Development and validation of an Argentine set of facial expressions of emotion

Vaiman, Marcelo; Wagner, Monica; Caicedo, Estefanía Elena; Pereno, Germán Leandro

doi:10.1080/02699931.2015.1098590

Cited by 10 publications

(6 citation statements)

References 24 publications

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“…Other procedures could be used to determine users' interpretation of meaning. For instance, researchers could use forced choice tasks (i.e., decide which emotion/meaning is expressed by the stimuli; Vaiman, Wagner, Caicedo, & Pereno, 2017).…”

Section: Discussionmentioning

confidence: 99%

Lisbon Emoji and Emoticon Database (LEED): Norms for emoji and emoticons in seven evaluative dimensions

et al. 2017

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The use of emoticons and emoji is increasingly popular across a variety of new platforms of online communication. They have also become popular as stimulus materials in scientific research. However, the assumption that emoji/emoticon users' interpretations always correspond to the developers'/researchers' intended meanings might be misleading. This article presents subjective norms of emoji and emoticons provided by everyday users. The Lisbon Emoji and Emoticon Database (LEED) comprises 238 stimuli: 85 emoticons and 153 emoji (collected from iOS, Android, Facebook, and Emojipedia). The sample included 505 Portuguese participants recruited online. Each participant evaluated a random subset of 20 stimuli for seven dimensions: aesthetic appeal, familiarity, visual complexity, concreteness, valence, arousal, and meaningfulness. Participants were additionally asked to attribute a meaning to each stimulus. The norms obtained include quantitative descriptive results (means, standard deviations, and confidence intervals) and a meaning analysis for each stimulus. We also examined the correlations between the dimensions and tested for differences between emoticons and emoji, as well as between the two major operating systems-Android and iOS. The LEED constitutes a readily available normative database (available at www.osf.io/nua4x ) with potential applications to different research domains.

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Section: Discussionmentioning

confidence: 99%

Lisbon Emoji and Emoticon Database (LEED): Norms for emoji and emoticons in seven evaluative dimensions

et al. 2017

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“…These databases include dynamic (i.e., videos) and static (i.e., pictures) stimuli depicting human models of different nationalities and cultural backgrounds, expressing a wide range of facial expressions. However, most databases include only young adults as models [19,21,37–39]. A few exceptions include adult models of distinct age groups.…”

Section: Introductionmentioning

confidence: 99%

Subjective ratings and emotional recognition of children’s facial expressions from the CAFE set

et al. 2018

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Access to validated stimuli depicting children’s facial expressions is useful for different research domains (e.g., developmental, cognitive or social psychology). Yet, such databases are scarce in comparison to others portraying adult models, and validation procedures are typically restricted to emotional recognition accuracy. This work presents subjective ratings for a sub-set of 283 photographs selected from the Child Affective Facial Expression set (CAFE [1]). Extending beyond the original emotion recognition accuracy norms [2], our main goal was to validate this database across eight subjective dimensions related to the model (e.g., attractiveness, familiarity) or the specific facial expression (e.g., intensity, genuineness), using a sample from a different nationality (N = 450 Portuguese participants). We also assessed emotion recognition (forced-choice task with seven options: anger, disgust, fear, happiness, sadness, surprise and neutral). Overall results show that most photographs were rated as highly clear, genuine and intense facial expressions. The models were rated as both moderately familiar and likely to belong to the in-group, obtaining high attractiveness and arousal ratings. Results also showed that, similarly to the original study, the facial expressions were accurately recognized. Normative and raw data are available as supplementary material at https://osf.io/mjqfx/.

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“…Indeed, validation procedures usually focus on emotion recognition, either using forced choice tasks (e.g., Vaiman et al, 2015) or rating scales (e.g., agreement with items such as BThis person seems to be angry^; Samuelsson et al, 2012). To our knowledge, only a few exceptions go beyond emotion recognition.…”

Section: Facial Expressions Databasesmentioning

confidence: 99%

“…For instance, they include Argentinian (Argentine Set of Facial Expressions of Emotion: Vaiman, Wagner, Caicedo, & Pereno, 2015), Chinese (Wang & Markham, 1999), Polish (WSEFEP: Olszanowski et al, 2015), or Swedish models (Umeå University Database of Facial Expressions: Samuelsson, Jarnvik, Henningsson, Andersson, & Carlbring, 2012). Regarding the models' ethnicity, most databases include exclusively (e.g., Radboud Faces Database-RaFD: Langner et al, 2010;FACES: Ebner et al, 2010;KDEF: Lundqvist et al, 1998) or a majority (the McEwan Faces: McEwan et al, 2014;NimStim: Tottenham et al, 2009) of white or European descent models (for exceptions, see, e.g., the Chicago Face Database-CFD [Ma, Correll, & Wittenbrink, 2015] and the Japanese and Caucasian Facial Expression of Emotion-JACFEE [Matsumoto & Ekman, 1988]).…”

Section: Facial Expressions Databasesmentioning

confidence: 99%

The many faces of a face: Comparing stills and videos of facial expressions in eight dimensions (SAVE database)

et al. 2016

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This article presents subjective rating norms for a new set of Stills And Videos of facial Expressions-the SAVE database. Twenty nonprofessional models were filmed while posing in three different facial expressions (smile, neutral, and frown). After each pose, the models completed the PANAS questionnaire, and reported more positive affect after smiling and more negative affect after frowning. From the shooting material, stills and 5 s and 10 s videos were edited (total stimulus set = 180). A different sample of 120 participants evaluated the stimuli for attractiveness, arousal, clarity, genuineness, familiarity, intensity, valence, and similarity. Overall, facial expression had a main effect in all of the evaluated dimensions, with smiling models obtaining the highest ratings. Frowning expressions were perceived as being more arousing, clearer, and more intense, but also as more negative than neutral expressions. Stimulus presentation format only influenced the ratings of attractiveness, familiarity, genuineness, and intensity. The attractiveness and familiarity ratings increased with longer exposure times, whereas genuineness decreased. The ratings in the several dimensions were correlated. The subjective norms of facial stimuli presented in this article have potential applications to the work of researchers in several research domains. From our database, researchers may choose the most adequate stimulus presentation format for a particular experiment, select and manipulate the dimensions of interest, and control for the remaining dimensions. The full stimulus set and descriptive results (means, standard deviations, and confidence intervals) for each stimulus per dimension are provided as supplementary material.

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Development and validation of an Argentine set of facial expressions of emotion

Cited by 10 publications

References 24 publications

Lisbon Emoji and Emoticon Database (LEED): Norms for emoji and emoticons in seven evaluative dimensions

Lisbon Emoji and Emoticon Database (LEED): Norms for emoji and emoticons in seven evaluative dimensions

Subjective ratings and emotional recognition of children’s facial expressions from the CAFE set

The many faces of a face: Comparing stills and videos of facial expressions in eight dimensions (SAVE database)

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