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, 2021) is a computerized task to assess different modalities of nonverbal ERA. The 72-item task uses items from the GEMEP corpus (Banziger et al., 2012)-a set of dynamic video clips of emotional expressions-and is divided into three blocks using different kinds of items: video, audio, or audio-video presentations of nonverbal emotional expressions. By this, the test provides separate measures for these three modalities. In the video modality, the participant needs to infer which emotion an actor is expressing based on their facial expression and body language. In the audio modality, the participant needs to infer the emotion based only on prosody without any visual information. To exclude the influence of verbal content, the actor expresses the emotion in a pseudo- language (“ne kal i bam sud molen” and “kun se mina lod belam”). In the audio-video modality, the participant is provided both visual and auditory information. The GEMEP clips include five female and five male professional actors of different ages that recorded the emotional expressions under the guidance of a professional theater director (see Banziger et al., 2012). The emotions used were anger, anxiety, despair, disgust, fear, interest, joy, pleasure, pride, relief, irritation, and sadness. They vary in their valence and degree of arousal. Each modality (consisting of 24 items) depicts two displays of each of these 12 emotions. The task consists of identifying as fast as possible which emotion was displayed and to match it with a predefined list of emotions using the computer mouse. The task was conducted in Swedish. The internal consistency of the test in the current sample was ?pre = 0.67 and ?article = 0.66, using Kuder-Richardson Formula 20 for dichotomous data (KR-20; Kuder and Richardson, 1937); whereas in an evaluation study (Laukka et al., 2021), the ERAM showed stronger reliability.
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As a measure of recognition accuracy for micro expressions, we developed a 70-item computer task (micro expression recognition task; MICRO) using colored still pictures from the Radboud Faces Database (Langner et al., 2010) depicting both female and male young faces. A frontal picture of an actor’s face displaying an emotional expression is presented for 200 ms and is both preceded and followed by a neutral expression that lasts for 2 s. Using this double-masking technique, a micro expression is created from still pictures. The actors were trained to express facial emotional expressions according to prototypes from the Facial Action Coding System (Ekman et al., 2002). The 70 items for each measurement were randomly selected from a pool of 312 items. The emotions used were seven of the basic emotions proposed by Ekman (e.g., Ekman and Cordaro, 2011): happiness, surprise, fear, disgust, sadness, anger, contempt. The participants were instructed to indicate which micro expression they perceived based on a list of emotions, using the keyboard (forced choice format). They were instructed to answer as fast and as accurately as possible; the task was conducted in Swedish. pre = 0.84 and ?post = 0.86.
A slightly modified and computerized version of the Patient Emotion Cue Test (PECT; Blanch-Hartigan, 2011) was used to assess the accuracy for detecting verbal and nonverbal emotional displays that are representative of the medical clinical context. In 47 video clips, a young female actor is displaying the five emotions anger, sadness, happiness, anxiety, and confusion, as well as neutral expressions, via a combination of nonverbal (prosody, face, body language) and verbal (content) information. The actor plays the role of a patient that is talking to a medical professional. g., “It’s just being gradually getting worse” or “Can I play golf again?”) and the verbal and nonverbal expressions vary in their intensity. The clips were averaging 3 s and were followed by a black screen (8 s) as response window. The PECT has been found to be a reliable and valid tool for assessing patients’ emotional cues in the medical context (Blanch-Hartigan, 2011). In the current sample, the KS-20 internal consistency was ?pre = 0.61 and ?post = 0.57.