Synchronization and emergence of feeling

Scherer (2001; 2009b; 2009c) assumes that components of emotion correspond to major organismic substrata. That is, for each emotion component it is assumed that there is a corresponding component subsystem (e.g., a physiology system, an expression system). A key feature of the CPM is that it treats the five component subsystems as a dynamic system (Sander et al., 2005). A dynamic system refers to a system whose parts (e.g., variables, processing units) are interconnected by feedforward and feedback connections. Dynamic systems theory is the branch of mathematics that describes and studies how the output of such systems evolves over time. Large dynamic systems can be characterized by

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multidirectional causality and complex behavior over time (e.g., weather patterns arising from atmospheric interactions). The application of dynamic systems theory to emotion is a relatively recent development in affective science (e.g., Hoeksma et al., 2007; Lewis, 2005; Lewis & Granic, 2000;

Scherer, 2000; 2001; 2009b), although some of its basic principles have been supported without necessarily being named as such. Other emotion theorists have assumed, for example, that the organismic networks involved in emotion are interconnected (e.g., Damasio, 1998; Frijda, 2005;

Koelsch et al., 2015, Ledoux, 2012) and that connectivity between these networks is bi-directional.

The advantage of applying an explicit dynamic systems view is that it allows other concepts of dynamic systems theory to be imported to emotion theory. In the CPM, two such concepts are attractors and emergence, which directly relate to the hypotheses of (a) component synchronization and (b) emergence of feeling (Scherer, 2000; 2001; 2009b).

Figure 4.3. Conceptual illustration of a transient attractor. A dynamic system switches between phases of random activity and a phase with a periodic pattern. Note how the random phase and the pattern phase seem perceptually discrete, that is, qualitatively different states of the system.

A schematic diagram of the CPM as a dynamic system is shown in Figure 4.2. This diagram shows four component subsystems (appraisal, motivation, physiology, and expression) that collectively take on values, or states, over time. Ongoing appraisal of an external event (bottom section) pushes the system through a series of states. Since the underlying system is fully interconnected, the complete state of the system at time t is influenced by the complete state of the system at time t – 1.¹⁸ Another way to put this is that the emotion component subsystems mutually constrain the trajectory of states through time. Due to these constraints, not all states (or trajectories) are equally likely to occur. Some patterns of activity in the emotion components may never occur whereas other patterns may occur very frequently. States of the latter kind are often referred to as attractors in dynamic systems theory because the system will be drawn to visit these patterns—given a

18 Although the diagram does not depict these arrows, cross-influencing of even earlier states could be allowed

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proper impulse. Attractors of a dynamic system can be permanent states (i.e., the activation of the system stays there) or transient trajectories (i.e., sequences of states that the activation of the system passes through). An example of the former kind is a system that reverts to a null state (i.e., all zeroes), regardless of the initial input (e.g., spinning a marble inside a bowl). An example of the latter kind could be any periodic sequence that arises against a permanent background of random activity (Figure 4.3; e.g., a rain episode in an atmospheric dynamic system). Scherer (2000; 2001; 2009b) assumes that emotional episodes are transient attractors of the dynamic system of component subsystems (i.e., appraisal, motivation, physiology, and expression). That is, given an appropriate trigger of one or more appraisals, components of emotion will collectively evolve toward an emotional response.

The concept of an attractor also evokes the notion of an attractor landscape, that is, the full collection of states and attractors that a dynamic system can visit, visualized as a map. A conceptual visualisation of emotional attractors is depicted in Figure 4.4. This map illustrates the state space trajectories that are emotional. Scherer (2000; 2001; 2009b) assumes that trajectories in these regions will exhibit a relatively increased correlation in component activity and a qualitatively distinct component patterning (e.g., a fear or anger pattern of component activity).The strongest attractors in these regions (thickest white lines) are expected to correspond to the most prototypical emotional activity, and the ones most likely to be labeled with an emotion word in language. Scherer (2009b) also refers to these types of emotion episodes as modal emotions.

Note that the attractor view on synchronization in the CPM encompasses two distinct meanings of the term synchronization in the literature. One is the relative increase in correlation¹⁹ or dependence between emotion components, another is the distinct or coherent patterning of one or more emotion components (Bulteel et al., 2013). In the CPM, the synchronization of emotional attractors is taken to imply both component correlation and component patterning. Moreover, Scherer (2000, 2009b) assumes that component patterning should involve all components of emotion simultaneously.²⁰ It is important to emphasize that the model and measure of synchronization that we propose in this paper focuses primarily on the correlational aspect of component synchronization, although it may capture aspects of coherent patterning as well (due to data transformations). These two types of synchronization can but need not necessarily be independent. For example, the stronger two variables

19 Not necessarily linear correlation.

20 Combinations of two or three components (e.g., appraisal and motivation) may well pass through attractor states of their own without recruiting the others.

