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Remote work presents a challenge to workers’ creativity, especially during the COVID-19 pandemic and the stay-at-home requirements. Individual differences in creativity, considered through the lens of distributional models, and their stability across different conditions are unknown. We assess the between-person variability in common metrics of creativity, despite sharing similar experiences of virtual reality and mindfulness. The paper also assesses the stability of an individual’s creativity over time. We measured the creativity of 20 remote-workers daily, during a 9-week study. Creativity was measured with respect to divergent thinking and convergent thinking. Distributional models show significant individual differences in variability of creativity. Stability analyses also revealed that individuals’ creativity is relatively unstable over time— both within and across conditions. Although one measure of divergent creativity was relatively stable, the other was not. We suggest more research should assess the extent of variability in creativity relative to individual differences and under different conditions. 

We explore the transfer of control from an automated vehicle to the driver. Based on data from N=19 participants who participated in a driving simulator experiment, we find evidence that the transfer of control often does not take place in one step. In other words, when the automated system requests the transfer of control back to the driver, the driver often does not simply stop the non-driving task. Rather, the transfer unfolds as a process of interleaving the non-driving and driving tasks. We also find that the process is moderated by the length of time available for the transfer of con- trol: interleaving is more likely when more time is available. Our interface designs for automated vehicles must take these results into account so as to allow drivers to safely take back control from automation. 

Abstract PANTHER (Protein Analysis Through Evolutionary Relationships, http://www.pantherdb.org) is a resource for the evolutionary and functional classification of protein-coding genes from all domains of life. The evolutionary classification is based on a library of over 15,000 phylogenetic trees, and the functional classifications include Gene Ontology terms and pathways. Here, we analyze the current coverage of genes from genomes in different taxonomic groups, so that users can better understand what to expect when analyzing a gene list using PANTHER tools. We also describe extensive improvements to PANTHER made in the past two years. The PANTHER Protein Class ontology has been completely refactored, and 6101 PANTHER families have been manually assigned to a Protein Class, providing a high level classification of protein families and their genes. Users can access the TreeGrafter tool to add their own protein sequences to the reference phylogenetic trees in PANTHER, to infer evolutionary context as well as fine-grained annotations. We have added human enhancer-gene links that associate non-coding regions with the annotated human genes in PANTHER. We have also expanded the available services for programmatic access to PANTHER tools and data via application programming interfaces (APIs). Other improvements include additional plant genomes and an updated PANTHER GO-slim.