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.     

The evolution of cognition—a hypothesis

Behavior may be controlled by reactive systems. In a reactive system the motor output is exclusively driven by actual sensory input. An alternative solution to control behavior is given by “cognitive” systems capable of planning ahead. To this end the system has to be equipped with some kind of internal world model. A sensible basis of an internal world model might be a model of the system's own body. I show that a reactive system with the ability to control a body of complex geometry requires only a slight reorganization to form a cognitive system. This implies that the assumption that the evolution of cognitive properties requires the introduction of new, additional modules, namely internal world models, is not justified. Rather, these modules may already have existed before the system obtained cognitive properties. Furthermore, I discuss whether the occurrence of such world models may lead to systems having internal perspective.     

Computational enactivism under the free energy principle

In this paper, I argue that enactivism and computationalism—two seemingly incompatible research traditions in modern cognitive science—can be fruitfully reconciled under the framework of the free energy principle (FEP). FEP holds that cognitive systems encode generative models of their niches and cognition can be understood in terms of minimizing the free energy of these models. There are two philosophical interpretations of this picture. A computationalist will argue that as FEP claims that Bayesian inference underpins both perception and action, it entails a concept of cognition as a computational process. An enactivist, on the other hand, will point out that FEP explains cognitive systems as constantly self-organizing to non-equilibrium steady-state. My claim is that these two interpretations are both true at the same time and that they enlighten each other.

     

A mechanistic perspective on canonical neural computation

Although it has been argued that mechanistic explanation is compatible with abstraction (i.e., that there are abstract mechanistic models), there are still doubts about whether mechanism can account for the explanatory power of significant abstract models in computational neuroscience. Chirimuuta has recently claimed that models describing canonical neural computations (CNCs) must be evaluated using a non-mechanistic framework. I defend two claims regarding these models. First, I argue that their prevailing neurocognitive interpretation is mechanistic. Additionally, a criterion recently proposed by Levy and Bechtel to legitimize mechanistic abstract models, and also a criterion proposed by Chirimuuta herself aimed to distinguish between causal and non-causal explanation, can be employed to show why these models are explanatory only under this interpretation (as opposed to a purely mathematical or non-causal interpretation). Second, I argue that mechanism is able to account for the special epistemic achievement implied by CNC models. Canonical neural components contribute to an integrated understanding of different cognitive functions. They make it possible for us to explain these functions by describing different mechanisms constituted by common basic components arranged in different ways.     

The methodological role of mechanistic-computational models in cognitive science

This paper discusses the relevance of models for cognitive science that integrate mechanistic and computational aspects. Its main hypothesis is that a model of a cognitive system is satisfactory and explanatory to the extent that it bridges phenomena at multiple mechanistic levels, such that at least several of these mechanistic levels are shown to implement computational processes. The relevant parts of the computation must be mapped onto distinguishable entities and activities of the mechanism. The ideal is contrasted with two other accounts of modeling in cognitive science. The first has been presented by David Marr in combination with a distinction of “levels of computation”. The second builds on a hierarchy of “mechanistic levels” in the sense of Carl Craver. It is argued that neither of the two accounts secures satisfactory explanations of cognitive systems. The mechanistic-computational ideal can be thought of as resulting from a fusion of Marr’s and Craver’s ideals. It is defended as adequate and plausible in light of scientific practice, and certain metaphysical background assumptions are discussed.

     

The Goldilocks problem and extended cognition

According to the hypothesis of extended cognition (HEC), parts of the extrabodily world can constitute cognitive operations. I argue that the debate over HEC should be framed as a debate over the location and bounds of cognitive systems. The “Goldilocks problem” is how to demarcate these systems in a way that is neither too restrictive nor too permissive. I lay out a view of systems demarcation on which cognitive systems are sets of mechanisms for producing cognitive processes that are bounded by transducers and effectors: structures that turn physical stimuli into representations, and representations into physical effects. I show how the transducer–effector view can stop the problem of uncontrolled cognitive spreading that faces HEC, and illustrate its advantages relative to other views of system individuation. Finally, I argue that demarcating systems by transducers and effectors is not question-begging in the context of a debate over HEC.     

