Perceptual bias, more than age, impacts on eye movements during face processing

Consistent with the right hemispheric dominance for face processing, a left perceptual bias (LPB) is typically demonstrated by younger adults viewing faces and a left eye movement bias has also been revealed. Hemispheric asymmetry is predicted to reduce with age and older adults have demonstrated a weaker LPB, particularly when viewing time is restricted. What is currently unclear is whether age also weakens the left eye movement bias. Additionally, a right perceptual bias (RPB) for facial judgments has less frequently been demonstrated, but whether this is accompanied by a right eye movement bias has not been investigated. To address these issues older and younger adults’ eye movements and gender judgments of chimeric faces were recorded in two time conditions. Age did not significantly weaken the LPB or eye movement bias; both groups looked initially to the left side of the face and made more fixations when the gender judgment was based on the left side. A positive association was found between LPB and initial saccades in the freeview condition and with all eye movements (initial saccades, number and duration of fixations) when time was restricted. The accompanying eye movement bias revealed by LPB participants contrasted with RPB participants who demonstrated no eye movement bias in either time condition. Consequently, increased age is not clearly associated with weakened perceptual and eye movement biases. Instead an eye movement bias accompanies an LPB (particularly under restricted viewing time conditions) but not an RPB

White, Man, and Highly Followed: Gender and Race Inequalities in Twitter

Social media is considered a democratic space in which people connect and interact with each other regardless of their gender, race, or any other demographic factor. Despite numerous efforts that explore demographic factors in social media, it is still unclear whether social media perpetuates old inequalities from the offline world. In this paper, we attempt to identify gender and race of Twitter users located in U.S. using advanced image processing algorithms from Face++. Then, we investigate how different demographic groups (i.e. male/female, Asian/Black/White) connect with other. We quantify to what extent one group follow and interact with each other and the extent to which these connections and interactions reflect in inequalities in Twitter. Our analysis shows that users identified as White and male tend to attain higher positions in Twitter, in terms of the number of followers and number of times in user's lists. We hope our effort can stimulate the development of new theories of demographic information in the online space.Comment: In Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence (WI'17). Leipzig, Germany. August 201