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In this study, identical expressions were applied to young and old faces using FaceGen, a state of the art 3D facial modeling software (Singular Inversions, Vancouver, BC, Canada). The effects of aging were thereby examined while holding the underlying expression constant. In this study, young faces were rated as expressing target emotions more intensely, whereas older faces were rated as more emotionally complex (i.e., they had higher ratings across a number of non-target emotions). In other words, the greater Selleck Capmatinib number of emotions present elder faces was associated with a reduced signal clarity for any given target emotion. Neutral old faces were also rated as more emotionally complex, particularly for anger and fear (Hess et al., 2012). These findings are consistent with another earlier study conducted by Malatesta et al. (1987a), in which they asked 14 elderly models to pose 5 different emotions (anger, fear, sad, joy, and neutral). In this study they found that, aside from happy displays, all other photographic stimuli produced high error rates, suggesting again that wrinkles give rise to more complex and negative looking expressions. Notably, even for neutral faces over 60% of labels given represented negative emotions (note there was no ��neutral�� label offered): 15% sadness, 14% contempt, 11% anger 8% fear, 7% disgust, 5% guilt, 4% shame/shyness. Matheson (1997) similarly found that when focused on the perception of pain in the face young adult observers were systematically predisposed to see more Oxygenase pain in the faces of the elderly, including in their neutral faces, again presumably due to misreading aging cues as expressive. One particularly compelling finding in the Malatesta et al. (1987a) study was the correspondence between misperceived emotion displays in elderly faces and the models�� self-reported emotionality. Before posing emotions, the fourteen elderly actors in this study also filled out a Differential Emotion Scale (DES; Izard, 1972) on the same emotions that independent raters later used to label their expressions based on their facial poses (these included, anger, interest, sadness, joy, contempt, disgust, shame/shy, guilt, fear, and surprise). When judges�� mean error rates (i.e., the average IOX1 in vitro error rate for a particular emotion collapsed across all of the actor��s posed expressions) for labeling expressions were examined, they found correlations between specific types of errors and the participants�� own DES scores. For example, judges�� errors for selecting a face as angry predicted participants�� anger scores on the DES, as did sadness, contempt, and guilt. In all, 19 out of 100 correlations conducted were significant, beyond what would be expected by chance alone (i.e., p