Luis H. Favela

As with much contemporary philosophical and scientific research, the predominant metaphysics of situatedness is monism, particularly, physicalism. Here, we claim that while monism is the proper metaphysical thesis, empirically-supported theories of situated phenomena require ontological pluralism as well. We defend this position via the example of bird flocks, which are situated systems that exhibit ontologically plural features, namely, component dominance and interaction dominance. The description of these features will illustrate that understanding these phenomena requires a coevolution of conceptual and methodological development. Specifically, ontological features are partially identified and evaluated by way of the analyses applied to them. Both descriptive and normative lessons are drawn. Descriptively, research on bird flocks demonstrate that natural phenomena may not be readily cast via a monistic ontology (e.g., component dominant), even at the same scale of investigation. The normative consequence is that while research on situated systems need not reject metaphysical monism, it ought to begin from a pluralistic position concerning ontology.

The Ecological Brain is the first book of its kind, using complexity science to integrate the seemingly disparate fields of ecological psychology and neuroscience. The book develops a unique framework for unifying investigations and explanations of mind that span brain, body, and environment: the NeuroEcological Nexus Theory (NExT). Beginning with an introduction to the history of the fields, the author provides an assessment of why ecological psychology and neuroscience are commonly viewed as irreconcilable methods for investigating and explaining cognition, intelligent behavior, and the systems that realize them. The book then progresses to its central aim: presenting a unified investigative and explanatory framework offering concepts, methods, and theories applicable across neural and ecological scales of investigation. By combining the core principles of ecological psychology, neural population dynamics, and synergetics under a unified complexity science approach, NExT offers a compressive investigative framework to explain and understand neural, bodily, and environmental contributions to perception-action and other forms of intelligent behavior and thought. The book progresses the conversation around the role of brains in ecological psychology, as well as bodies and environments in neuroscience. It is essential reading for all students of ecological psychology, perception, cognitive sciences and neuroscience, as well as anyone interested in the history and philosophy of the brain/mind sciences and their state-of-the-art methods and theories.

Complexity science is an investigative framework that stems from a number of tried and tested disciplines—including systems theory, nonlinear dynamical systems theory, and synergetics—and extends a common set of concepts, methods, and principles to understand how natural systems operate. By quantitatively employing concepts, such as emergence, nonlinearity, and self‐organization, complexity science offers a way to understand the structures and operations of natural cognitive systems in a manner that is conceptually compelling and mathematically rigorous. Thus, complexity science both transforms understandings of cognition and reframes more traditional approaches. Consequently, if cognitive systems are indeed complex systems, then cognitive science ought to consider complexity science as a centerpiece of the discipline.

Although there is a substantial philosophical literature on dynamical systems theory in the cognitive sciences, the same is not the case for neuroscience. This paper attempts to motivate increased discussion via a set of overlapping issues. The first aim is primarily historical and is to demonstrate that dynamical systems theory is currently experiencing a renaissance in neuroscience. Although dynamical concepts and methods are becoming increasingly popular in contemporary neuroscience, the general approach should not be viewed as something entirely new to neuroscience. Instead, it is more appropriate to view the current developments as making central again approaches that facilitated some of neuroscience’s most significant early achievements, namely, the Hodgkin–Huxley and FitzHugh–Nagumo models. The second aim is primarily critical and defends a version of the “dynamical hypothesis” in neuroscience. Whereas the original version centered on defending a noncomputational and nonrepresentational account of cognition, the version I have in mind is broader and includes both cognition and the neural systems that realize it as well. In view of that, I discuss research on motor control as a paradigmatic example demonstrating that the concepts and methods of dynamical systems theory are increasingly and successfully being applied to neural systems in contemporary neuroscience. More significantly, such applications are motivating a stronger metaphysical claim, that is, understanding neural systems as being dynamical systems, which includes not requiring appeal to representations to explain or understand those phenomena. Taken together, the historical claim and the critical claim demonstrate that the dynamical hypothesis is undergoing a renaissance in contemporary neuroscience.

