Emergence and reduction in dynamical cognitive science

This paper examines the widespread intuition that the dynamical approach to cognitive science is importantly related to emergentism about the mind. The explanatory practices adopted by dynamical cognitive science rule out some conceptions of emergence; covering law explanations require a deducibility relationship between explanans and explanandum, whereas canonical theories of emergence require the absence of such deducibility. A response to this problem - one which would save the intuition that dynamics and emergence are related - is to reconstrue the concept of emergence as a relationship between laws. I call this “nomological emergence” and comment on the extent to which dynamicists would find it acceptable. Alternatively, dynamical cognitive science might be viewed as fitting better with the kind of “functional reductionism” which has recently been developed by authors such as Jaegwon Kim. Which of these two alternatives is preferable remains an open question pending the further development of dynamical cognitive science, particularly in its “non-classical” forms.     

Rethinking the explanatory power of dynamical models in cognitive science

In this paper I offer an interventionist perspective on the explanatory structure and explanatory power of (some) dynamical models in cognitive science: I argue that some “pure” dynamical models – ones that do not refer to mechanisms at all – in cognitive science are “contextualized causal models” and that this explanatory structure gives such models genuine explanatory power. I contrast this view with several other perspectives on the explanatory power of “pure” dynamical models. One of the main results is that dynamical models need not refer to underlying mechanisms in order to be explanatory. I defend and illustrate this position in terms of dynamical models of the A-not-B error in developmental psychology as elaborated by Thelen and colleagues, and dynamical models of unintentional interpersonal coordination developed by Richardson and colleagues.     

Grades of Explanation in Cognitive Science

I sketch an explanatory framework that fits a variety of contemporary research programs in cognitive science. I then investigate the scope and the implications of this framework. The framework emphasizes (a) the explanatory role played by the semantic content of cognitive representations, and (b) the important mechanistic, non-intentional dimension of cognitive explanations. I show how both of these features are present simultaneously in certain varieties of cognitive explanation. I also consider the explanatory role played by grounded representational content, that is, content evaluated by appeal to its truth, falsity, accuracy, inaccuracy and other relational properties.     

The role of representation in computation

Reformers urge that representation no longer earns its explanatory keep in cognitive science, and that it is time to discard this troublesome concept. In contrast, we hold that without representation cognitive science is utterly bereft of tools for explaining natural intelligence. In order to defend the latter position, we focus on the explanatory role of representation in computation. We examine how the methods of digital and analog computation are used to model a relatively simple target system, and show that representation plays an in-eliminable explanatory role in both cases. We conclude that, to the extent that biologic systems engage in computation, representation is destined to play an explanatory role in cognitive science.

Radicalizing numerical cognition

In recent decades, non-representational approaches to mental phenomena and cognition have been gaining traction in cognitive science and philosophy of mind. In these alternative approach, mental representations either lose their central status or, in its most radical form, are banned completely. While there is growing agreement that non-representational accounts may succeed in explaining some cognitive capacities (e.g. perception), there is widespread skepticism about the possibility of giving non-representational accounts of cognitive capacities such as memory, imagination or abstract thought. In this paper, I will critically examine the view that there are fundamental limitations to non-representational explanations of cognition. Rather than challenging these arguments on general grounds, I will examine a set of human cognitive capacities that are generally thought to fall outside the scope of non-representational accounts, i.e. numerical cognition. After criticizing standard representational accounts of numerical cognition for their lack of explanatory power, I will argue that a non-representational approach that is inspired by radical enactivism offers the best hope for developing a genuine naturalistic explanatory account for these cognitive capacities.

     

The proper role of history in evolutionary explanations

Evolutionary explanations are not only common in the biological sciences, but also widespread outside biology. But an account of how evolutionary explanations perform their explanatory work is still lacking. This paper develops such an account. I argue that available accounts of explanations in evolutionary science miss important parts of the role of history in evolutionary explanations. I argue that the historical part of evolutionary science should be taken as having genuine explanatory force, and that it provides how-possibly explanations sensu Dray. I propose an account of evolutionary explanations as comparative-composite explanations consisting of two distinct kinds of explanations, one processual and one historical, that are connected via the explanandum's evolvability to show how the explanandum is the product of its evolutionary past. The account is both a reconstruction of how evolutionary explanations in biology work and a guideline specifying what kind of explanations evolutionary research programs should develop.     

Manipulation is key: on why non-mechanistic explanations in the cognitive sciences also describe relations of manipulation and control

A popular view presents explanations in the cognitive sciences as causal or mechanistic and argues that an important feature of such explanations is that they allow us to manipulate and control the explanandum phenomena. Nonetheless, whether there can be explanations in the cognitive sciences that are neither causal nor mechanistic is still under debate. Another prominent view suggests that both causal and non-causal relations of counterfactual dependence can be explanatory, but this view is open to the criticism that it is not clear how to distinguish explanatory from non-explanatory relations. In this paper, I draw from both views and suggest that, in the cognitive sciences, relations of counterfactual dependence that allow manipulation and control can be explanatory even when they are neither causal nor mechanistic. Furthermore, the ability to allow manipulation can determine whether non-causal counterfactual dependence relations are explanatory. I present a preliminary framework for manipulation relations that includes some non-causal relations and use two examples from the cognitive sciences to show how this framework distinguishes between explanatory and non-explanatory, non-causal relations. The proposed framework suggests that, in the cognitive sciences, causal and non-causal relations have the same criterion for explanatory value, namely, whether or not they allow manipulation and control.     

