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Description: Although similar facial areas are used to recognize static and dynamic expressions, different fixation patterns are used to extract that information; namely, dynamic expressions are recognized without directly fixating on the eyes and mouth features (Blais et al., 2017). The present study aims to further our understanding of the visual underpinnings of dynamic and static facial expression recognition. Three experiments were conducted. Exp. 1 (N=20) measured the spatial frequency (SF) utilization for static and dynamic expressions using SF Bubbles method (Willenbockel et al.,2010) and revealed that participants relied more on lower SF for dynamic than for static expressions. In Exp. 2 (N=27), we used the same method to verify if this low SF bias could be explained by extrafoveal processing of biological motion. Dynamic-random expressions with altered biological motion were created by randomizing the frames. Unexpectedly, results indicated a higher reliance on low SFs with dynamic-random than with static expressions. Exp. 3 tested the hypothesis that motion drives covert attention to the features of interest, decreasing the need for direct fixations. Performance with dynamic expressions was compared with static expressions in which attention was driven to features of interest by abrupt changes in luminance. Results (N=25) do not support this hypothesis. Further research will be needed to draw a better understanding of those perceptual mechanisms.