Although there is a substantial philosophical literature on dynamical systems theory in the cognitive sciences, the same is not the case for neuroscience. This paper attempts to motivate increased discussion via a set of overlapping issues. The first aim is primarily historical and is to demonstrate that dynamical systems theory is currently experiencing a renaissance in neuroscience. Although dynamical concepts and methods are becoming increasingly popular in contemporary neuroscience, the general approach should not be viewed as something entirely new to neuroscience. Instead, it is more appropriate to view the current developments as making central again approaches that facilitated some of neuroscience’s most significant early achievements, namely, the Hodgkin–Huxley and FitzHugh–Nagumo models. The second aim is primarily critical and defends a version of the “dynamical hypothesis” in neuroscience. Whereas the original version centered on defending a noncomputational and nonrepresentational account of cognition, the version I have in mind is broader and includes both cognition and the neural systems that realize it as well. In view of that, I discuss research on motor control as a paradigmatic example demonstrating that the concepts and methods of dynamical systems theory are increasingly and successfully being applied to neural systems in contemporary neuroscience. More significantly, such applications are motivating a stronger metaphysical claim, that is, understanding neural systems as being dynamical systems, which includes not requiring appeal to representations to explain or understand those phenomena. Taken together, the historical claim and the critical claim demonstrate that the dynamical hypothesis is undergoing a renaissance in contemporary neuroscience.

Dynamical systems theory (DST) is a branch of mathematics that assesses abstract or physical systems that change over time. It has a quantitative part (mathematical equations) and a related qualitative part (plotting equations in a state space). Nonlinear dynamical systems theory applies the same tools in research involving phenomena such as chaos and hysteresis. These approaches have provided different ways of investigating and understanding cognitive systems in cognitive science and neuroscience. The ‘dynamical hypothesis’ claims that cognition is and can be understood as dynamical systems. Common consequences for such an approach include rejecting understanding cognition as information processing in nature, including eschewing explanatory roles for computation or representation. Contemporary applications of DST include mouse‐tracking studies in cognitive science and nonrepresentational perspectives on motor control in neuroscience. Such work has philosophical implications concerning the boundaries of cognition, explanation, and representations. DST offers powerful methodology and theories that raise many topics of philosophical significance.

I claim that the integrated information theory of consciousness is a complexity science approach to consciousness. In general, complexity science investigates phenomena comprised of numerous non-linearly interacting parts, where global-scale behaviour and structures are irreducible to activities and properties of its constituent parts at local scales. I draw attention to some key features of IIT and complexity science in order to defend the claim that IIT is properly understood within the broader theoretical framework of complexity science. Doing so has the advantage of making IIT an even more compelling theory of consciousness, which has the added benefit of strengthening its ability to respond to some common criticisms.

I defend the claim that emergence is always a kind of interaction dominance. I utilize Francescotti’s definition of emergence, which captures five features typically thought crucial for emergence: downward causal influence, novelty, relationality, supervenience, and unpredictability. I then explicate interaction dominance, a concept from complexity science. In short, a system is interaction dominant when the interactions of the parts give rise to features that override the features of the parts in isolation or linked via additive and linear dynamics. Locust swarms are presented as an illustrative case of a natural phenomenon that meets the definition for emergent properties. Moreover, locust swarms provide a case of an emergent property arising via interaction-dominant dynamics. I conclude by discussing the relationship of emergence and interaction dominance, with emphasis on the claim that all emergent properties occur due to interaction dominance, but not all systems that exhibit interaction-dominant dynamics have emergent properties.

Embodied cognition is a well-established and increasingly influential branch of the cognitive, neural, and psychological sciences. Unlike embodied cognition, extended cognition is not as well-established or influential. Our goal is to defend the idea that if cognition is truly embodied, then it is embodied in systems, and if it is embodied in systems, then it extends beyond animal boundaries. In order to demonstrate this, we situate the idea of extended cognitive systems in a historical context. Then, we present a theoretical and methodological framework for investigating extended cognitive systems. Finally, we discuss some potential experimental work that could adjudicate the existence of extended cognitive systems.

Several philosophers have expressed concerns with some recent uses of the term ‘cognition’. Underlying a number of these concerns are claims that cognition is only located in the brain and that no compelling case has been made to use ‘cognition’ in any way other than as a cause of behavior that is representational in nature. These concerns center on two primary misapprehensions: First, that some adherents of dynamical cognitive science think DCS implies the thesis of extended cognition and the rejection of representation, and second, that cognition is mistakenly equated with behavior. We make three points in response to these claims: First, there is no thoroughly entrenched conception of cognition as distinct from behavior that is being illegitimately disregarded. Second, we present Shapiro’s exposition of dynamical systems theory as revealing a misunderstanding of the way that dynamical models are used in explanations of cognition and related phenomena. Accordingly, a proper conception of DCS’s methods facilitates an appreciation of extended cognition as a legitimate phenomenon of scientific investigation. Finally, we demonstrate that practicing cognitive scientists and psychologists are far more pluralistic in the phenomena they apply ‘cognition’ to than is suggested by some. At the heart of our disagreement with these concerned folks is that although we think it likely that some cognitive phenomena are representational, non-extended, and only in-between the ears, we also think there are good conceptual and empirical reasons to believe that many cognitive phenomena are non-representational, extended, and not confined to the brain.

