Gender differences in face-based emotion recognition, notably differential use of mimicry, may compromise the extent to which the Simulation of Smiles (SIMS) model can be generalized to populations besides the adult females on which it has been tested. Much work indicates sex differences in face-based emotion recognition, including smile recognition.

The set of 30 stimulating commentaries on our target article helps to define the areas of our initial position that should be reiterated or else made clearer and, more importantly, the ways in which moderators of and extensions to the SIMS can be imagined. In our response, we divide the areas of discussion into (1) a clarification of our meaning of (2) a consideration of our proposed categories of smiles, (3) a reminder about the role of top-down processes in the (...) interpretation of smile meaning in SIMS, (4) an evaluation of the role of eye contact in the interpretation of facial expression of emotion, and (5) an assessment of the possible moderators of the core SIMS model. We end with an appreciation of the proposed extensions to the model, and note that the future of research on the problem of the smile appears to us to be assured. (shrink)

Niedenthal et al's classification of smiles erroneously conflates psychological mechanisms and adaptive functions. This confusion weakens the rationale behind the types of smiles they chose to individuate, and it obfuscates the distinction between the communicative versus denotative nature of smiles and the role of perceived-gaze direction in emotion recognition.

With regard to facial emotion recognition, previous studies found that specific facial regions were attended more in order to identify certain emotions. We investigated whether a preferential search for emotion-specific diagnostic regions could contribute toward the accurate recognition of facial emotions. Twenty-three neurotypical adults performed an emotion recognition task using six basic emotions: anger, disgust, fear, happiness, sadness, and surprise. The participants’ exploration patterns for the faces were measured using the Moving Window Technique (MWT). This technique presented a small window (...) on a blurred face, and the participants explored the face stimuli through a mouse-controlled window in order to recognize the emotions on the face. Our results revealed that when the participants explored the diagnostic regions for each emotion more frequently, the correct recognition of the emotions occurred at a faster rate. To the best of our knowledge, this current study is the first to present evidence that an exploration of emotion-specific diagnostic regions can predict the reaction time of accurate emotion recognition among neurotypical adults. Such findings can be further applied in the evaluation and/or training (regarding emotion recognition functions) of both typically and atypically developing children with emotion recognition difficulties. (shrink)

Understanding the very nature of the smile with an integrative approach and a novel model is a fertile ground for a new theoretical vision and insights. However, from this perspective, I challenge the authors to integrate culture and race in their model, because both factors would impact upon the embodying and decoding of facial expressions.