Representations and Cognitive Explanations: Assessing the Dynamicist's Challenge in Cognitive Science

Advocates of dynamical systems theory (DST) sometimes employ revolutionary rhetoric. In an attempt to clarify how DST models differ from others in cognitive science, I focus on two issues raised by DST: the role for representations in mental models and the conception of explanation invoked. Two features of representations are their role in standing-in for features external to the system and their format. DST advocates sometimes claim to have repudiated the need for stand-ins in DST models, but I argue that they are mistaken. Nonetheless, DST does offer new ideas as to the format of representations employed in cognitive systems. With respect to explanation, I argue that some DST models are better seen as conforming to the covering-law conception of explanation than to the mechanistic conception of explanation implicit in most cognitive science research. But even here, I argue, DST models are a valuable complement to more mechanistic cognitive explanations.     

Function and Functional Explanation in Social Capital Theory: A Philosophical Appraisal

Social capital is frequently offered up as a variable to explain such educational outcomes as academic attainment, drop-out rates and cognitive development. Yet, despite its popularity amongst social scientists, social capital theory remains the object of some scepticism, particularly in respect of its explanatory ambitions. I provide an account of some explanatory options available to social capital theorists, focussing on the functions ascribed to social capital and on how these are used as explanatory variables in educational theory. Two of the most influential writers in this field are Coleman and Bourdieu. I explore their commonalities and differences, both in respect of their writing and in respect of some of the many theorists they have influenced. I argue that social capital theorists have made substantial progress in responding to sceptically minded critics, but that significant questions remain, especially about the success of the more ambitious explanatory variants as these apply to educational outcomes—functional explanation in particular. Functional explanation, and its association with Bourdieu, is discussed in ‘Bourdieu and functional explanation’; thereafter I discuss the more modest ambition of identifying the functions associated with social capital. In ‘Coleman, intergenerational closure and educational outcomes’ I show how Coleman provides resources for revealing how social structure features in social explanation in an educational context, and in ‘Inequality, class and ethnicity’ I suggest that some of the questions raised in his account are most satisfactorily responded to by educational theorists who adopt Bourdieu’s emphasis on social class and inequality.     

Explanations in cognitive science: unification versus pluralism

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.

     

Experimental design: Problems in understanding the dynamical behavior-environment system

In this paper, I attempt to describe the implications of dynamical approaches to science for research in the experimental study of behavior. I discuss the differences between classical and dynamical science, and focus on how dynamical science might see replication differently from classical science. Focusing on replication specifically, I present some problems that the classical approach has in dealing with dynamics and multiple causation. I ask about the status and meaning of "error" variance, and whether it may be a potent source of information. I show how a dynamical approach can handle the sort of control by past events that is hard for classical science to understand. These concerns require, I believe, an approach to variability that is quite different from the one most researchers currently employ. I suggest that some of these problems can be overcome by a notion of "behavioral state," which is a distillation of an organism's history.     

Scalar implicature and contrastive explanation

I argue for a subsumption of any version of Grice’s first quantity maxim posited to underlie scalar implicature, by developing the idea of implicature recovery as a kind of explanatory inference, as e.g. in science. I take the applicable model to be contrastive explanation, while following van Fraassen’s analysis of explanation as an answer to a why-question. A scalar implicature is embedded in such an answer, one that meets two probabilistic constraints: the probability of the answer, and ‘favoring’. I argue that besides having application at large, outside of linguistic interpretation, these constraints largely account not only for implicatures based on strength order, logical and otherwise, but also for unordered cases. I thus suggest that Grice’s maxim and its descendants are expressions of general explanatory constraints, as they happen to be manifested in this particular explanatory task. I conclude by briefly discussing how I accordingly view Grice’s system outside of scalar implicature.     

Importance and Explanatory Relevance: The Case of Mathematical Explanations

A way to argue that something (e.g. mathematics, idealizations, moral properties, etc.) plays an explanatory role in science is by linking explanatory relevance with importance in the context of an explanation. The idea is deceptively simple: a part of an explanation is an explanatorily relevant part of that explanation if removing it affects the explanation either by destroying it or by diminishing its explanatory power, i.e. an important part (one that if removed affects the explanation) is an explanatorily relevant part. This can be very useful in many ontological debates. My aim in this paper is twofold. First of all, I will try to assess how this view on explanatory relevance can affect the recent ontological debate in the philosophy of mathematics—as I will argue, contrary to how it may appear at first glance, it does not help very much the mathematical realists. Second of all, I will show that there are big problems with it.     