Karl Popper (2002) once instructed a group of physics students to carefully write down what they observed. Popper relates that the students asked what he wanted them to observe and said that the sole instruction to “observe” was absurd. This story motivated Popper’s claim that, especially in science: Observation is always selective. It needs a chosen object, a definite task, an interest, a point of view, a problem. And its description presupposes a descriptive language . . . , which in its turn presupposes interests, points of view, and problems. (2002, p. 62) In Discovering the Human Connectome, the problem is how the brain works, and from Olaf Sporns’ point of view, the brain can be understood as a network (p. ix). One of Sporns’ main goals in his previous book (2011) was to introduce neuroscientists to the theory and methods of network science. That book was Sporns’ attempt to answer the “how?” questions of integrating the methods of network science with neuroscience. The current book builds on the previous one and attempts to answer more theoretical questions such as why network science is an appropriate framework for investigating the brain.

Mechanistic frameworks of investigation and explanation dominate the cognitive, neural, and psychological sciences. In this dissertation, I argue that mechanistic frameworks cannot, in principle, explain some kinds of cognition. In its place, I argue that complexity science has methods and theories more appropriate for investigating and explaining some cognitive phenomena. I begin with an examination of the term 'cognition.' I defend the idea that "cognition" has been a moving target of investigation in the relevant sciences. As such it is not historically true that there has been a thoroughly entrenched and agreed upon conception of "cognition." Next, I take up mechanistic frameworks. Although 'mechanism' is an umbrella term for a set of loosely related characteristics, there are common features: linearity, localization, and component dominance. I then describe complexity science, with emphasis on its utilization of dynamical systems modeling. Next, I discuss two phenomena that typically fall under the purview of complexity science: nonlinearity and interaction dominance. A complexity science framework guided by the theory of self-organized criticality and utilizing the methods of dynamical systems modeling can surmount a number of challenges that face mechanistic frameworks when investigating some kinds of cognition. The first challenge is epistemic and concerns the inadequacy of mechanistic frameworks to facilitate the comprehensibility of massive amounts of data across various scales and areas of inquiry. I argue that complexity science is more appropriate for making big data comprehensible when investigating cognition, particularly across disciplines. I demonstrate this via an approach called nested dynamical modeling (NDM). NDM can facilitate comprehensibility of large amounts of data obtained from various scales of investigation by eliminating irrelevant degrees of freedom of that system as relates to the target of investigation. The second shortcoming concerns ontological blind spots within mechanistic frameworks. Cognitive phenomena like extended cognition often fail to meet most, if not all, of the criteria assumed by many mechanistic approaches, especially component dominance. I argue that research guided by the notion of interaction dominance allows for extended cognition to be a real, empirically supported phenomenon within complex systems frameworks. In this chapter I discuss some of my experimental work on extended cognitive systems. The search for mechanisms can be a reasonable starting position when attempting to explain natural phenomena in the life sciences. However, too strict of an adherence to theoretical and methodological commitments such as linearity, localization, and component dominance can result in intractable epistemic challenges and ontological blind spots. Complexity science has theories and methods to overcome such challenges in the investigation of cognition.

It is becoming ever more accepted that investigations of mind span the brain, body, and environment. To broaden the scope of what is relevant in such investigations is to increase the amount of data scientists must reckon with. Thus, a major challenge facing scientists who study the mind is how to make big data intelligible both within and between fields. One way to face this challenge is to structure the data within a framework and to make it intelligible by means of a common theory. Radical embodied cognitive neuroscience can function as such a framework, with dynamical systems theory as its methodology, and self-organized criticality as its theory.

Individuals make decisions under uncertainty every day based on incomplete information concerning the potential outcome of the choice or chance levels. The choices individuals make often deviate from the rational or mathematically objective solution. Accordingly, the dynamics of human decision-making are difficult to capture using conventional, linear mathematical models. Here, we present data from a two-choice task with variable risk between sure loss and risky loss to illustrate how a simple nonlinear dynamical system can be employed to capture the dynamics of human decision-making under uncertainty (i.e., multi-stability, bifurcations). We test the feasibility of this model quantitatively and demonstrate how the model can account for up to 86% of the observed choice behavior. The implications of using dynamical models for explaining the nonlinear complexities of human decision-making are discussed, as well as the degree to which nonlinear dynamical systems theory might offer an alternative framework for understanding human decision-making processes.

