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Amanda Estephan, Camille Saumure Regimbald, Daniel Fiset, Dan Sun, Ye Zhang, Marie-Pier Plouffe-Demers, Caroline Blais; Measuring the time course of spatial frequency use for face recognition from East to West. Journal of Vision 2016;16(12):1393. doi: 10.1167/16.12.1393.
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Easterners allocate their attention more broadly and integrate more the peripheral elements of a scene or a face than Westerners (Boduroglu et al., 2009). Relying on the peripheral visual field entails the use of lower spatial frequencies (SF; Hilz & Cavonius, 1974). In a recent study we found that Chinese participants made a better utilization of low SF whereas Canadians made a better utilization of high SF during face identification (Tardif et al., 2015). Here, we investigate the time course of the SF utilization across cultures. For this, we used a modified version of SF Bubbles (Willenbockel et al., 2010) with 15 Canadians and 25 Chinese. The method consisted in creating temporal sequences of random SF filters, meaning that the SF available to the participant varied through time within one trial. On each trial, a randomly filtered face, either Asian or Caucasian, was presented for 300ms, followed by a robust mask. The participant had to recognize its identity among eight identities of the same ethnicity learned beforehand (block design). Multiple regression analysis on the SF sampled and the participant's accuracy was used to create group classification images showing the SF tuning across time of Westerners and Easterners for Caucasian and Asian faces separately. Statistical thresholds were found using the Stat4CI (Chauvin et al., 2005). We replicate our previous findings suggesting that Westerners make more use of higher SF than Easterners for Caucasian faces (>15.6 cycles per face (cpf); Zcrit=2.7, p< 0.025) whereas the latter group makes more use of lower SF than the former (from 0.3 to 12 cpf for Caucasian faces, from 0.3 to 8 cpf for Asian faces; Zcrit=-2.7, p< 0.025). Most importantly, we show that this cultural difference occurs within 30ms and is consistent for the next 200ms of information extraction.
Meeting abstract presented at VSS 2016
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