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  • Chapter 12 - Using Data-Driven Methods to Advance Knowledge of Social Face Perception

  • from Part V - Methodological Approaches to the Study of the Effects of Aging on Emotion Communication
  • Edited by Ursula Hess, Humboldt-Universität zu Berlin, Reginald B. Adams, Jr., Pennsylvania State University, Robert E. Kleck, Dartmouth College, New Hampshire
  • Book:
    Emotion Communication by the Aging Face and Body
    Published online:
    07 December 2023
    Print publication:
    07 December 2023, pp 286-307

  • Summary

    This book has shown that the human face is a rich source of information about the people around us, including their age and emotional states. Given the importance of facial appearance in social interactions, a central goal is to understand how observers extract this information – that is, what makes someone look young, old, sad, or happy? However, this is empirically challenging because the human face has many different features that could drive these judgments, such as specific face shapes, complexions, or expressions. Today, new data-driven methods make this challenge tractable and present exciting new horizons in the field of social perception. In this final chapter, we extend the discussion on data-driven methods introduced in Chapter 11 (Albohn et al., this volume) by illustrating one such approach that can precisely model the specific facial features that drive social perception. We use recent work to illustrate how this approach has advanced current understanding of social face perception and conclude by illuminating future research directions.