IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i20p7420-d426620.html
   My bibliography  Save this article

Recognizing Emotions through Facial Expressions: A Largescale Experimental Study

Author

Listed:
  • Artemisa R. Dores

    (Center for Rehabilitation Research, School of Health, Polytechnic of Porto, 4200-072 Porto, Portugal
    Laboratory of Neuropsychophysiology, Faculty of Psychology and Education Sciences, University of Porto, 4200-135 Porto, Portugal)

  • Fernando Barbosa

    (Laboratory of Neuropsychophysiology, Faculty of Psychology and Education Sciences, University of Porto, 4200-135 Porto, Portugal)

  • Cristina Queirós

    (Faculty of Psychology and Education Sciences, University of Porto, 4200-135 Porto, Portugal)

  • Irene P. Carvalho

    (Clinical Neurosciences and Mental Health, School of Medicine, University of Porto, 4200-319 Porto, Portugal)

  • Mark D. Griffiths

    (International Gaming Research Unit, Psychology Department, Nottingham Trent University, Nottingham NG1 4FQ, UK)

Abstract

Experimental research examining emotional processes is typically based on the observation of images with affective content, including facial expressions. Future studies will benefit from databases with emotion-inducing stimuli in which characteristics of the stimuli potentially influencing results can be controlled. This study presents Portuguese normative data for the identification of seven facial expressions of emotions (plus a neutral face), on the Radboud Faces Database (RaFD). The effect of participants’ gender and models’ sex on emotion recognition was also examined. Participants ( N = 1249) were exposed to 312 pictures of white adults displaying emotional and neutral faces with a frontal gaze. Recognition agreement between the displayed and participants’ chosen expressions ranged from 69% (for anger) to 97% (for happiness). Recognition levels were significantly higher among women than among men only for anger and contempt. The emotion recognition was higher either in female models or in male models depending on the emotion. Overall, the results show high recognition levels of the facial expressions presented, indicating that the RaFD provides adequate stimuli for studies examining the recognition of facial expressions of emotion among college students. Participants’ gender had a limited influence on emotion recognition, but the sex of the model requires additional consideration.

Suggested Citation

  • Artemisa R. Dores & Fernando Barbosa & Cristina Queirós & Irene P. Carvalho & Mark D. Griffiths, 2020. "Recognizing Emotions through Facial Expressions: A Largescale Experimental Study," IJERPH, MDPI, vol. 17(20), pages 1-13, October.
  • Handle: RePEc:gam:jijerp:v:17:y:2020:i:20:p:7420-:d:426620
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/20/7420/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/17/20/7420/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Douglas Medin & Bethany Ojalehto & Ananda Marin & Megan Bang, 2017. "Systems of (non-)diversity," Nature Human Behaviour, Nature, vol. 1(5), pages 1-5, May.
    2. Maruti Vijayshankar Mishra & Sonia Baloni Ray & Narayanan Srinivasan, 2018. "Cross-cultural emotion recognition and evaluation of Radboud faces database with an Indian sample," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-19, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lou Safra & Nicolas Baumard & Valentin Wyart & Coralie Chevallier, 2020. "Social motivation is associated with increased weight granted to cooperation-related impressions in face evaluation tasks," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-17, April.
    2. Sakurako S. Okuzono & Koichiro Shiba & Harold H. Lee & Kokoro Shirai & Hayami K. Koga & Naoki Kondo & Takeo Fujiwara & Katsunori Kondo & Fran Grodstein & Laura D. Kubzansky & Claudia Trudel-Fitzgerald, 2022. "Optimism and Longevity Among Japanese Older Adults," Journal of Happiness Studies, Springer, vol. 23(6), pages 2581-2595, August.
    3. Wei, Hongxu & Hauer, Richard J. & He, Xingyuan, 2021. "A forest experience does not always evoke positive emotion: A pilot study on unconscious facial expressions using the face reading technology," Forest Policy and Economics, Elsevier, vol. 123(C).
    4. Lou Safra & Coralie Chevallier & Stefano Palminteri, 2019. "Depressive symptoms are associated with blunted reward learning in social contexts," PLOS Computational Biology, Public Library of Science, vol. 15(7), pages 1-22, July.
    5. Wullum Nielsen, Mathias & Börjeson, Love, 2019. "Gender diversity in the management field: Does it matter for research outcomes?," Research Policy, Elsevier, vol. 48(7), pages 1617-1632.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:17:y:2020:i:20:p:7420-:d:426620. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.