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.     

Situated Action: A Symbolic Interpretation

The congeries of theoretical views collectively referred to as “situated action” (SA) claim that humans and their interactions with the world cannot be understood using symbol-system models and methodology, but only by observing them within real-world contexts or building nonsymbolic models of them. SA claims also that rapid, real-time interaction with a dynamically changing environment is not amenable to symbolic interpretation of the sort espoused by the cognitive science of recent decades. Planning and representation, central to symbolic theories, are claimed to be irrelevant in everyday human activity. We will contest these claims, as well as their proponents' characterizations of the symbol-system viewpoint. We will show that a number of existing symbolic systems perform well in temporally demanding tasks embedded in complex environments, whereas the systems usually regarded as exemplifying SA are thoroughly symbolic (and representational), and, to the extent that they are limited in these respects, have doubtful prospects for extension to complex tasks. As our title suggests, we propose that the goals set forth by the proponents of SA can be attained only within the framework of symbolic systems. The main body of empirical evidence supporting our view resides in the numerous symbol systems constructed in the past 35 years that have successfully simulated broad areas of human cognition.     

Composition and transactive memory systems

A recurrent theme in research on socially distributed cognition is to establish the claim that the cognitive phenomenon of transactive memory is grounded in a specific mode of organization: mechanistic compositional organization. My topic is the confluence of transactive remembering or transactive memory systems (TMSs) and mechanistic compositional organization. In relation to this confluence, the paper scrutinizes the claim that the kind of organization grounding TMSs and/or tokens of transactive remembering takes the specific form of mechanistic compositional organization – at least as the latter is usually construed. It is argued (i) that the usual account of mechanistic compositional organization is based on a synchronic composition function, and (ii) that the organization of TMSs and/or transactive remembering is not well understood by way of synchronic composition. The positive account pursued is that TMSs and/or transactive remembering are better understood as grounded in a diachronic composition function.     

A neuro-cognitive defense of the unified self

The topic of the self remains one of considerable controversy, and many arguments have been put forth suggesting the intuitive concept of self must be in some way mistaken – in part based on results in the cognitive and neural sciences. In this article I offer the alternative positive proposal that “the self” may indeed refer to a physical/computational system within the brain. To do this, I draw on empirical work regarding the neural basis of consciousness and decision-making, and on philosophical work regarding ecological control, unified group perspectives, and functional/mechanistic explanation. The work I review jointly supports the conclusion that a “core-circuit” of interacting cortical regions – the global workspace network – can be understood as a unified system for consciously perceiving and deciding, and thus fulfills many of the roles intuitively assigned to the self. I conclude that this self-concept need not be mistaken given current empirical knowledge.     

Behavior systems and reinforcement: an integrative approach.

Most traditional conceptions of reinforcement are based on a simple causal model in which responding is strengthened by the presentation of a reinforcer. I argue that reinforcement is better viewed as the outcome of constraint of a functioning causal system comprised of multiple interrelated causal sequences, complex linkages between causes and effects, and a set of initial conditions. Using a simplified system conception of the reinforcement situation, I review the similarities and drawbacks of traditional reinforcement models and analyze the recent contributions of cognitive, regulatory, and ecological approaches. Finally, I show how the concept of behavior systems can begin to incorporate both traditional and recent conceptions of reinforcement in an integrative approach.     

What are symbols symbols of? Situated action,mythological bootstrapping and the emergence of the Self

This paper addresses the question of how symbols should be understood in analytical psychology and psychoanalysis. The point of view examined focuses on the recent turn to more cognitive and developmental models in both disciplines and briefly reviews and critiques the evolutionary and cognitive arguments. The paper then presents an argument based on dynamic systems theory in which no pre-existing template or structure for either mind or behaviour is assumed. Within the dynamic systems model the Self is viewed as an emergent phenomenon deriving from the dynamic patterns existing in a complex system that includes the physiological characteristics of the infant, the intentional attributions of the caregiver and the cultural or symbolic resources that constitute the environment. The symbol can then be seen as a discrete, and in important ways an autonomous, element in the dynamic system. Conclusions are drawn for further research into the nature of the symbol with implications for both theory and practice in analytical psychology and psychoanalysis.     