The importance of touch to mammalian survival and well-being cannot be overstated. The capacity for action depends on the sense of touch, which is a necessary feature of an animal’s being-in-the-world (O’Shaughnessy, 1989, pp. 38–39). Interpersonal touch has been shown to be an important part of human welfare, including disease prevention and treatment (see Field, 2001 for review). Throughout a mammal’s lifespan, social relation- ships are also mediated by touch behavior (see Thayer, 1986 for review). Given these facts, the sense of touch is relevant to a variety of topics in psychology, including but not limited to: perception, action, nonverbal behavior, relationships, development, emotion, and health.

Network theory arguably has its origins in Euler’s (1741) graph theory, which was first developed in the mid-1700s to solve the Königsberg bridge problem. Since then, the basic units of graph theory—vertices and edges—have been utilized by a number of scientific disciplines to describe and analyze a wide variety of phenomena. Mark Newman begins his clear and comprehensive introduction to networks with a sampling of various kinds that have been studied: information networks such as the World Wide Web, biological networks such as neural connections, and social networks such as friendships among members of a club. As Newman highlights, network theory and methods are utilized not only within but also across scientific disciplines. The interdisciplinary nature of the scientific study of networks is a double-edged sword. On the one hand, it fosters the cross-pollination of ideas and methods across fields—for example, applying methods used to study computer networks to the investigation of brain networks (cf., Sporns, 2011). On the other hand, such interdisciplinarity can result in confusion and misapplication of theory and methods. Accordingly, Newman’s goal in “Networks: An Introduction” is to synthesize the current state of network theory into a “consistent language and notation” (2010, p. x, Preface). This is no easy task, but Newman gives an admirable attempt that is successful in many ways.

Individuals make decisions under uncertainty every day. Decisions are based on in- complete information concerning the potential outcome or the predicted likelihood with which events occur. In addition, individuals’ choices often deviate from the rational or mathematically objective solution. Accordingly, the dynamics of human decision making are difficult to capture using conventional, linear mathematical models. Here, we present data from a 2-choice task with variable risk between sure loss and risky loss to illustrate how a simple nonlinear dynamical system can be employed to capture the dynamics of human decision making under uncertainty (i.e., multistability, bifurcations). We test the feasibility of this model quantitatively and demonstrate how the model can account for up to 86% of the observed choice behavior. The implications of using dynamical models for explaining the nonlin- ear complexities of human decision making are discussed as well as the degree to which the theory of nonlinear dynamical systems might offer an alternative framework for understanding human decision making processes.

Consciousness has traditionally been the subject matter of philosophy. However, especially in recent years, various branches of science have attempted to develop theories of consciousness. I evaluate the biological theories of Francis Crick, Gerald Edelman, and Antti Revonsuo in order to gauge the current state of biological accounts of consciousness. I begin with an explication of the easy and hard problems of consciousness as defined by David Chalmers. Next, I summarize how each theory defines ‘consciousness’ and then I evaluate each theoretical model. Finally, I evaluate the models in terms of the easy problems and, especially, the hard problem. My aim in this work is to demonstrate that the current biological theories of consciousness are inadequate. This claim is not tantamount to one that claims that the theories are ineffective. On the contrary, these theories tend to be more useful than not. However, these biological models do not address the most essential component of consciousness that is subjective phenomenological experience.

Ecological psychology (see Gibson, 1979) is generally thought of as comprising two main claims. The first is that perception is direct insofar as it is not the result of information added to sensory representations. The second is that perception is comprised of affordances (at least most of the time) or opportunities for action that exist in the environment. Barrett explores the possibility of giving an objective account of perceiving religious meaning and value by means of ecological psychology. The attempt to utilize ecological psychology to account for values is not without precedent, however. Jayawickreme and Chemero (2008), for instance, used the ecological concept of ‘‘affordance’’ to sketch an account of both virtues and morally relevant situations. Surprisingly, Barrett never mentions this central ecological concept, choosing instead to focus solely on the directness of perception. We believe that this constricts his ecological account of religious value. While we agree with Barrett that the cognitive science of religion treats presence as an insider’s experience, such that religious experience is a ‘‘black box’’ phenomenon, intractable, and mysterious, and that ecological theory might provide an account of values, we are not convinced by his particular attempt at an ecological account of religious meaning.