Pinpointing the optimal spatial frequency range for automatic neural facial fear processing

• Main Authors: Stephanie Van der Donck, Tiffany Tang, Milena Dzhelyova, Johan Wagemans, Bart Boets
• Format: Article
• Language: English
• Published: Elsevier 2020-11-01
• Series: NeuroImage
• Subjects: EEG; FPVS; Facial emotion processing; Implicit fear detection; Spatial frequency
• Online Access: http://www.sciencedirect.com/science/article/pii/S1053811920306376

Faces convey an assortment of emotional information via low and high spatial frequencies (LSFs and HSFs). However, there is no consensus on the role of particular spatial frequency (SF) information during facial fear processing. Comparison across studies is hampered by the high variability in cut-off values for demarcating the SF spectrum and by differences in task demands. We investigated which SF information is minimally required to rapidly detect briefly presented fearful faces in an implicit and automatic manner, by sweeping through an entire SF range without constraints of predefined cut-offs for LSFs and HSFs. We combined fast periodic visual stimulation with electroencephalography. We presented neutral faces at 6 ​Hz, periodically interleaved every 5th image with a fearful face, allowing us to quantify an objective neural index of fear discrimination at exactly 1.2 ​Hz. We started from a stimulus containing either only very low or very high SFs and gradually increased the SF content by adding higher or lower SF information, respectively, to reach the full SF spectrum over the course of 70 ​s. We found that faces require at least SF information higher than 5.93 cycles per image (cpi) to implicitly differentiate fearful from neutral faces. However, exclusive HSF faces, even in a restricted SF range between 94.82 and 189.63 cpi already carry the critical information to extract the emotional expression of the faces.