Social Influence in Social-Ecological Systems: Collaborative Capacity and Inter-Organizational Networks in the U.S. Sagebrush.

Collaborative governance has become a prominent, if not dominant, framework for thinking about multi-scalar and cross-jurisdictional environmental management. The literature broadly and consistently suggests that collaborative capacity and inter-organizational networks provide the institutional framework for addressing social-ecological system challenges. Surprisingly little scholarship addresses processes of social influence (or contagion) in social-ecological systems writ large, or more specifically as it relates to collaborative capacity.

Fine-scale monitoring and mapping of biodiversity and ecosystem services reveals multiple synergies and few tradeoffs in urban green space management.

Urban watersheds can play a critical role in supporting biodiversity and ecosystem services in a rapidly changing world. However, managing for multiple environmental and social objectives in urban landscapes is challenging, especially if the optimization of one ecosystem service conflicts with another. Urban ecology research has frequently been limited to a few indicators - typically either biodiversity or ecosystem service indices - making tradeoffs and synergies difficult to assess.

The eye-mind wandering link: Identifying gaze indices of mind wandering across tasks.

During mind wandering, visual processing of external information is attenuated. Accordingly, mind wandering is associated with changes in gaze behaviors, albeit findings are inconsistent in the literature. This heterogeneity obfuscates a complete view of the moment-to-moment processing priorities of the visual system during mind wandering.

An automated behavioral measure of mind wandering during computerized reading.

Mind wandering is a ubiquitous phenomenon in which attention shifts from task-related to task-unrelated thoughts. The last decade has witnessed an explosion of interest in mind wandering, but research has been stymied by a lack of objective measures, leading to a near-exclusive reliance on self-reports. We addressed this issue by developing an eye-gaze-based, machine-learned model of mind wandering during computerized reading.