Facial Expression Aftereffect Revealed by Adaption to Emotion-Invisible Dynamic Bubbled Faces
Visual adaptation is a powerful tool to probe the short-term plasticity of the visual system. Adapting to local features such as the oriented lines can distort our judgment of subsequently presented lines, the tilt aftereffect. The tilt aftereffect is believed to be processed at the low-level of the...
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sg-ntu-dr.10356-823562022-02-16T16:26:59Z Facial Expression Aftereffect Revealed by Adaption to Emotion-Invisible Dynamic Bubbled Faces Luo, Chengwen Wang, Qingyun Schyns, Philippe G. Kingdom, Frederick A. A. Xu, Hong Tamietto, Marco School of Humanities and Social Sciences facial expression recognition Visual adaptation is a powerful tool to probe the short-term plasticity of the visual system. Adapting to local features such as the oriented lines can distort our judgment of subsequently presented lines, the tilt aftereffect. The tilt aftereffect is believed to be processed at the low-level of the visual cortex, such as V1. Adaptation to faces, on the other hand, can produce significant aftereffects in high-level traits such as identity, expression, and ethnicity. However, whether face adaptation necessitate awareness of face features is debatable. In the current study, we investigated whether facial expression aftereffects (FEAE) can be generated by partially visible faces. We first generated partially visible faces using the bubbles technique, in which the face was seen through randomly positioned circular apertures, and selected the bubbled faces for which the subjects were unable to identify happy or sad expressions. When the subjects adapted to static displays of these partial faces, no significant FEAE was found. However, when the subjects adapted to a dynamic video display of a series of different partial faces, a significant FEAE was observed. In both conditions, subjects could not identify facial expression in the individual adapting faces. These results suggest that our visual system is able to integrate unrecognizable partial faces over a short period of time and that the integrated percept affects our judgment on subsequently presented faces. We conclude that FEAE can be generated by partial face with little facial expression cues, implying that our cognitive system fills-in the missing parts during adaptation, or the subcortical structures are activated by the bubbled faces without conscious recognition of emotion during adaptation. Published version 2016-02-15T08:11:43Z 2019-12-06T14:54:01Z 2016-02-15T08:11:43Z 2019-12-06T14:54:01Z 2015 Journal Article Luo, C., Wang, Q., Schyns, P. G., Kingdom, F. A. A., & Xu, H. (2015). Facial Expression Aftereffect Revealed by Adaption to Emotion-Invisible Dynamic Bubbled Faces. PLOS ONE, 10(12), e0145877-. 1932-6203 https://hdl.handle.net/10356/82356 http://hdl.handle.net/10220/39979 10.1371/journal.pone.0145877 26717572 en PLOS ONE © 2015 Luo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 16 p. application/pdf |
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facial expression recognition Luo, Chengwen Wang, Qingyun Schyns, Philippe G. Kingdom, Frederick A. A. Xu, Hong Facial Expression Aftereffect Revealed by Adaption to Emotion-Invisible Dynamic Bubbled Faces |
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Visual adaptation is a powerful tool to probe the short-term plasticity of the visual system. Adapting to local features such as the oriented lines can distort our judgment of subsequently presented lines, the tilt aftereffect. The tilt aftereffect is believed to be processed at the low-level of the visual cortex, such as V1. Adaptation to faces, on the other hand, can produce significant aftereffects in high-level traits such as identity, expression, and ethnicity. However, whether face adaptation necessitate awareness of face features is debatable. In the current study, we investigated whether facial expression aftereffects (FEAE) can be generated by partially visible faces. We first generated partially visible faces using the bubbles technique, in which the face was seen through randomly positioned circular apertures, and selected the bubbled faces for which the subjects were unable to identify happy or sad expressions. When the subjects adapted to static displays of these partial faces, no significant FEAE was found. However, when the subjects adapted to a dynamic video display of a series of different partial faces, a significant FEAE was observed. In both conditions, subjects could not identify facial expression in the individual adapting faces. These results suggest that our visual system is able to integrate unrecognizable partial faces over a short period of time and that the integrated percept affects our judgment on subsequently presented faces. We conclude that FEAE can be generated by partial face with little facial expression cues, implying that our cognitive system fills-in the missing parts during adaptation, or the subcortical structures are activated by the bubbled faces without conscious recognition of emotion during adaptation. |
author2 |
Tamietto, Marco |
author_facet |
Tamietto, Marco Luo, Chengwen Wang, Qingyun Schyns, Philippe G. Kingdom, Frederick A. A. Xu, Hong |
format |
Article |
author |
Luo, Chengwen Wang, Qingyun Schyns, Philippe G. Kingdom, Frederick A. A. Xu, Hong |
author_sort |
Luo, Chengwen |
title |
Facial Expression Aftereffect Revealed by Adaption to Emotion-Invisible Dynamic Bubbled Faces |
title_short |
Facial Expression Aftereffect Revealed by Adaption to Emotion-Invisible Dynamic Bubbled Faces |
title_full |
Facial Expression Aftereffect Revealed by Adaption to Emotion-Invisible Dynamic Bubbled Faces |
title_fullStr |
Facial Expression Aftereffect Revealed by Adaption to Emotion-Invisible Dynamic Bubbled Faces |
title_full_unstemmed |
Facial Expression Aftereffect Revealed by Adaption to Emotion-Invisible Dynamic Bubbled Faces |
title_sort |
facial expression aftereffect revealed by adaption to emotion-invisible dynamic bubbled faces |
publishDate |
2016 |
url |
https://hdl.handle.net/10356/82356 http://hdl.handle.net/10220/39979 |
_version_ |
1725985664214761472 |