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Smiling and Laughter: Expressive Patterns
Although smiling and laughter are universal, studying these phenomena is a highly complex process. Unpacking the expressive patterns involved in smiling and laughter helps researchers understand and measure the meaning of the expression being conveyed. For example, when Fredrick Redlich, Jacob Levine, and Theodore Sohler (1951) presented patients with cartoons, they were also interested in the nonverbal responses, which they coded while interacting or from video. Six responses (N = negative response, O = no response, s = halfsmile, S = full smile, C = chuckle, and L = laughter) were identified as important and constituted the “mirth spectrum.” Ordering them according to intensity of response, Edward Zigler, Jacob Levine, and Laurence Gould (1966) created the mirthresponse index. Occasionally, chuckle and negative responses were not included, and often the index ranges from no response (0 points) to half-smile (1 point), smile (2 points), and laughter (3 points), which was assumed by some research as being prototypical and exhaustive.
What was revolutionary 50 years ago is no longer tenable. Negative and positive responses are not on a same dimension and might even blend into one single expression (e.g., disgust jokes). Researchers now doubt that laughter should be scored higher than smiling as they propose that smiling and laughing signify different things and thus should not be included in the same index. Importantly, types of smiles, laughter, and negative responses do matter for a comprehensive understanding, but researchers do not believe that words carry the information. Rather, researchers believe it is important to provide accurate morphological observation, that is, how the laughter or smile is created. Moreover, current assessment tools are used for all body movement or all facial actions and are not restricted to smiling and laughter. This entry discusses how researchers measure and classify smiles and laughter as well as the emotions expressed through smiles and laughter and other nonverbal gestures.
Measurement of Smiles and Laughter
Facial measurement is typically based on facial electromyography (or facial EMG) or coding systems. The former is economic but may be less valid. Surface (or needle) electrodes gather signals generated when muscles contract. A reference electrode is also needed. Advantages include continuous measurement and saving time. While fine-grained intensity may vary, the amplitude is not standardized and not comparable across people, as the strength of the signal is affected not only by degree of muscle contraction but also by factors such as muscle thickness, exact muscle placement, skin thickness, and fat layers. Furthermore, muscles may “cross-talk,” especially when using surface electrodes (e.g., one electrode placed on one muscle may actually pick up the electric activity from the muscle below or next to it).
Coding systems differ in level of sophistication, usually trading how much information they offer for how time consuming they are. The leading tool, the Facial Action Coding System (FACS) significantly updated by Paul Ekman, Wallace V. Friesen, and Joseph C. Hager in 2002, is an anatomically based, comprehensive, objective technique for measuring all observable facial movement, distinguishing 44 action units (AUs). These are the minimal units that are anatomically separate and visually distinguishable. Each AU can be measured in terms of occurrence, intensity (5 steps), symmetry/asymmetry, and duration. To become a certified coder of FACS, 100 hours of instruction and a final test are required. Different variants (baby FACS, chimpanzee FACS) exist. Although automated coding procedures that identify many AUs exist, they do not catch all AUs and are often inaccurate regarding intensity scores. Hence, they are not yet able to replace manual coding.
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