Ophir, Nass, and Wagner report in this issue of PNAS (1) that heavy media multitaskers (HMMs) performed worse on task switching than light media multitaskers (LMMs), likely because of HMMs' reduced ability to filter out interference from irrelevant stimuli and representations in memory. Their findings are surprising in that, intuitively, HMMs should be better at task switching (i.e., multitasking) because they frequently switch between tasks, a habit or expertise (if so) that should have helped them to be better multitaskers (task switchers). However, the findings are also not surprising in that, as pointed out by Ophir, Nass, and Wagner, HMMs tend to be breadth-biased in their behaviors and are inclined to pay attention to a larger scope of information instead of focusing on a particular piece of information. Such a behavior or habit has conditioned them to be less selective when it comes to filtering information and tasks in front of them. In other words, HMMs may have developed a habit of treating all of the information in front of them with equal (or almost equal) amounts of attention instead of focusing their attention steadily on a particular task. As a result, they performed worse than LMMs did when they were asked to focus attention on selective pieces.
Social vulnerability assessment has been recognized as a reliable and effective measure for informing coastal hazard management. Although significant advances have been made in the study of social vulnerability for over two decades, China’s social vulnerability profiles are mainly based on administrative unit. Consequently, no detailed distribution is provided, and the capability to diagnose human risks is hindered. In this study, we established a social vulnerability index (SoVI) in 2000 and 2010 at a spatial resolution of 250 m for China’s coastal zone by combining the potential exposure index (PEI) and social resilience index (SRI). The PEI with a 250 m resolution was obtained by fitting the census data and multisource remote sensing data in random forest model. The county-level SRI was evaluated through principal component analysis based on 33 socioeconomic variables. For identifying the spatiotemporal change, we used global and local Moran’s I to map clusters of SoVI and its percent change in the decade. The results suggest the following: (1) Counties in the Yangtze River Delta, Pearl River Delta, and eastern Guangzhou, except several small hot spots, exhibited the most vulnerability, especially in urban areas, whereas those in Hainan and southern Liaoning presented the least vulnerability. (2) Notable increases were emphasized in Tianjin, Yangtze River Delta, and Pearl River Delta. The spatiotemporal variation and heterogeneity in social vulnerability obtained through this analysis will provide a scientific basis to decision-makers to focus risk mitigation effort.
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