Realness of face images can be decoded from non-linear modulation of EEG responses
Publication year
2024Author(s)
Number of pages
14 p.
Source
Scientific Reports, 14, (2024), article 5683ISSN
Publication type
Article / Letter to editor
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Organization
SW OZ DCC SMN
Journal title
Scientific Reports
Volume
vol. 14
Languages used
English (eng)
Subject
Action, intention, and motor controlAbstract
Artificially created human faces play an increasingly important role in our digital world. However, the so-called uncanny valley effect may cause people to perceive highly, yet not perfectly human-like faces as eerie, bringing challenges to the interaction with virtual agents. At the same time, the neurocognitive underpinnings of the uncanny valley effect remain elusive. Here, we utilized an electroencephalography (EEG) dataset of steady-state visual evoked potentials (SSVEP) in which participants were presented with human face images of different stylization levels ranging from simplistic cartoons to actual photographs. Assessing neuronal responses both in frequency and time domain, we found a non-linear relationship between SSVEP amplitudes and stylization level, that is, the most stylized cartoon images and the real photographs evoked stronger responses than images with medium stylization. Moreover, realness of even highly similar stylization levels could be decoded from the EEG data with task-related component analysis (TRCA). Importantly, we also account for confounding factors, such as the size of the stimulus face’s eyes, which previously have not been adequately addressed. Together, this study provides a basis for future research and neuronal benchmarking of real-time detection of face realness regarding three aspects: SSVEP-based neural markers, efficient classification methods, and low-level stimulus confounders.
This item appears in the following Collection(s)
- Academic publications [246216]
- Electronic publications [133894]
- Faculty of Social Sciences [30432]
- Open Access publications [107422]
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