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Abstract Implicit biases - differential attitudes towards members of distinct groups - are pervasive in human societies and create inequities across many aspects of life. Recent research has revealed that implicit biases are generally driven by social contexts, but not whether they are systematically influenced by the ways that humans self-organize in cities. We leverage complex system modeling in the framework of urban scaling theory to predict differences in these biases between cities. Our model links spatial scales from city-wide infrastructure to individual psychology to predict that cities that are more populous, more diverse, and less segregated are less biased. We find empirical support for these predictions in U.S. cities with Implicit Association Test data spanning a decade from 2.7 million individuals and U.S. Census demographic data. Additionally, we find that changes in cities’ social environments precede changes in implicit biases at short time-scales, but this relationship is bi-directional at longer time-scales. We conclude that the social organization of cities may influence the strength of these biases. 

Abstract Sleep is critical to a variety of cognitive functions and insufficient sleep can have negative consequences for mood and behavior across the lifespan. An important open question is how sleep duration is related to functional brain organization which may in turn impact cognition. To characterize the functional brain networks related to sleep across youth and young adulthood, we analyzed data from the publicly available Human Connectome Project (HCP) dataset, which includesn‐back task‐based and resting‐state fMRI data from adults aged 22–35 years (taskn = 896; restn = 898). We applied connectome‐based predictive modeling (CPM) to predict participants' mean sleep duration from their functional connectivity patterns. Models trained and tested using 10‐fold cross‐validation predicted self‐reported average sleep duration for the past month fromn‐back task and resting‐state connectivity patterns. We replicated this finding in data from the 2‐year follow‐up study session of the Adolescent Brain Cognitive Development (ABCD) Study, which also includesn‐back task and resting‐state fMRI for adolescents aged 11–12 years (taskn = 786; restn = 1274) as well as Fitbit data reflecting average sleep duration per night over an average duration of 23.97 days. CPMs trained and tested with 10‐fold cross‐validation again predicted sleep duration fromn‐back task and resting‐state functional connectivity patterns. Furthermore, demonstrating that predictive models are robust across independent datasets, CPMs trained on rest data from the HCP sample successfully generalized to predict sleep duration in the ABCD Study sample and vice versa. Thus, common resting‐state functional brain connectivity patterns reflect sleep duration in youth and young adults. 

Implicit biases, expressed as differential treatment towards out-group members, are pervasive in human societies. These biases are often racial or ethnic in nature and create disparities and inequities across many aspects of life. Recent research has revealed that implicit biases are, for the most part, driven by social contexts and local histories. However, it has remained unclear how and if the regular ways in which human societies self-organize in cities produce systematic variation in implicit bias strength. Here we leverage extensions of the mathematical models of urban scaling theory to predict and test between-city differences in implicit racial biases. Our model comprehensively links scales of organization from city-wide infrastructure to individual psychology to quanti-tatively predict that cities that are (1) more populous, (2) more diverse, and (3) less segregated have lower levels of implicit biases. We find broad empirical support for each of these predictions in U.S. cities for data spanning a decade of racial implicit association tests from millions of individuals. We conclude that the organization of cities strongly drives the strength of implicit racial biases and provides potential systematic intervention targets for the development and planning of more equitable societies. 

Research has shown differences in pro-social and pro-environmental attitudes after exposure to different physical environments. It is unclear whether these perspective shifts are associated with changes in conscious thoughts and feelings about other people and the environment. In Study 1, we used a within-subject experiment to measure social and environmental thought content throughout 1-h environmental explorations of a nature conservatory and an indoor mall. At three survey time points, participants (N = 86, undergraduates and community members) reported whom they were thinking about and how connected they felt to the physical and social environment. Using Bayesian multi-level models, we found that while visiting the conservatory, participants were less likely to think about themselves, felt closer to people nearby and around the world, and felt higher connectedness to their social and physical environment. In Study 2, we used a correlational design to investigate the association between perceived naturalness of city parks and feelings of connection to nearby others and the physical environment while visiting. Participants (N = 303, Chicago residents) reported feeling higher levels of connection to nearby people and the physical environment when they were visiting city parks rated as more natural. These studies further our understanding of the ways in which natural environments influence conscious thoughts and feelings about the social and physical environment. 

Abstract The current outbreak of COVID-19 poses an unprecedented global health and economic threat to interconnected human societies. Strategies for controlling the outbreak rely on social distancing and face covering measures which largely disconnect the social network fabric of cities. We demonstrate that early in the US outbreak, COVID-19 spread faster on average in larger cities and discuss the implications of these observations, emphasizing the need for faster responses to novel infectious diseases in larger cities. 

Abstract Crime is a costly societal issue. While many factors influence urban crime, one less-studied but potentially important factor is neighborhood greenspace. Research has shown that greenspace is often negatively associated with crime. Measuring residents’ use of greenspace, as opposed to mere physical presence, is critical to understanding this association. Here, we used cell phone mobility data to quantify local street activity and park visits in Chicago and New York City. We found that both factors were negatively associated with crime, while controlling for socio-demographic factors. Each factor explained unique variance, suggesting multiple pathways for the influence of street activity and greenspace on crime. Physical tree canopy had a smaller association with crime, and was only a significant predictor in Chicago. These findings were further supported by exploratory directed acyclic graph modeling, which found separate direct paths for both park visits and street activity to crime. 

Many eye-tracking data analyses rely on the Area-of-Interest (AOI) methodology, which utilizes AOIs to analyze metrics such as fixations. However, AOI-based methods have some inherent limitations including variability and subjectivity in shape, size, and location of AOIs. In this article, we propose an alternative approach to the traditional AOI dwell time analysis: Weighted Sum Durations (WSD). This approach decreases the subjectivity of AOI definitions by using Points-of-Interest (POI) while maintaining interpretability. In WSD, the durations of fixations toward each POI is weighted by the distance from the POI and summed together to generate a metric comparable to AOI dwell time. To validate WSD, we reanalyzed data from a previously published eye-tracking study (n = 90). The re-analysis replicated the original findings that people gaze less towards faces and more toward points of contact when viewing violent social interactions.