The dynamical renaissance in 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.

The Dynamics of Cognitive Performance: What Has Been Learnt from Empirical Research in Science Education

This paper discusses investigations in science education addressing the nonlinear dynamical hypothesis. Learning science is a suitable field for applying interdisciplinary research and predominately for testing psychological theories. It was demonstrated that in this area the paradigm of complexity and nonlinear dynamics have offered theoretical advances and better interpretations of empirical data. Research showed that besides linear modes of behavior, sudden transitions occur in cognitive performance and this has questioned basic theoretical and epistemological assumptions. The neo-Piagetian framework and motivational theories offering constructs for serving as predictors in various model are the local theories which are embraced by the CDS meta-theory. Sudden transitions are modeled by catastrophe theory (CT) the analyses of which reveal the crucial role of certain variables, namely the bifurcation factors. Beyond a critical value of the bifurcation factor, the state variable splits into two-attractor regions and becomes bimodal. The bifurcation effect induces uncertainty and unpredictability in the system, which oscillates between two states entering the regime of chaos. Then in state variables such as learning outcomes and achievement, sudden transitions from success to failure are expected. Catastrophe theory explains unexpected phenomena associated with school failure, dropouts, illicit behaviors, sudden attitude change, and creativity. Moreover CT could contribute in elucidating theoretical debates and conflicting empirical evidences.

Bifurcation and Hysteresis Effects in Student Performance: The Signature of Complexity and Chaos in Educational Research

This paper addresses some methodological issues concerning traditional linear approaches and shows the need for a paradigm shift in education research towards the Complexity and Nonlinear Dynamical Systems (NDS) framework. It presents a quantitative piece of research aiming to test the nonlinear dynamical hypothesis in education. It applies catastrophe theory and demonstrates that students’ achievements in science education could be described by a cusp model, where two cognitive variables are implemented as controls - the logical thinking as the asymmetry and the field dependence/independence as the bifurcation respectively. The results support the nonlinear hypothesis by providing the empirical evidence for bifurcation and hysteresis effects in students’ performance. Interpretation of the model is provided and implications and fundamental epistemological issues are discussed.

What is the dynamical hypothesis?

Van Gelder's specification of the dynamical hypothesis does not improve on previous notions. All three key attributes of dynamical systems apply to Turing machines and are hence too general. However, when a more restricted definition of a dynamical system is adopted, it becomes clear that the dynamical hypothesis is too underspecified to constitute an interesting cognitive claim.

The dynamical hypothesis in cognitive science

According to the dominant computational approach in cognitive science, cognitive agents are digital computers; according to the alternative approach, they are dynamical systems. This target article attempts to articulate and support the dynamical hypothesis. The dynamical hypothesis has two major components: the nature hypothesis (cognitive agents are dynamical systems) and the knowledge hypothesis (cognitive agents can be understood dynamically). A wide range of objections to this hypothesis can be rebutted. The conclusion is that cognitive systems may well be dynamical systems, and only sustained empirical research in cognitive science will determine the extent to which that is true.

Why dynamical implementation matters

Another objection to the dynamical hypothesis is explored. To resolve it completely, one must focus more directly on an area not emphasized in van Gelder's discussion: the contributions of dynamical systems theory to understanding how cognition is neutrally implemented.

What might dynamical intentionality be, if not computation?

(1) Van Gelder's concession that the dynamical hypothesis is not in opposition to computation in general does not agree well with his anticomputational stance. (2) There are problems with the claim that dynamic systems allow for nonrepresentational aspects of computation in a way in which digital computation cannot. (3) There are two senses of the “cognition is computation” claim and van Gelder argues against only one of them. (4) Dynamical systems as characterized in the target article share problems of universal realizability with formal notions of computation, but differ in that there is no solution available for them. (5) The dynamical hypothesis cannot tell us what cognition is, because instantiating a particular dynamical system is neither necessary nor sufficient for being a cognitive agent.