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Researchers have used eye-tracking measures to explore the relationship between face encoding and recognition, including the impact of ethnicity on this relationship. Previous studies offer a variety of conflicting conclusions. This confusion may stem from misestimation of the relationship between encoding and recognition. First, most previous models fail to account for the structure of eye-tracking data, potentially falling prey to Simpson’s paradox. Second, previous models assume a linear relationship between attention (e.g., the number of fixations to a to-be-remembered face) and recognition accuracy. Two eye-tracking studies (Ns = 41, 59), one online experiment that manipulates exposure (N = 150), and a mega-analysis examine the effects of ethnicity using what we believe to be more appropriate analytical models. Across studies and measures, we document a novel, critical pattern: The relationship between attention and recognition is nonlinear and negatively accelerating. At low levels of baseline attention, a small increment in attention improves recognition. However, as attention increases further, increments yield smaller and smaller benefits. This finding parallels work in learning and memory. In models that allow for nonlinearity, we find evidence that central features (eyes, nose, and mouth) generally contribute to recognition accuracy, potentially resolving disagreements in the field. We also find that the effects of attention on recognition are similar for ingroup and outgroup faces, which have important implications for theories of perceptual expertise. 

The scale and urgency of sustainability problems the world over has led to calls for sustainability transformations in cities, regions, and countries. Such calls for transformation are underlain by a persistent knowledge-to-action gap between scientific knowledge production, policy, and practice. To rise to the challenges of sustainability and resilience, municipal administrators need to set evidence-based and ambitious sustainability targets and develop strategies to achieve them. Simultaneously, transdisciplinary sustainability science researchers need to generate scientific knowledge to further enable cities along pathways of transformation. This paper details a collaborative backcasting game, AudaCITY, developed to build transformative capacity in city administrations while also generating deep contextual knowledge to inform a transformative sustainability science research agenda. We present AudaCITY's key features, potential applications and adaptations, and exemplary outputs and outcomes for cities and researchers. We conclude with recommendations for adopting and adapting AudaCITY for use in action-oriented and transformational sustainability science and capacity building. 

This paper highlights current technological limitations and offers recommendations for scientists and engineers when designing devices to support aging in place. Existing technology for older adults to support independent living is examined as well as the implications of contextual factors, namely, location, on how people live and age based on the location in which they reside. This is the first review to investigate how challenges of aging change relative to location of residence and, subsequently, how such variation may inform technological solutions. To date, few devices consider the environment in which older individuals age. Places examined include aging: at home; assisted living facilities; nursing homes; and family housing. Challenges found in common across these locations were financial strain and isolation. In addition, each setting was found to have its own unique hurdles. Understanding these barriers is essential to developing technology that enables older adults to successfully age in place.