Abstract
Recognition of the inverted faces (compared to upright faces) becomes much more difficult, this phenomenon is known as “facial inversion effect”. The role of face inversion on the eye-movement patterns is still under debate. In the current research, we aimed to investigate the effects of face inversion and the number of feature differences on eye-movement patterns during judgement of similarity between two faces. The participants were asked to judge the pairs of faces similarity by a 4-point Likert scale. Eye-movements were recorded during similarity ratings. The faces were presented either upright or inverted. The pairs of the faces were either the same or different. The different pairs of faces were manipulated via the number of feature differences (i.e., one feature or two features). As expected, we found that the inversion of the faces resulted in a higher level of estimated similarity in overall. However, when only the hairs were different between two faces, the inversion of the faces resulted in a lower level of estimated similarity. Surprisingly, the inversion of the faces resulted in a significant decrease of transitions between the pairs of inverted faces. The less eye gaze transitions during similarity rating of inverted pairs suggested that the feature-based processing strategy of inverted faces was less likely to be used in our rating task that presumably exerts no memory load. In conclusion, our findings suggested that both face inversion and the number of feature differences influence the eye-movement patterns during the similarity rating of two simultaneously presented faces.
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Acknowledgments
This project was supported by National Natural Science Foundation of China (31700955), Guangdong Planning Office of Philosophy and Social Science Project (GD17CXL03) and Guangdong Innovative Young Talents in Universities and Colleges Project (2018WQNCX175).
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Zhao, MF., Zimmer, H.D. (2020). The Effects of Face Inversion and the Number of Feature Differences on Eye-Movement Patterns. In: Harris, D., Li, WC. (eds) Engineering Psychology and Cognitive Ergonomics. Mental Workload, Human Physiology, and Human Energy. HCII 2020. Lecture Notes in Computer Science(), vol 12186. Springer, Cham. https://doi.org/10.1007/978-3-030-49044-7_22
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