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 DOI: 10.1177/1754073910372687

2010 2: 292Emotion ReviewJames A. Coan

What We Talk About When We Talk About Emotion  

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Emotion Review Vol. 2, No. 3 (July 2010) 292–293

© 2010 SAGE Publications andThe International Society for Research on Emotion ISSN 1754-0739 DOI: 10.1177/1754073910372687er.sagepub.com

What We Talk About When We Talk About Emotion

James A. CoanDepartment of Psychology, University of Virginia, USA

Abstract

In this article I respond to commentaries of my review of latent versus emergent variable models of emotion. I note that Ross Buck’s view of emo-tion as stated in his commentary largely endorses an emergent variable model. Drawing from Dynamical Systems Theory, Camras frames the emer-gent variable model as softly-assembled attractor states. This implies that emotions are “fuzzy sets” of indicators that vary in the degree to which they indicate an emergent emotional state. Calvo offers affective comput-ing as a method of evaluating indicators according to their incremental contribution to predicting human emotional responses. This innovative perspective holds a great deal of promise. Ultimately, I hope that this discussion has contributed to the field's understanding of emotion and emotional processes.

Keywordsaffective computing, development, emergence, emotion

In order to find the real artichoke, we divested it of its leaves.Wittgenstein (1953/2009, p. 164)

In proposing the emergent variable model of emotion—and explicitly distinguishing it from the latent variable model—my main goal was to clarify the methodological assumptions about emotion that are embedded in the theoretical language used to describe it. On the one hand, many theories of emotion either imply or assert an underlying neural structure or circuit that organizes and causes a set of outputs (e.g., expressions, feel-ings, actions, physiological responses) that we call emotional. I aligned this view with the latent variable model, and made explicit the measurement properties such a model assumes. On the other hand, recent theorists have characterized emotions as emergent properties involving the interaction or co-occurrence of multiple dissociable situational, cognitive and evaluative ele-ments. I aligned this view with the emergent variable model, and outlined some of the measurement properties that distinguish it from the latent variable perspective.

Interestingly, Ross Buck’s (2010) view of emotion largely endorses an emergent variable model, even asserting that social and moral emotions “exist at the ecological level,” where they “emerge” from interactions between an organism and its environ-ment. He contrasts emotions per se with primary motivational-emotional systems, or primes, arguing for example that certain primes—panic, stress, and anxiety—create fear in combina-tion with the ecological condition of threat. If he is right, then he is endorsing an emergent variable view. This does not imply that fear does not exist, only that it is an output of the interaction between situational demands and relatively independent constituent processes.

Taken together, the commentaries suggest there is no strong consensus about what the best indicators of emotion are. Citing difficulties researchers have had confirming Differential Emotions Theory (Izard & Malatesta, 1987), Linda Camras (2010) notes that emotional facial expressions do not distinguish emotions in children very well. Moreover, she reminds us that the best indicators of emotion may change over the course of devel-opment. Camras’ discussion of emotions as “softly assembled” attractor states associated with Dynamical Systems Theory is highly compatible with the emergent variable perspective, and brought to mind the notion of fuzzy sets (Zadeh, 1965)—the pos-sibility that a variety of subtly different indicators may occupy varying degrees of “membership” in emergent emotional states.

Rafael Calvo (2010) proposes the very promising possibility that affective computing may assist in determining the optimal indicators of emotion in humans. From Calvo’s perspective, computers may identify the most salient emotion indicators by using them to effectively respond to human emotional needs. If the latent variable model is true, then computers should eventu-ally be able to correctly identify and respond to emotional states in humans by tracking a relatively small number of indicators—perhaps only a single indicator. If additional indicators are necessary, this may be evidence for superadditivity, where indi-cators contribute unique sources of non-random variance in identifying an emotional response. The question, then, is one of

Corresponding author: James A. Coan, Department of Psychology, University of Virginia, 102 Gilmer Hall, PO Box 400400, Charlottesville, VA 22904, USA. Email: jcoan@virginia.edu

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Coan What We Talk About When We Talk About Emotion 293

incremental validity (Sechrest, 1963)—the degree to which each new indicator in the model provides a significant increase in the model’s predictive power.

Calvo’s commentary restates a methodological asymmetry between the latent and emergent variable models. If the indicators do in fact share a common cause, then they must be correlated, because variation in the cause precedes variation in the indicators. This suggests further that a small number of indicators (possibly just one) would suffice for affective computing. However, it is still possible for uncorrelated indicators to simultaneously cause the same outcome by contributing unique sources of variability to that outcome. If true, then the addition of indicators to a predictive model should significantly increase its utility.

Finally, in his commentary, Buck worries with Wittgenstein about the bewitchment of our intelligence by imprecise language, especially (at least in Buck’s case) as applied to solving the problem of what emotions are and how they are best defined. I share his concern. Indeed, my review of the latent and emergent variable models was largely fueled by this concern. It seemed to me that, although the assumptions of the latent variable model were fairly well understood, they were nevertheless deserving of a concrete restatement. Because it has rarely been

explicitly defined, however, the emergent variable model deserved even more attention, both at the level of its own under-lying statistical and methodological properties, and in terms of what makes it so different from the latent variable model. Ultimately, I hope to have contributed modestly to the resolution of which general model is the most viable.

ReferencesBuck, R. (2010). Emotion is an entity at both biological and ecological

levels: The ghost in the machine is language. Emotion Review, 2(3), 286–287.

Calvo, R. A. (2010). Latent and emergent models in affective computing. Emotion Review, 2(3), 288–289.

Camras, L. A. (2010). Emergent ghosts in the developmental machine. Emotion Review, 2(3), 290–291.

Izard, C. E., & Malatesta, C. (1987). Perspectives on emotional develop-ment I: Differential emotions theory of early emotional development. In J. Osofsky (Ed.), Handbook of infant development (2nd ed., pp. 494–554). New York: Wiley.

Sechrest, L. (1963). Incremental validity: A recommendation. Educational and Psychological Measurement, 23, 153–158.

Wittgenstein, L. (2009). Philosophical investigations (4th ed.). Malden, MA: Wiley-Blackwell. (Original work published 1953)

Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353.

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