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3.1 Introduction The best way to conceptualize emotional episodes, I believe, is as dynamical patterns. More precisely, emotional episodes should be understood as selforganizing patterns of the organism, best described with the conceptual tools of dynamical systems theory (DST). DST is a branch of mathematics used to model a variety of complex temporal phenomena. In the mind sciences, it has been used to model a variety of cognitive phenomena. This “dynamical cognitive science,” as Wheeler (2005) aptly calls it, is importantly related to the enactive approach. It can be seen as a precursor of it, with roots in cybernetics (see Pickering 2010), although it became more influential in cognitive science only in the mid-1990s with the publication of works such as Thelen and Smith 1994, Kelso 1995, and Port and van Gelder 1995. The enactive approach takes from dynamical cognitive science the view that cognition is a temporal phenomenon. It is particularly sympathetic to what Thompson (2007, 10) calls embodied dynamicism, namely, the view that cognitive systems are not just temporal but also embodied and situated , involving “multiple simultaneous interactions” of brain, body, and world (van Gelder and Port 1995, 23). Indeed, the very notion of autonomy, which (as we saw in chap. 1) in the enactive approach captures a crucial property of living systems, refers to a network of processes that influence one another over time and in so doing generate and maintain the unity of the network. As we shall see in more detail shortly, this kind of mutually influencing relationship is precisely what can usefully be captured by DST. In addition, the enactive approach has also begun to draw on what I will call, by analogy with dynamical cognitive science, dynamical affective science, namely, the application of dynamical systems concepts to affective 3 Emotional Episodes as Dynamical Patterns 54 Chapter 3 and emotional phenomena (see Thompson 2007, chap. 12; Colombetti 2007; Colombetti and Thompson 2008). The aim of this chapter is, in brief, to focus on this approach in detail, and to show that it provides a valuable alternative to the theories of emotion discussed in the previous chapter. After introducing some fundamental conceptual tools of DST, I distinguish three strands within the dynamical systems approach in affective science, highlight their distinctive claims, and eventually reconstruct a characterization of emotional episodes consistent with all of them. I then move on to spell out the implications that such a characterization has for the debate on the nature of the emotions illustrated in the previous chapter. I also discuss the implications of a dynamical systems characterization of emotional episodes for further issues in affective science, such as the purported discreteness of emotions, and the relationship between emotions and moods. 3.2 Fundamental Concepts of Dynamical Systems Theory (DST) DST is a branch of mathematics that describes the temporal evolution of dynamical systems, namely, systems that change over time.1 What changes over time is called the state of the system. Describing the temporal evolution of a dynamical system requires one first to provide a number of state variables that describe the state of the system at a given time. Next, one needs to provide a rule of evolution that describes how the values of the state variables change over time. When the system changes continuously over time, this rule takes the form of differential equations (systems that evolve in discrete steps do not need differential equations; difference equations will do). Given the state variables of the system, it is possible to represent all possible states of the system geometrically as a state space, such that each point of the state space corresponds to a state of the system. As the system changes its state over time, it moves from one point of the state space to another, tracing a curve in it that is called a trajectory. The set of all possible trajectories of a system is its phase portrait. Points or regions in the state space toward which the system’s trajectories tend to converge are called attractors. When a system is in a region of the state space such that it will evolve into a particular attractor, that region is the attractor’s basin of attraction. Points or regions in the state space from which the system’s trajectories are deflected are called repellors, and the overall layout of attractors and repellors in the state space is referred to as its topology. A bifurcation [18.119.126.80] Project MUSE (2024-04-25 06:41...

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