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.     

Mechanisms of developmental change in infant categorization

Computational models are tools for testing mechanistic theories of learning and development. Formal models allow us to instantiate theories of cognitive development in computer simulations. Model behavior can then be compared to real performance. Connectionist models, loosely based on neural information processing, have been successful in capturing a range of developmental phenomena, in particular on-line within-task category learning by young infants. Here we describe two new models. One demonstrates how age dependent changes in neural receptive field sizes can explain observed changes in on-line category learning between 3 and 10 months of age. The other aims to reconcile two conflicting views of infant categorization by focusing on the different task requirements of preferential looking and manual exploration studies. A dual-memory hypothesis posits that within-task category learning that drives looking time behaviors is based on a fast-learning memory system, whereas categorization based on background experience and assessed by paradigms requiring complex motor behavior relies on a second, slow-learning system. The models demonstrate how emphasizing the mechanistic causes of behaviors leads to discovery of deeper, more explanatory accounts of learning and development.     

Understanding implicit memory. A cognitive neuroscience approach.

Dissociations between implicit and explicit memory have attracted considerable attention in recent memory research. A central issue concerns whether such dissociations require the postulation of separate memory systems or are best understood in terms of different processes operating within a single system. This article presents a cognitive neuroscience approach to implicit memory in general and the systems-processes debate in particular, which draws on evidence from research with brain-damaged patients, neuroimaging techniques, and nonhuman primates. The article illustrates how a cognitive neuroscience orientation can help to supply a basis for postulating memory systems, can provide useful constraints for processing views, and can encourage the use of research strategies that the author refers to as cross-domain hypothesis testing and cross-domain hypothesis generation, respectively. The cognitive neuroscience orientation suggests a complementary role for multiple systems and processing approaches.     

Twelve issues for cognitive science

I am struck by how little is known about so much of cognition. One goal of this paper is to argue for the need to consider a rich set of interlocking issues in the study of cognition. Mainstream work in cognition—including my own—ignores many critical aspects of animate cognitive systems. Perhaps one reason that existing theories say so little relevant to real world activities is the neglect of social and cultural factors, of emotion, and of the major points that distinguish an animate cognitive system from an artificial one: the need to survive, to regulate its own operation, to maintain itself, to exist in the environment, to change from a small, uneducated, immature system to an adult, developed, knowledgeable one. Human cognition is not the same as artificial cognition, if only because the human organism must also be concerned with the problems of life, of development, of survival. There must be a regulatory system that interacts with the cognitive component. And it may well be that it is the cognitive component that is subservient, evolved primarily for the benefit of the regulatory system, working through the emotions, through affect. I argue that several concepts must become fundamental parts of the study of cognition, including the roles of culture, of social interaction, of emotions, and of motivation. I argue that there are at least 12 issues that should comprise the study of cognition, and thereby, the field of Cognitive Science. We need to study a wide variety of behavior before we can hope to understand a single class. Cognitive scientists as a whole ought to make more use of evidence from the neurosciences, from brain damage and mental illness, from cognitive sociology and anthropology, and from clinical studies of the human. These must be accompanied, of course, with the study of language, of the psychological aspects of human processing structures, and of artificially intelligent mechanisms. The study of Cognitive Science requires a complex interaction among different issues of concern, an interaction that will not be properly understood until all parts are understood, with no part independent of the others, the whole requiring the parts, and the parts the whole.     

