I Can See It in Your Face. Affective Valuation of Exercise in More or Less Physically Active Individuals

• Main Authors: Ralf Brand, Lukas Ulrich
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2019-12-01
• Series: Frontiers in Psychology
• Subjects: motivation; exercise; emotion; automatic facial expression analysis; Stroop effect
• Online Access: https://www.frontiersin.org/article/10.3389/fpsyg.2019.02901/full

The purpose of this study was to illustrate that people’s affective valuation of exercise can be identified in their faces. The study was conducted with a software for automatic facial expression analysis and it involved testing the hypothesis that positive or negative affective valuation occurs spontaneously when people are reminded of exercise. We created a task similar to an emotional Stroop task, in which participants responded to exercise-related and control stimuli with a positive or negative facial expression (smile or frown) depending on whether the photo was presented upright or tilted. We further asked participants how much time they would normally spend for physical exercise, because we assumed that the affective valuation of those who exercise more would be more positive. Based on the data of 86 participants, regression analysis revealed that those who reported less exercise and a more negative reflective evaluation of exercise initiated negative facial expressions on exercise-related stimuli significantly faster than those who reported exercising more often. No significant effect was observed for smile responses. We suspect that responding with a smile to exercise-related stimuli was the congruent response for the majority of our participants, so that for them no Stroop interference occurred in the exercise-related condition. This study suggests that immediate negative affective reactions to exercise-related stimuli result from a postconscious automatic process and can be detected in the study participants’ faces. It furthermore illustrates how methodological paradigms from social–cognition research (here: the emotional Stroop paradigm) can be adapted to collect and analyze biometric data for the investigation of exercisers’ and non-exercisers’ automatic valuations of exercise.