December 2022
Volume 22, Issue 14
Open Access
Vision Sciences Society Annual Meeting Abstract  |   December 2022
Does social network quality influence facial recognition abilities in emerging adults?
Author Affiliations
  • Myles Arrington
    Pennsylvania State University
  • K. Suzanne Scherf
    Pennsylvania State University
Journal of Vision December 2022, Vol.22, 4323. doi:https://doi.org/10.1167/jov.22.14.4323
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Myles Arrington, K. Suzanne Scherf; Does social network quality influence facial recognition abilities in emerging adults?. Journal of Vision 2022;22(14):4323. https://doi.org/10.1167/jov.22.14.4323.

      Download citation file:


      © ARVO (1962-2015); The Authors (2016-present)

      ×
  • Supplements
Abstract

Converging findings from research in clinical populations (e.g., autism, schizophrenia) suggest that altered social behavior is associated with individual differences in face recognition abilities. A similar association may exist in typically-developing populations. For example, people who rank higher on social traits like empathy, extraversion, and inclusion of others in self exhibit strong face recognition skills. Neuroimaging findings suggest that individuals with large social networks have larger volume amygdala, which in turn is purported to be associated with superior face recognition behavior. These findings lead to a strong prediction of a linear positive association such that individuals with larger and/or stronger social networks have better face recognition skills. To explore this, we evaluated whether networks characteristics are associated with individual differences in unfamiliar face recognition ability. The sample included 130 emerging adults (18 - 28 years old), representing a developmental period that emphasizes the importance on building social networks. Data were collected online via Amazon M-Turk by way of Cloud Research. Participants completed the Cambridge Face Memory Test long form (CFMT+) and Car Cambridge Memory Test to assess unfamiliar face and object memory. We computed a face recognition score (FR) by subtracting out car recognition. A network map was used to measure social network size, the Norbeck Social Support Questionnaire to measure network strength, the revised Social Connectedness Scale to measure belonging. Surprisingly, we found no association between network size or strength and FR. The association between belonging and FR was quadratic, with good recognizers existing at both high and low levels of belonging. Variation in belonging and strength was interactively associated with FR. Individuals who reported weaker networks and lower belonging exhibited higher FR. These results indicate that the drive to belong and find connection by way of one’s network motivates higher FR.

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×