Study 2 Preregistration
Creators: name: Roze, Krista
name: Kliemann, Dorit
Publisher: name: Open Science Framework
Year: 2023
Subjects: subject: Biological Psychology
subject: Cognitive Psychology
subject: Social and Behavioral Sciences
subject: Psychology
subject: FOS: Psychology

description: Identifying a person’s emotions through their facial expressions is necessary in helping us navigate social interactions. Some individuals, such as those with autism spectrum disorder (ASD), have difficulties in accurately identifying emotions from faces. These difficulties affect everyday interactions and contribute to the diagnostic phenotype. Facial emotion recognition (FER) tasks have been developed in an attempt to measure facial expression recognition abilities in both neurotypical and psychiatric populations in order to assess and quantify potential impairments. These tasks are also useful in retesting participants undergoing social skills or other training in order to track any improvements. FER tests to date often suffer from two limitations. First, stimulus sets used in established FER tasks are often limited to basic emotions (happiness, sadness, anger, disgust, fear, and surprise; plus neutral expressions as baseline), and add no or only few complex emotions (e.g., jealousy and boredom (Montagne, Kessels, De Haan, &amp; Perrett, 2007)). Using only basic emotions to quantify FER abilities could potentially result in reduced external validity of the tasks by not accounting for the wide array of more complex emotions encountered in everyday life. Second, FER tasks often use static images of emotional faces, which do not capture naturalistic and dynamic aspects of facial emotion recognition. Previously, the Face Puzzle tasks addressed these limitations by utilizing dynamic video stimuli featuring 15 actors portraying a wider variety of emotions to more closely approximate real life facial emotion recognition. (Kliemann et al., 2013). This stimulus set consisted of 25 videos of emotional facial expressions, with 5 basic (angry, happy, disgusted, fearful, surprised) and 20 complex emotions (interested, amused, touched, worried, jealous, enthusiastic, apologetic, disappointed, relieved, expectant, bored, compassionate, contemptuous, forgiving, embarrassed, wistful, furious, content, confident, doubtful), for a total of 11 positive and 14 negative emotions. In an initial validation study the Face Puzzle tasks showed good internal consistency, consistent external validity and sensitivity to impaired FER in adult individuals with ASD. Originally, the stimuli and task were designed in German, leaving it an open question whether intended emotion expressions and respective labels are valid in the English language, and thus whether the task is valid for use in English as well. The overall aim of this project is thus to validate the stimulus set and task design for the English language. We will only focus on the explicit Face Puzzle task, since the implicit version does not involve written labels. The optimal representation of each emotion was chosen from all options to form a new stimulus set of 25 videos. Of this newly established stimulus set consisting of 25 emotions, 12 of the original videos from the Kliemann et al. (2013) paper were kept after validation of valence, arousal, and believability: compassionate, bored, wistful, surprised, relieved, envious, furious, worried, enthusiastic, expectant, disgusted, and angry. The remaining 13 videos were traded out from the originals because either the valence or arousal ratings did not match the expected ratings obtained for the emotional words in Hepach et al. (2011), or because believability was not high enough; happy, forgiving, doubtful, content, embarrassed, disappointed, interested, fearful, confident, apologetic, contemptuous, amused, and touched. In this study, we will validate the English version of the Face Puzzle explicit task by determining construct validity. We will only focus on validating the explicit Face Puzzle task, since the implicit version does not involve written labels. To do so, we will conduct an item analysis for participants’ accuracy in correctly identifying the emotional label for the 25 dynamic emotional expression stimuli (selected in study 1) out of four provided written labels. We will measure internal consistency by calculating Cronbach’s alpha. In the original German version of the task, the items achieved high internal consistency as denoted by a Cronbach’s alpha of 0.81 (Kliemann, 2013). In the present task, the Cronbach’s alpha value that will serve as the cutoff for acceptable reliability is 0.7 (Tavakol &amp; Dennick, 2011). If Cronbach’s alpha is below 0.7, we will calculate “alpha-if-item-deleted" for each item to determine which items must be deleted. Any items that markedly reduce Cronbach’s alpha will be removed (“using alpha-if-deleted”) until an alpha of at least 0.7 is achieved. If total number of items must be reduced to less than 20 items, we will revise distractor words of the item with highest “alpha-if-item-deleted" according to the strategy outlined in Kliemann et al., 2013. Study 2 – with a new set of distractor words for respective items - will then be run again with a unique group of mTurk participants. Distractor words of the item with the highest “alpha-if-item-deleted" will continue to be revised until the stimulus set achieves a Cronbach’s alpha of 0.7.