Having difficulties reading the facial expression of older individuals? Blame it on the facial muscles, not the wrinkles.

Previous studies have found that it is more difficult to identify an emotional facial expression displayed by an older than a younger face. It is unknown whether this is caused by age-related changes such as wrinkles and folds interfering with perception, or by the aging of facial muscle, potentially reducing the ability of older individuals to display an interpretable expression. To discriminate between these two possibilities, we conducted a psychophysics experiment where participants attempted to identify emotional facial expression under different conditions. To control for the variables (wrinkles/folds vs facial muscles, we made use of Generative Adversarial Networks (GAN) to make images of faces look older or younger. As expected, emotions expressed by older faces (Condition 2) were harder to identify than those expressed by younger faces (Condition 1). Interestingly, participants' accuracy in identifying emotions was not affected when the "young faces" (Condition 1) were artificially aged (Condition 3). On the other hand, using a reverse aging filter to make the older faces (Condition 2) look young (Condition 4) significantly reduced the ability of our participants to identify the correct emotional expression.

Taken together, these results suggest that an age-related decline in ability to produce recognizable facial expressions, rather than the age-related physical changes in the face such as folds and wrinkles, explain why it is more difficult to recognize facial expressions from older faces. Consequently, facial muscle exercises might improve the capacity to convey facial emotional expressions in the elderly.

To promote transparency and repeatability of our manuscript, "Having difficulties reading the facial expression of older individuals? Blame it on the facial muscles, not the wrinkles." currently under review for publications in Frontiers Psychology, we make the following files available: "facedata.xlsx", which contains the raw data collected for our study (400 trials x 28 participants = 11,200 trials) and an expanded table based upon the hierarchical logistic regression analysis (a more limited version will be available in the published manuscript).

For questions about the raw data or table, please contact Nicolas Brunet at brunenm@millsaps.edu