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are correlated the more constrained (or patterned) combinations of activity between these variables will become.

Figure 4.4. Emotions as attractor trajectories (or loops) in an activity landscape. As the dynamic system of component subsystems cycles through activity, it will be pulled into loops of transient stability (white lines), while tending globally toward a permanent baseline attractor state (white circle). The CPM assumes that emotional episodes correspond to attractor trajectories that involve activity in all components of emotion, with highly synchronized component changes, distinct component patterning, and emergence of feeling (colored areas).

Figure 4.4 clarifies an important aspect about emotional attractors that has not always been rendered explicit in past treatments of the CPM, which is the baseline attractor state (Figure 4.4, solid dot at the bottom). Most emotion theorists agree that emotions are transient (hence the word

“episode”) and that, most of the time, people do not experience strong or prototypical emotions. This implies (a) that there should be a non-emotional baseline attractor that the activity in component subsystems will tend to revert to, and (b) that the baseline attractor should be the strongest attractor in the dynamic system. The baseline attractor could correspond to a state of relative goal fulfilment (e.g., a resting state) where the environment provokes no immediate organisation of emotion components toward an adaptive goal. Only when the dynamic system of component subsystems receives a proper impulse by the appraisal component (Figure 4.2) can the activity of components be directed toward an emotional trajectory (Figure 4.4, red dashed line). In the long run, the activity will always tend to revert back to the baseline attractor, which Scherer (2009b) expects to be a relatively desynchronized

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state.²¹ Indeed, Scherer (1984, 2001, 2009b) has put forward component synchronization as the criterion to separate emotional from non-emotional episode. Two clarifications should be appended to this view. First, the baseline attractor state does not necessarily have to be “reached” by the dynamic system. Since a person cannot be perfectly at rest (i.e., zero activity), the activity in the emotion components may simply orbit around the baseline attractor. Second, although Scherer (1984, 2001, 2009b) assumes that appraisal is the primary trigger and driver (i.e., as a sustaining cause) of component activity toward emotional attractors, this need not always be the case. A dynamic systems view is flexible because it allows other components of emotion to trigger, drive, or disrupt component activity.

The CPM claims that synchronization results in the emergence of a feeling state (Figure 4.2;

Grandjean et al., 2008; Scherer, 2000; 2005; 2009b). The concept of emergence is used in dynamic systems theory to explain how complex wholes can arise solely from interacting parts, for example complex natural structures like tornados, crystals, or bird swarming (Fromm, 2004). In each of these cases, there is no centralized intelligence that controls the formation of the whole. Scherer (2000, 2009b) applies this concept to explain the coherent experience of emotional feelings. He assumes that, when component activity is driven toward an emotional attractor, the degree of synchronization will alter the combined conscious experience of the individual component changes. This experience is expected to be greater than the sum of the parts like a Gestalt (e.g., a coherent fear experience). As noted in Section 4.2.1, other emotion theorists have also considered feeling as an integrated reflection of conscious changes in the other emotion components (e.g., Frijda, 1993). The CPM, feeling also has an explicit temporal dependence, since it is assumed to arise from synchronization, which itself depends on the dynamic trajectory that the component changes traverse (Figure 4.4). Note that the feeling component in Figure 4.2 does not directly influence the other components. Although it is assumed that the feeling component has its own organismic substrata, the content of the feeling is solely a product of the dynamic interaction of the other four components.

Many theories of emotion attempt to account for differentiation among a limited set of discrete emotion categories, most notably the six categories proposed by basic emotion theory (i.e., “joy”,

“surprise”, “fear”, “disgust”, “anger”, “sadness”). This has also been a pursuit of certain appraisal theories, such as the OCC model and Roseman’s theory (see Moors, 2014, for a discussion). The CPM differs from these approaches in that it considers the space of emotional responding to be potentially infinite, and so too the number of possible feeling states. Like dimensional theories, Scherer is less concerned with explaining variation in discrete states but tries to explain the continuous variation resulting from combinations of components. Unlike dimensional theories, however, the CPM does not deny the existence of discrete states as natural categories. Rather, such states are believed to correspond to particularly strong attractor trajectories of the emotion system (Figure 4.4), or so-called

21 Desynchronized in the sense that activity in the components of emotion is relatively independent

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modal emotions. The CPM assumes that, the stronger an attractor trajectory, (a) the more highly synchronized the underlying component changes are, (b) the more likely it is that a feeling emerges, (c) the more likely it is that the feeling is labelled with a (conventional) emotion term (e.g., fear), and (d) the more likely this attractor basin will be revisited by the component changes in the future.

