The CATS consists of 13 subtests: 11 emotion tasks and two control tasks assessing facial identification, emotion matching with and without verbal denotation (e.g., in some tasks both emotional faces and the name of the target emotion is displayed on the screen, whereas in other tasks no additional verbal cues are given), emotional tone or prosodic processing with and without verbal denotation (e.g., in some tasks both emotional prosody and the name of the target emotion is displayed on the screen, whereas in other tasks no additional verbal cues are given), and with conflicting or congruent semantic content. Emotional stimuli covered happy, sad, angry, surprised, disgusted, fearful, or neutral mood.
The different AUs implemented in the software are combinable and can be mobilized on a time frame for the creation of macro and micro expressions and complex animated expressions. The animations and images created have a transparent background. The researchers thus have the freedom to present the modeled faces on a colored background or an image of their choice.
The experimental material consisted of 42 black-and-white images representing faces in front view for the experimental part. Six different avatars were used (three men, three women) with seven images (one neutral and six emotional facial expressions) for each avatar for a total of 42 images. Different avatars were used to avoid the reinforcement process due to multiple exposure of the same emotion with the same face. Seven pictures with the same characteristics were produced for the training part. The use of black and white was justified as an element of comparison with existing databases and in particular the POFA (Ekman, 1976) as well as images from the FACS manual (Ekman et al., 2002). Previous studies have shown that there is no significant difference in recognition or categorization tasks between color and black-and-white images (Amini et al., 2015; Krumhuber et al., 2012). These faces were produced with GIMP and Blender software programs for the texture and the FACSHuman plugin for the modeling of facial expressions.
The experimental part had the same characteristics as the training part. Pictures of the training section were not reused. Each of the 42 images, described in the materials section, were presented at 100% level of intensity as configured in the software in accordance with the FACS definition (Ekman et al., 2002). Images were exposed in random order. The images of the modeled faces were displayed until the category was selected by the participant (happiness, disgust, sadness, fear, anger, surprise, and neutral). The choice of category led to the continuation of the experiment.
The ability to recognise basic facial emotions was evaluated using the Italian version of the Ekman 60-Faces Test65, 66. This task assesses both overall emotion recognition and basic emotion detection. It consists of 60 black and white pictures that portray the faces of 10 actors, each displaying the six basic emotions (happiness, surprise, anger, disgust, fear, and sadness). A computerised sequence of slides containing a facial emotion picture was administered to participants, requiring them to select the label that best described the facial expression in the picture. The labels were visible throughout testing and participants were allowed as much time as they needed to verbally make their selection. No feedback was given throughout testing.
For each of the selected articles, the following variables were determined: type of emotion evaluated (joy, sadness, anger, disgust, fear, surprise), specifically identifying the articles that evaluated the six basic emotions mentioned above and those that used neutral faces in their evaluation; description of the sample. The instruments used to evaluate the FER were also identified, in addition to the identification of those studies that used neuroimaging tests and/or biological and/or physiological markers.
Another sets of facial expressions used for emotion assessment on faces were NimStim stimuli (\"The Research Network on Early Experience and Brain Development\" (Kumfor et al., 2014c; 2016), DB99 (Advanced Telecommunications Research Institute International, Inc. Nara, Japan) database stimuli (Maki et al., 2013), \"The Awareness of Social Inference Test-Emotion Evaluation Test\" (TASIT-EET) (Martinez et al., 2018), stimuli belonging to \"The Multimodal Emotion Recognition Test\" (MERT) (Ostos et al., 2011), as well as the \"Penn Emotion Recognition Test\" (ER40) (Kohler et al., 2005; Weiss et al., 2008). Finally, several studies used the FACES battery adaptation as a test of emotional recognition (Dourado et al., 2019; Torres et al., 2015; Shimokawa et al., 2000). In addition to the studies that showed photographs in paper format, we found other works in which photographs were shown in digital format (Bertoux et al., 2015; Chiu et al., 2016; Fernandez-Duque & Black, 2005; Henry et al., 2008; Hot et al., 2013; Hsieh et al., 2012; 2013; Kipps et al., 2009; Kholer et al., 2005; Kumfor et al., 2014a; 2014b; 2014c; 2016; Maki et al., 2013; Narme et al., 2013; 2017; Park et al., 2017; Sapey-Triomphe et al., 2015; Weiss et al., 2008). Instruments were also used in digital format in which images are presented in a continuum of different intensities and/or between expressions that can be confusing (Chiu et al., 2016; Kipps et al., 2009; Kholer et al., 2005; Maki et al., 2013; Sapey-Triomphe et al., 2015) (see Table 6).
However, University of Essex researchers found that angry faces are one of the fastest expressions to be detected. This is because we need to be able to quickly tell if the person we are talking to suddenly becomes angry to avoid possible physical harm. 1e1e36bf2d