In the world of emotion classification, there are two schools of thought. On one end, there are those who subscribe to the idea that all emotions can be boiled down to several basic, discrete emotions, common across all cultures and demographics. On the other end are those who think that emotion is more complex, and that it needs a multi-dimensional model to be explained. Both sides concur that facial expressions are essential indicators of emotion, which is supported by neuroscience1, but they differ in their ideas of how they are implemented.
The big name behind the basicality side is psychologist Dr. Paul Ekman. In the 70’s, he developed the Facial Action Coding System (FACS for short), which breaks down the face into various sections into order to quantify their movements2. From these bits of coding, he suggests that one could infer seven basic emotions: happiness, sadness, surprise, fear, anger, disgust, and contempt. Within each one of these emotions, an intensity level is also observed. His system is founded on the principle of universality, which states that these basic emotions are recognizable cross-culturally.
Ekman laid a foundation for many to follow. With the advancement of computers, it soon became possible to automate this coding process in lieu of having trained human coders. Modern companies like Ekman’s own Emotient (http://emotient.com/) and Affectiva (http://www.affectiva.com/) both offer sophisticated software that can analyze videos recorded by non-scientist users in order to generate information about emotional response.
But Ekman and his basicality buddies are not without their detractors. Many believe there is no universality of facial expression and that cultural differences can be quite different3. James Russell was the first to suggest the circumplex model, which classifies emotion into two dimensions: valence and arousal4. You may remember I talked about these in my last post (http://hcdi.net/blog/view.cfm?bID=39) Later, his work detailed a third dimension: dominance, which is another way of describing approach/avoid response, also discussed in the previous post. Information about emotion classification is gained by pinpointing the reaction in this 3-dimensional space.
Though these approaches are very different from each other, they converge to a single point: a description of human emotion. They each have their own intrinsic advantages, but they are not without disadvantages as well. Basicality theorists can reproduce their studies on large scales with ease, yet the quality of the information is debatable considering several issues. These include criticism over the universality claim, and the lack of transparency as to how these companies’ automation algorithms work, especially when they boast their ability to analyze low quality video.
Multi-dimensional model users can generate very detailed information about distinct emotions unique to each subject. By having more precise and sensitive measures, researchers can differentiate between very similar stimuli.
A good example of this would be a company trying to decide which fragrance to select for their product over several others. Let’s suppose they want their product to have a “relaxing” scent. Traditional survey data may not find that any one of the possible selections is statistically better than the other in matching this concept. Using a multi-dimensional model, researchers can look for statistical significance on the pleasantness and arousal scales to find the fragrance that fits the “relaxing” concept that the company is going for.
One of the drawbacks to this type of study is that large-scale implementation is a bit cumbersome. But this disadvantage is essentially negated by the fact that a sufficient amount of data can be generated by a relatively small participant pool.
In neuromarketing, the method of research must fit the need. If the nature of the study is of large scale, as is the case but ad media testing, then a basic approach to emotional understanding might be adequate. But if a study calls for much a more nuanced and accurate assessment of emotional response in order to unearth the required information, then a more complex means of measurement and interpretation must be applied.
At HCD Research, we are capable of providing this means. We offer a toolbox of methodologies custom-designed to suit the client’s needs, which can range across these two types of descriptions of emotion. By remaining methodologically agnostic, we aim to provide clients with the best methodological fit for their needs.
References:
1Rinn, W. E. “The neuropsychology of facial expression: A review of the neurological and psychological mechanisms for producing facial expressions.” Psychological Bulletin 95.1 (1984): 52-77. Print.
2Ekman, Paul, and Erika L. Rosenberg, eds. What the face reveals: Basic and applied studies of spontaneous expression using the Facial Action Coding System (FACS). Oxford University Press, 1997.
3Jack, R. E. et al. “Facial expressions of emotion are not culturally universal.” PNAS 109.19 (2012): 7241-7244. Print.
4Posner, J., Russell, J. A., and Peterson, B. S. “The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology.” Development and Psychopathology 17.3 (2005): 715-734. Print.
5Matsuda, Yoshi-Taka, et al. "The implicit processing of categorical and dimensional strategies: an fMRI study of facial emotion perception." Frontiers in human neuroscience 7 (2013). [Image edited for clarification]
