questions, among others, are addressed in this Synthese’s special issue. In the introductory paper, (...) we discuss the background of the questions, distinguishing integrative theorizing from building unified theories. On the one hand, integrative efforts involve collaboration between various disciplines, fields, approaches, or theories. These efforts could even be quite temporary, without establishing any long-term institutionalized fields or disciplines, but could also contribute to developing new interfield theories. On the other hand, unification can rely on developing complete theories of mechanisms and representations underlying all cognition, as Newell’s “unified theories of cognition”, or may appeal to grand principles, as predictive coding. Here, we also show that unification in contemporary cognitive science goes beyond reductive unity, and may involve various forms of joint efforts and division of explanatory labor. This conclusion is one of the themes present in the content of contributions constituting the special issue. (shrink)

Opponents to consciousness in fish argue that fish do not feel pain because they do not have a neocortex, which is a necessary condition for feeling pain. A common counter-argument appeals to the multiple realizability of pain: while a neocortex might be necessary for feeling pain in humans, pain might be realized differently in fish. This paper argues, first, that it is impossible to find a criterion allowing us to demarcate between plausible and implausible cases of multiple realization of pain (...) without running into a circular argument. Second, opponents to consciousness in fish cannot be provided with reasons to believe in the multiple realizability of pain. I conclude that the debate on the existence of pain in fish is impossible to settle by relying on the multiple realization argument. (shrink)

In natural kind debates, Boyd's famous Homeostatic Property Cluster theory (HPC) is often misconstrued in two ways: Not only is it thought to make for a normative standard for natural kinds, but also to require the homeostatic mechanisms underlying nomological property clusters to be uniform. My argument for the illegitimacy of both overgeneralizations, both on systematic as well as exegetical grounds, is based on the misconstrued view's failure to account for functional kinds in science. I illustrate the combination of these (...) two misconstruals with recent entries into the natural kind debate about emotions. Finally, I examine and reject Stich's "Kornblith-Devitt method" as a potential justification of these misconstruals. (shrink)

Recently, several critics of the multiple realizability thesis have argued that philosophers have tended to accept the thesis on too weak grounds. On the one hand, the analytic challenge has problematized how philosophers have treated the multiple realization relation itself, claiming that assessment of the sameness of function and the relevant difference of realizers has been uncritical. On the other hand, it is argued that the purported evidence of the thesis is often left empirically unverified. This paper provides a novel (...) strategy to answer these worries by introducing a role for multiple realizability in the context of biological engineering. In the field of synthetic biology, bioengineers redesign the evolutionary realizations of biological functions, even constructing artificial chemical surrogates in the laboratory. I show how in the rational design approach to biological engineering, multiple realizability can function as a design heuristic in which the sameness of function and difference of realizers can be controlled. Although practically motivated, this engineering approach has also a theoretical, exploratory component that can be used to study the empirical limitations of multiple realizability. Successful realization of the engineering designs would amount to a concrete demonstration of multiple realizability, taking evidence for MRT beyond what is readily found in nature. (shrink)

Critics of multiple realizability have recently argued that we should concentrate solely on actual here-and-now realizations that are found in nature. The possibility of alternative, but unactualized, realizations is regarded as uninteresting because it is taken to be a question of pure logic or an unverifiable scenario of science fiction. However, in the biological context only a contingent set of realizations is actualized. Drawing on recent work on the theory of neutral biological spaces, the paper shows that we can have (...) ways of assessing the modal dimension of multiple realizability that do not have to rely on mere conceivability. (shrink)

Researchers in the cognitive sciences often seek neural correlates of psychological constructs. In this paper, I argue that even when these correlates are discovered, they do not always lead to reductive outcomes. To this end, I examine the psychological construct of a critical period and briefly describe research identifying its neural correlates. Although the critical period is correlated with certain neural mechanisms, this does not imply that there is a reductionist relationship between this psychological construct and its neural correlates. Instead, (...) this case study suggests that there may be many-to-many psychological-neural mappings, not just one-to-one or even one-to-many relations between psychological kinds and types of neural mechanisms. (shrink)

This paper makes a distinction between ahistorical causal-functional kinds and historical kinds, which include both type- and token-historical kinds, some of which are “copied kinds.” After showing how these distinctions play out in various social sciences, a number of reasons are put forward for the historical individuation of some social kinds. As in the natural sciences, historical individuation in the social sciences can enable us to infer common causes, explain synchronic causal properties, and discover exceptions to causal regularities, among other (...) epistemic aims. (shrink)

