The Identification of Unfolding Facial Expressions

Abstract

We asked whether the identification of emotional facial expressions (FEs) involves the simultaneous perception of the facial configuration or the detection of emotion-specific diagnostic cues. We recorded at high speed (500 frames s−1) the unfolding of the FE in five actors, each expressing six emotions (anger, surprise, happiness, disgust, fear, sadness). Recordings were coded every 10 frames (20 ms of real time) with the Facial Action Coding System (FACS, Ekman et al 2002, Salt Lake City, UT: Research Nexus eBook) to identify the facial actions contributing to each expression, and their intensity changes over time. Recordings were shown in slow motion (1/20 of recording speed) to one hundred observers in a forced-choice identification task. Participants were asked to identify the emotion during the presentation as soon as they felt confident to do so. Responses were recorded along with the associated response times (RTs). The RT probability density functions for both correct and incorrect responses were correlated with the facial activity during the presentation. There were systematic correlations between facial activities, response probabilities, and RT peaks, and significant differences in RT distributions for correct and incorrect answers. The results show that a reliable response is possible long before the full FE configuration is reached. This suggests that identification is reached by integrating in time individual diagnostic facial actions, and does not require perceiving the full apex configuration.

Facial Motion Engages Predictive Visual Mechanisms

“…This leaves the door open to future research to further investigate if there is a minimum level of dynamic motion necessary or static presentation necessary to elicit this predictive mechanism. Also, although facial stimuli morphs like those used here are common in the emotion processing literature, more ecologically valid stimuli (including seeing actual faces) which entail onset latencies that vary with each facial feature [39] may more dramatically reveal the predictive mechanism shown to be at play here. Also, as discussed above, it is worth considering the potential influence of ethnicity on our results.…”

Emotion perception from a componential perspective

“…Fiorentini et al, 2012). The question of the way in which we recognise emotion expressions and the types of inferences we make when we view them is relevant for research on emotion perception such as emotion processing in the brain.…”

Further evidence of early development of attention to dynamic facial emotions: Reply to Grossmann and Jessen

“…For example, using the Facial Action Coding System (FACS) action units (AUs) description from Ekman, Friesen, and Hager (2002), Fiorentini, Schmidt, and Viviani (2012) identified a number of characteristics present across emotion expressions for their actors, including the temporal sequence of the onset of different AUs and the total number of AUs activated. Across their actors, the lowest number of AUs activated (3) was found for the happy facial expression, whereas the highest number activated (10) was found for the fearful facial expression.…”

“…Similar to Fiorentini and colleagues (2012), the number of AUs required for each emotion in the ADFES varied, with three AUs being necessary for inclusion for happiness and six AUs for fear. In addition to variations in the number and location of activated AUs across emotions, the order of the onset of different AUs resulted in differences in confusability or errors in adults’ identification of emotions in Fiorentini and colleagues (2012). This indicates that the timing of the changes in the face is also important for disentangling complex emotions.…”