Robust anger: Recognition of deteriorated dynamic bodily emotion expressions

Abstract

In two studies, the robustness of anger recognition of bodily expressions is tested. In the first study, video recordings of an actor expressing four distinct emotions (anger, despair, fear, and joy) were structurally manipulated as to image impairment and body segmentation. The results show that anger recognition is more robust than other emotions to image impairment and to body segmentation. Moreover, the study showed that arms expressing anger were more robustly recognised than arms expressing other emotions. Study 2 added face blurring as a variable to the bodily expressions and showed that it decreased accurate emotion recognition—but more for recognition of joy and despair than for anger and fear. In sum, the paper indicates the robustness of anger recognition in multileveled deteriorated bodily expressions.

Keywords:

• Anger
• Emotion recognition accuracy
• Bodily expression
• Image impairment
• Body segmentation

The research of Valentijn Visch was funded by a NWO Rubicon Grant (446-06-009), the research of Martijn Goudbeek and Marcello Mortillaro by SNFS grant (FNRS 101411-100367; FNRS 100014-122491/1), the NCCR SNFS grant (51NF40-104897), and partly by the NWO VICI grant (277-70-007). We thank Tanja Bänziger for the work done on construction and rating of the GEMEP corpus. These studies was realized using Cogent 2000 developed by the Cogent 2000 team at the FIL and the ICN and Cogent Graphics developed by John Romaya at the LON at the Wellcome Department of Imaging Neuroscience. We kindly thank Marieke Hoetjes for careful reading of the manuscript and thank Marlie van Meer for her assistance in running Study 2.

The research of Valentijn Visch was funded by a NWO Rubicon Grant (446-06-009), the research of Martijn Goudbeek and Marcello Mortillaro by SNFS grant (FNRS 101411-100367; FNRS 100014-122491/1), the NCCR SNFS grant (51NF40-104897), and partly by the NWO VICI grant (277-70-007). We thank Tanja Bänziger for the work done on construction and rating of the GEMEP corpus. These studies was realized using Cogent 2000 developed by the Cogent 2000 team at the FIL and the ICN and Cogent Graphics developed by John Romaya at the LON at the Wellcome Department of Imaging Neuroscience. We kindly thank Marieke Hoetjes for careful reading of the manuscript and thank Marlie van Meer for her assistance in running Study 2.