Domain-creating constraints

The contributions to this special issue on cognitive development collectively propose ways in which learning involves developing constraints that shape subsequent learning. A learning system must be constrained to learn efficiently, but some of these constraints are themselves learnable. To know how something will behave, a learner must know what kind of thing it is. Although this has led previous researchers to argue for domain-specific constraints that are tied to different kinds/domains, an exciting possibility is that kinds/domains themselves can be learned. General cognitive constraints, when combined with rich inputs, can establish domains, rather than these domains necessarily preexisting prior to learning. Knowledge is structured and richly differentiated, but its "skeleton" must not always be preestablished. Instead, the skeleton may be adapted to fit patterns of co-occurrence, task requirements, and goals. Finally, we argue that for models of development to demonstrate genuine cognitive novelty, it will be helpful for them to move beyond highly preprocessed and symbolic encodings that limit flexibility. We consider two physical models that learn to make tone discriminations. They are mechanistic models that preserve rich spatial, perceptual, dynamic, and concrete information, allowing them to form surprising new classes of hypotheses and encodings.     

“Looking Up” and “Looking Down”: On the Dual Character of Mechanistic Explanations

Mechanistic explanation is at present the received view of scientific explanation. One of its central features is the idea that mechanistic explanations are both “downward looking” and “upward looking”: they explain by offering information about the internal constitution of the mechanism as well as the larger environment in which the mechanism is situated. That is, they offer both constitutive and contextual explanatory information. Adequate mechanistic explanations, on this view, accommodate the full range of explanatory factors both “above” and “below” the target phenomenon. The aim of this paper is to demonstrate that mechanistic explanation cannot furnish both constitutive and contextual information simultaneously, because these are different types of explanation with distinctly different aims. Claims that they can, I argue, depend on several intertwined confusions concerning the nature of explanation. Particularly, such claims tend to conflate mechanistic and functional explanation, which I argue ought to be understood as distinct. Conflating them threatens to oversell the explanatory power of mechanisms and obscures the means by which they explain. I offer two broad reasons in favor of distinguishing mechanistic and functional explanation: the first concerns the direction of explanation of each, and the second concerns the type of questions to which these explanations offer answers. I suggest an alternative picture on which mechanistic explanation is understood as fundamentally constitutive, and according to which an adequate understanding of a phenomenon typically requires supplementing the mechanistic explanation with a functional explanation.     

Practical Tools and Guidelines for Exploring and Fitting Linear and Nonlinear Dynamical Systems Models

A dynamical system is a system of variables that show some regularity in how they evolve over time. Change concepts described in most dynamical systems models are by no means novel to social and behavioral scientists, but most applications of dynamic modeling techniques in these disciplines are grounded on a narrow subset of—typically linear—theories of change. I provide practical guidelines, recommendations, and software code for exploring and fitting dynamical systems models with linear and nonlinear change functions in the context of four illustrative examples. Cautionary notes, challenges, and unresolved issues in utilizing these techniques are discussed.     

Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition

The prominence of Bayesian modeling of cognition has increased recently largely because of mathematical advances in specifying and deriving predictions from complex probabilistic models. Much of this research aims to demonstrate that cognitive behavior can be explained from rational principles alone, without recourse to psychological or neurological processes and representations. We note commonalities between this rational approach and other movements in psychology - namely, Behaviorism and evolutionary psychology - that set aside mechanistic explanations or make use of optimality assumptions. Through these comparisons, we identify a number of challenges that limit the rational program's potential contribution to psychological theory. Specifically, rational Bayesian models are significantly unconstrained, both because they are uninformed by a wide range of process-level data and because their assumptions about the environment are generally not grounded in empirical measurement. The psychological implications of most Bayesian models are also unclear. Bayesian inference itself is conceptually trivial, but strong assumptions are often embedded in the hypothesis sets and the approximation algorithms used to derive model predictions, without a clear delineation between psychological commitments and implementational details. Comparing multiple Bayesian models of the same task is rare, as is the realization that many Bayesian models recapitulate existing (mechanistic level) theories. Despite the expressive power of current Bayesian models, we argue they must be developed in conjunction with mechanistic considerations to offer substantive explanations of cognition. We lay out several means for such an integration, which take into account the representations on which Bayesian inference operates, as well as the algorithms and heuristics that carry it out. We argue this unification will better facilitate lasting contributions to psychological theory, avoiding the pitfalls that have plagued previous theoretical movements.