for emotional processing and attributional style training both in behavioral and neural studies [10]. Meta-analytic and review studies in this field [11; 12; 13] conclude the presence of interpretation biases as a mediator of performance on emotion recognition in depressed patients; they also highlight the heterogeneity of the methodology as an important factor that makes it difficult to obtain valid and reliable conclusive results (samples, emotion recognition tasks, or statistical analysis).  
The increased focus on social cognition and, specifically, on facial emotion recognition, is driving researchers towards the development of diverse assessment frameworks. In recent years, virtual reality (VR) has gained prominence with the recreation of controllable situations in near-real environments [14; 15; 16; 17]. This has allowed for new computerized interventions and assessment tools targeting mental disorders of easier accessibility and higher ecological validity. Therefore, VR has become an attractive tool for clinical practice [18; 19; 20; 21; 22; 23; 24]. Interestingly, the more recent meta-analytic review on facial emotion recognition in MDD [11] has not described any studies conducted with the help of VR. To our knowledge, this is the first study to focus on the comparison of facial emotion recognition in people with MDD and healthy populations using VR technology.  
The contribution of VR to research on social cognition has been mainly through the creation of dynamic avatars to represent several emotional states, allowing social real-time interaction with the participant, and evaluating affective processing [25]. The avatar faces used to be constructed based on the standardized Facial Action Coding System (FACS) [26], which makes it possible to build normative data for cataloging facial movements by encoding muscle contraction as a unit of measure called an action unit (AU) [27].  
Our research team, comprising multidisciplinary experts, is in the process of developing a novel intervention aimed at enhancing facial emotion recognition, referred to as \"AFRONTA: Affect Recognition Through Avatars\". Prior to implementing the therapy, various methodological steps are undertaken. The initial step entailed the representation of the six basic emotions through a new set of dynamic virtual faces (DVFs), which were constructed based on the action units of the Facial Action Coding System (FACS) [28]. Subsequently, these DVFs were evaluated by a sample of 204 healthy individuals, yielding the conclusion that the DVFs were effective in accurately recreating human facial expressions of emotions [29]. The current study is focused on analyzing facial emotion recognition scores using DVFs in a sample of 54 stable patients diagnosed with MDD and comparing these scores with those of healthy controls from the same demographic area, with the purpose of discovering specific deficits to develop better targeted interventions. To this end, the following was hypothesized:  
* Hypothesis 1 (H1). Individuals diagnosed with MDD will demonstrate a diminished ability to recognize emotions, as well as longer reaction times compared to healthy controls.
* Hypothesis 2 (H2). Both the MDD and control groups will display greater precision in recognizing more DVFs compared to less dynamic ones, resulting in a higher number of successful identifications.
* Hypothesis 3 (H3). Both groups will exhibit greater accuracy in recognizing DVFs presented in a frontal view in comparison to those presented in profile views, resulting in a higher number of successful identifications.
* Hypothesis 4 (H4). For the depression group, differences in age will be observed, with younger participants performing better. No differences will be found in terms of gender or educational level.