Dynamical approaches to cognitive science

Dynamical ideas are beginning to have a major impact on cognitive science, from foundational debates to daily practice. In this article, I review three contrasting examples of work in this area that address the lexical and grammatical structure of language, Piaget's classic 'A-not-B' error, and active categorical perception in an embodied, situated agent. From these three examples, I then attempt to articulate the major differences between dynamical approaches and more traditional symbolic and connectionist approaches. Although the three models reviewed here vary considerably in their details, they share a focus on the unfolding trajectory of a system's state and the internal and external forces that shape this trajectory, rather than the representational content of its constituent states or the underlying physical mechanisms that instantiate the dynamics. In some work, this dynamical viewpoint is augmented with a situated and embodied perspective on cognition, forming a promising unified theoretical framework for cognitive science broadly construed.     

A Neurathian Conception of the Unity of Science

An historically important conception of the unity of science is explanatory reductionism, according to which the unity of science is achieved by explaining all laws of science in terms of their connection to microphysical law. There is, however, a separate tradition that advocates the unity of science. According to that tradition, the unity of science consists of the coordination of diverse fields of science, none of which is taken to have privileged epistemic status. This alternate conception has roots in Otto Neurath’s notion of unified science. In this paper, I develop a version of the coordination approach to unity that is inspired by Neurath’s views. The resulting conception of the unity of science achieves aims similar to those of explanatory reductionism, but does so in a radically different way. As a result, it is immune to the criticisms facing explanatory reductionism. This conception of unity is also importantly different from the view that science is disunified, and I conclude by demonstrating how it accords better with scientific practice than do conceptions of the disunity of science.     

Intentional concepts in cognitive neuroscience

In this article, I develop an account of the use of intentional predicates in cognitive neuroscience explanations. As pointed out by Maxwell Bennett and Peter Hacker, intentional language abounds in neuroscience theories. According to Bennett and Hacker, the subpersonal use of intentional predicates results in conceptual confusion. I argue against this overly strong conclusion by evaluating the contested language use in light of its explanatory function. By employing conceptual resources from the contemporary philosophy of science, I show that although the use of intentional predicates in mechanistic explanations sometimes leads to explanatorily inert claims, intentional predicates can also successfully feature in mechanistic explanations as tools for the functional analysis of the explanandum phenomenon. Despite the similarities between my account and Daniel Dennett's intentional-stance approach, I argue that intentional stance should not be understood as a theory of subpersonal causal explanation, and therefore cannot be used to assess the explanatory role of intentional predicates in neuroscience. Finally, I outline a general strategy for answering the question of what kind of language can be employed in mechanistic explanations.     

Challenging the importance of vision for the development of an extrinsic spatial framework: evidence from the blind and sighted

Although connectionism is advocated by its proponents as an alternative to the classical computational theory of mind, doubts persist about its computational credentials. Our aim is to dispel these doubts by explaining how connectionist networks compute. We first develop a generic account of computation-no easy task, because computation, like almost every other foundational concept in cognitive science, has resisted canonical definition. We opt for a characterisation that does justice to the explanatory role of computation in cognitive science. Next we examine what might be regarded as the "conventional" account of connectionist computation. We show why this account is inadequate and hence fosters the suspicion that connectionist networks are not genuinely computational. Lastly, we turn to the principal task of the paper: the development of a more robust portrait of connectionist computation. The basis of this portrait is an explanation of the representational capacities of connection weights, supported by an analysis of the weight configurations of a series of simulated neural networks.     

Dynamical Systems Theory as an Approach to Mental Causation

Summary  Dynamical systems theory (DST) is gaining popularity in cognitive science and philosophy of mind. Recently several authors (e.g. J.A.S. Kelso, 1995; A. Juarrero, 1999; F. Varela and E. Thompson, 2001) offered a DST approach to mental causation as an alternative for models of mental causation in the line of Jaegwon Kim (e.g. 1998). They claim that some dynamical systems exhibit a form of global to local determination or downward causation in that the large-scale, global activity of the system governs or constrains local interactions. This form of downward causation is the key to the DST model of mental causation. In this paper I evaluate the DST approach to mental causation. I will argue that the main problem for current DST approaches to mental causation is that they lack a clear metaphysics. I propose one metaphysical framework (Gillett, 2002a/b/c) that might deal with this deficiency.     

Make up your mind: octopus cognition and hybrid explanations

In order to argue that cognitive science should be more accepting of explanatory plurality, this paper presents the control of fetching movements in the octopus as an exemplar of a cognitive process that comprises distinct and non-redundant representation-using and non-representational elements. Fetching is a type of movement that representational analyses can normally account for completely—but not in the case of the octopus. Instead, a comprehensive account of octopus fetching requires the non-overlapping use of both representational and non-representational explanatory frameworks. What this need for a pluralistic or hybrid explanation implies is that cognitive science should be more open to using both representational and non-representational accounts of cognition, depending on their respective appropriateness to the type of cognition in question.