Exploring the Underlying Mechanisms of Spontaneous and Voluntary Facial Expressions
Facial expressions can be the physiological outcome of an internal emotional state (i.e., spontaneous productions), or consciously controlled displays used for social communication purposes (i.e., voluntary displays; Gordon et al., 2014). Previous research has shown that individuals with ASD are less spontaneously expressive (McIntosh et al., 2006) contributing to ‘flat affect’, and are more likely to produce confusing or inaccurate facial expressions (Brewer et al., 2016) impairing social interaction quality.
The goal of this study is to determine whether reduced spontaneous expression and inaccurate voluntary expression have distinct underlying mechanisms. The prediction is that reduced spontaneous expression will be better explained by alexithymia than ASD traits because interoceptive difficulties (poor awareness of one’s internal affective cues) will impair proprioceptive processes (i.e., automatic facial muscle movements). In contrast, inaccurate voluntary expression will be better explained by ASD traits than alexithymia because voluntary nonverbal expression represents the core social communication difficulties characteristic of ASD.
This study examined ASD traits and alexithymia using the Autism Spectrum Quotient (AQ) and the Toronto Alexithymia Scalef (TAS-20) in a sample of neurotypical undergraduates to predict variance in spontaneous and voluntary expressions. Spontaneous expressions were assessed in response to participants telling emotional stories about their lives, or from watching emotional video clips. Voluntary expressions were assessed by asking participants to pose various emotional facial expressions. Facial expressions were analyzed using iMotions software.
Spontaneous expression was calculated as the total amount of emotional expression detected while telling a negative story, or while watching the movie clips. A hierarchical multiple regression with quantity of negative expression entered as the dependent variable was conducted with AQ entered into the first step, and TAS-20 entered into the second step. The model approached significance in step 1, F Change(2,39) = 3.84, p = .057, accounting for 9.0% of the variance (R2 Change = .090). Upon entering TAS-20 into the regression in step 2, the model was significant, F Change(2,39) = 14.11, p < .001, adding 24.7% explained variance (R2 Change = .247).
Voluntary expression accuracy was calculated dichotomously based on whether the target emotion was judged by iMotions as being the dominant emotion. A hierarchical multiple regression with expression accuracy entered as the dependent variable was conducted with TAS-20 entered into the regression in step 1 and AQ entered in step 2. The model approached significance with TAS-20 entered into the model in step 1, F Change(2,37) = 2.164, p = .150, and accounted for 5.7% of the variance (R2 Change = .057). Upon entering AQ into the regression in step 2, the model became significant, F Change(2,37) = 6.38, p = .016, adding 14.5% explained variance (R2 Change = .145).
As predicted, alexithymia is more strongly related to spontaneous expression of emotion than ASD traits, whereas ASD traits are more strongly related to voluntary expression. Research is needed to replicate this study in individuals with ASD to determine whether “flat affect” is a symptom of alexithymia, and “confusing expression” is a symptom of ASD.