Figure 4.5. A Venn model of component integration and the role of conscious feeling. Circle A = dynamic component changes, Circle B = integrated awareness of component changes, Circle C = effable feelings. Diagram adapted from Scherer (2009b).

Figure 4.5 (adapted from Scherer, 2009b) depicts (a) the relation between dynamic component changes in appraisal, motivation, physiology and expression (circle A), (b) the part of these changes that emerges into feeling (circle B), and (c) the part of feeling that can be verbalized (circle C). These three areas overlap only partially. Owing to the richness and complexity of feelings, some states may require multiple labels to verbalize (e.g., Scherer & Meuleman, 2013), and yet others may be inexpressible with existing labels. Labelling is not a central feature of the CPM, however.

Conventional emotion categories have not appeared in any diagram that we have presented so far.

Attractors can explain how seemingly discrete states could arise from continuous dynamic interactions between emotion components, without resorting to affect programs. Discrete states are transient attractors, trajectories of the dynamic system’s state space that activity can be pushed into and back out of, for example, to a baseline state. Transitioning between attractors happens in a continuous, smooth, fashion. A system can be in the stage of moving through an attractor, but nevertheless appear to be trapped. Figure 4.3 illustrates how activity can switch smoothly between two phases that seem perceptually discrete. Again, it is useful to consider the analogy between emotion categories and categories of the weather. The atmosphere can be considered a dynamic system whose determinants (e.g., wind speed, humidity, atmospheric pressure) interact continuously over time.

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Regularly this system will be pulled into qualitatively distinct patterns, for which human language has developed words like “rain”, “snow”, or “tornados”, and which represent structured phases arising from the same underlying dynamic system. This example also illustrates how a large dynamic system can generate complex phenomena, such as lightning, in a matter of milliseconds. This occurs through the process of feedback and rapid amplification inside the dynamic system.

Summary. The idea that integrated feelings (and hence emotions) could emerge from a dynamic system of distributed and interacting mental and bodily subsystems is not unique to the CPM but has only gradually gained ground in the last decades. Lewis (1996, 2005) has strongly advocated this perspective as an attempt to bridge psychology and neurobiology and to unite scattered subfields in affective science (see the volume by Lewis and Granic, 2000, as well as open peer reviews for Lewis, 2005). Recently, LeDoux (2012) proposed a highly similar framework for understanding emotion, although his theory does not explicitly adopt dynamic systems terminology. Other authors have adopted parts of dynamic systems theory (e.g., feedback relations, nonlinear processes) without elaborating complete dynamic emotion theories of their own (e.g., Carver & Scheier, 1990; Frijda, 1993; Mascolo, Harkins, & Harakal, 2000; Mathews, 1990).

A dynamic systems architecture for an emotion model affords many advantages that are best illustrated by contrasting it to the classical input-output approach. In this approach the output of a model is typically generated in a strictly feedforward fashion, according to a series of ordered computation steps. Much research in affective science has adopted—either implicitly or explicitly—

the input-output approach (Lewis, 2005). Causality in such systems is modelled as unidirectional and usually linear. Time is treated as discrete steps of input to output or averaged out of emotion episodes—if it is represented at all. In a dynamic system, on the other hand, there is no clear input-output distinction. Although the CPM assigns a causal role to the appraisal component, the activity and direction of the component changes can be rapidly overtaken by their internal dynamics. These internal dynamics are characterized by multidirectional causality, allowing simultaneous cross-influencing between components. Note that, in Figure 4.2, values for the four depicted emotion components are updated in a parallel fashion. Again, this stands in contrast to the classical input-output approach, where emotion components would be updated one after the other in a strictly ordered fashion.

These advantages appeal to authors who wish to resolve longstanding conflicts between different families of emotion theories (e.g., discrete versus dimensional perspectives), or even within families (e.g., appraisal as a part versus cause of an emotion). The dynamic systems perspective offers an attractive venue for integration. The challenge that remains is to operationalize these approaches so that they can be applied to concrete research. No study thus far has attempted this. In a series of papers and book chapters, Scherer has extensively advocated computational modelling of the CPM (Wehrle

& Scherer, 2001; Scherer, 2005; 2009b). For that purpose, a potential sketch of an architecture was introduced in Sander et al. (2005). This architecture borrows notation from the field of artificial neural

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networks, with emotion components represented as layers of interconnected computational units. This approached proved to be a useful starting point for the current study.