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Profiles on the Orientation Discrimination Processing of Human Faces

Author

Listed:
  • Carmen Moret-Tatay

    (Escuela de Doctorado, Universidad Católica de Valencia San Vicente Mártir, San Agustín 3, 46002 València, Spain)

  • Inmaculada Baixauli-Fortea

    (Faculty of Psychology, Universidad Católica de Valencia San Vicente Mártir, Sede Padre Jofré, 46100 Burjassot, València, Spain)

  • M. Dolores Grau-Sevilla

    (Faculty of Psychology, Universidad Católica de Valencia San Vicente Mártir, Sede Padre Jofré, 46100 Burjassot, València, Spain)

Abstract

Face recognition is a crucial subject for public health, as socialization is one of the main characteristics for full citizenship. However, good recognizers would be distinguished, not only by the number of faces they discriminate but also by the number of rejected stimuli as unfamiliar. When it comes to face recognition, it is important to remember that position, to some extent, would not entail a high cognitive cost, unlike other processes in similar areas of the brain. The aim of this paper was to examine participant’s recognition profiles according to face position. For this reason, a recognition task was carried out by employing the Karolinska Directed Emotional Faces. Reaction times and accuracy were employed as dependent variables and a cluster analysis was carried out. A total of two profiles were identified in participants’ performance, which differ in position in terms of reaction times but not accuracy. The results can be described as follows: first, it is possible to identify performance profiles in visual recognition of faces that differ in position in terms of reaction times, not accuracy; secondly, results suggest a bias towards the left. At the applied level, this could be of interest with a view to conducting training programs in face recognition.

Suggested Citation

  • Carmen Moret-Tatay & Inmaculada Baixauli-Fortea & M. Dolores Grau-Sevilla, 2020. "Profiles on the Orientation Discrimination Processing of Human Faces," IJERPH, MDPI, vol. 17(16), pages 1-11, August.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:16:p:5772-:d:396837
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    References listed on IDEAS

    as
    1. Carmen Moret-Tatay & Inmaculada Baixauli-Fortea & M. Dolores Grau Sevilla & Tatiana Quarti Irigaray, 2020. "Can You Identify These Celebrities? A Network Analysis on Differences between Word and Face Recognition," Mathematics, MDPI, vol. 8(5), pages 1-17, May.
    2. Regina Nuzzo, 2014. "Scientific method: Statistical errors," Nature, Nature, vol. 506(7487), pages 150-152, February.
    3. Naomi R Smart & Claire J Horwell & Trevor S Smart & Karen S Galea, 2020. "Assessment of the Wearability of Facemasks against Air Pollution in Primary School-Aged Children in London," IJERPH, MDPI, vol. 17(11), pages 1-13, June.
    4. Daniel Norton & Ryan McBain & Yue Chen, 2009. "Reduced Ability to Detect Facial Configuration in Middle-Aged and Elderly Individuals: Associations With Spatiotemporal Visual Processing," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 64(3), pages 328-334.
    5. Sheba Selvam & Ramadoss Balakrishnan & Balasundaram Sadhu Ramakrishnan, 2018. "Ontology With Hybrid Clustering Approach for Improving the Retrieval Relevancy in Social Event Detection," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 14(4), pages 33-56, October.
    6. Zonglei Zhen & Huizhen Fang & Jia Liu, 2013. "The Hierarchical Brain Network for Face Recognition," PLOS ONE, Public Library of Science, vol. 8(3), pages 1-9, March.
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