I will begin by proposing a taxonomy of taxonomic positions regarding the mind–brain: localism, globalism, revisionism, and contextualism, and will go on to focus on the last position. Although some versions of contextualism have been defended by various researchers, they largely limit themselves to a version of neural contextualism: different brain regions perform different functions in different neural contexts. I will defend what I call “environmental-etiological contextualism,” according to which the psychological functions carried out by various neural regions can only (...) be identified and individuated against an environmental context or with reference to a causal history. While this idea may seem innocuous enough, it has important implications for a structure-to-function mapping in the mind and brain sciences. It entails that the same neural structures can subserve different psychological functions in different contexts, leading to crosscutting psycho-neural mappings. I will try to illustrate how this can occur with reference to recent research on episodic memory. (shrink)

The notion of multi-functional kind is introduced to explain how social scientists may be able to draw inferences across historically unrelated societies or cultures. Multi-functional kinds are neither eternal nor purely historical, support non-trivial inductive generalisations, and allow to overcome scepticism about the inductive potential of multiply realised (functional) properties. Two examples, from monetary economics and anthropology, provide support for a pluralistic ontology of the social world.

Standard defenses of ontological individualism are challenged by arguments that exploit the dependence of social facts on material facts—that is, facts that are not about human individuals. In this article, I discuss Brian Epstein’s “materialism” in The Ant Trap: granting Epstein’s strict definition of individualism, I show that his arguments depend crucially on a generous conception of social properties and social facts. Individualists, however, are only committed to the claim that projectible properties are individualistically realized, and materialists have not undermined (...) this claim. (shrink)

I argue that there is a tension between the claim that at least some kinds in the special sciences are multiply realized and the claim that the reason why kinds are prized by science is that they enter into a variety of different empirical generalizations. Nevertheless, I show that this tension ceases in the case of ‘cultural homologues’—such as specific ideologies, religions, and folk wisdom. I argue that the instances of such special science kinds do have several projectable properties in (...) common due to their shared history of reproduction, and that the social learning involved means that we should also expect these kinds to be multiply realized. (shrink)

In this paper, I defend an updated account of functional kinds, initially presented by Daniel Weiskopf, from the criticism that functional kinds will not qualify as scientific kinds. An important part of Weiskopf’s account is that functional kinds are multiply realisable. The criticisms I consider avoid discussion of multiple realisability. Instead, it is argued that functional kinds carry inferior counterfactual profiles when compared to other accounts of kinds. I respond to this charge by arguing that this criticism fails to take (...) into consideration the role that multiple realisability can play in providing important explanatory counterfactuals. I do so by highlighting some points made by Lauren Ross that highlight where multiple realisability is explanatorily pertinent. I then argue that the criticisms of Weiskopf’s account fail to establish the explanatory inferiority of functional kinds. (shrink)

Proponents of mechanistic explanation have argued that dynamical models are mere phenomenal models, in that they describe rather than explain the scientific phenomena produced by complex systems. I argue instead that dynamical models can, in fact, be explanatory. Using an example from neuroscientific research on epilepsy, I show that dynamical models can meet the explanatory demands met by mechanistic models, and as such occupy their own unique place within the space of explanatory scientific models.

In this dissertation, I address the question of how to construct scientific theories of emotions that are both conceptually sound and empirically fruitful. To do this, I offer an analysis of the main challenges scientific theories of emotions face, and I propose a meta-theoretical framework to construct scientific concepts of emotions as explications of folk emotion concepts. Part I discusses the main challenges theories of emotions in psychology and neuroscience encounter. The first states that a proper scientific theory of emotions (...) must explain all and only the phenomena under the vernacular term ‘emotion’ with a common set of conceptual resources and under an overarching generic concept of emotion. The second demands that each emotion category corresponds to well-coordinated sets of neural, physiological, and behavioral patterns of responses. I argue that none of the best contemporary theories of emotions in psychology and neuroscience overcomes these challenges. As a result, a new theory of emotions is required. In Part II, I develop the meta-theoretical framework to construct a theory of emotions that overcomes the challenges above. First, I propose a pluralistic account of scientific kinds based on different patterns of projection that various disciplines may take to justify inductive inferences. These are essentialist, historical, and social patterns. Each of these patterns provides a framework to construct different types of scientific concepts. I argue that among the frameworks for scientific kinds available, the one that is best suited to explicate emotion concepts is a functional framework. Consequently, I conclude by recommending scientists pursue functionalist theories of emotions over essentialist, historical, or social theories. (shrink)

This paper focuses on the framework for the compositional relations of properties in the sciences, or "realization relations", offered by Ken Aizawa and Carl Gillett (A&G) in a series of papers, and in particular on the analysis of "multiple realizations" they build upon it. I argue that A&G's analysis of multiple realization requires an account of levels and I try to show, then, that the A&G framework is not successful under any of the extant accounts of levels. There is consequently (...) a real concern tha thte A&G framework for realization may not be viable. (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)