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Crowdsourcing platforms have traditionally been designed with a focus on workstation interfaces, restricting the flexibility that crowdworkers need. Recognizing this limitation and the need for more adaptable platforms, prior research has highlighted the diverse work processes of crowdworkers, influenced by factors such as device type and work stage. However, these variables have largely been studied in isolation. Our study is the first to explore the interconnected variabilities among these factors within the crowdwork community. Through a survey involving 150 Amazon Mechanical Turk crowdworkers, we uncovered three distinct groups characterized by their interrelated variabilities in key work aspects. The largest group exhibits a reliance on traditional devices, showing limited interest in integrating smartphones and tablets into their work routines. The second-largest group also primarily uses traditional devices but expresses a desire for supportive tools and scripts that enhance productivity across all devices, particularly smartphones and tablets. The smallest group actively uses and strongly prefers non-workstation devices, especially smartphones and tablets, for their crowdworking activities. We translate our findings into design insights for platform developers, discussing the implications for creating more personalized, flexible, and efficient crowdsourcing environments. Additionally, we highlight the unique work practices of these crowdworker clusters, offering a contrast to those of more traditional and established worker groups. 

Medial entorhinal cortex (MEC) supports a wide range of navigational and memory related behaviors. Well-known experimental results have revealed specialized cell types in MEC — e.g. grid, border, and head-direction cells — whose highly stereotypical response profiles are suggestive of the role they might play in sup- porting MEC functionality. However, the majority of MEC neurons do not exhibit stereotypical firing patterns. How should the response profiles of these more “het- erogeneous” cells be described, and how do they contribute to behavior? In this work, we took a computational approach to addressing these questions. We first performed a statistical analysis that shows that heterogeneous MEC cells are just as reliable in their response patterns as the more stereotypical cell types, suggest- ing that they have a coherent functional role. Next, we evaluated a spectrum of candidate models in terms of their ability to describe the response profiles of both stereotypical and heterogeneous MEC cells. We found that recently developed task-optimized neural network models are substantially better than traditional grid cell-centric models at matching most MEC neuronal response profiles — including those of grid cells themselves — despite not being explicitly trained for this pur- pose. Specific choices of network architecture (such as gated nonlinearities and an explicit intermediate place cell representation) have an important effect on the ability of the model to generalize to novel scenarios, with the best of these models closely approaching the noise ceiling of the data itself. We then performed in silico experiments on this model to address questions involving the relative functional relevance of various cell types, finding that heterogeneous cells are likely to be just as involved in downstream functional outcomes (such as path integration) as grid and border cells. Finally, inspired by recent data showing that, going beyond their spatial response selectivity, MEC cells are also responsive to non-spatial rewards, we introduce a new MEC model that performs reward-modulated path integration. We find that this unified model matches neural recordings across all variable-reward conditions. Taken together, our results point toward a conceptually principled goal-driven modeling approach for moving future experimental and computational efforts beyond overly-simplistic single-cell stereotypes. 

Phenology is a key biological trait of an organism’s success and is one of the best indicators of its response to recent climate change. Plants are among the most well-studied organisms in this regard, but observational data bearing on this topic are largely restricted to woody species of the northern hemisphere, mostly from ca. the last three decades. Recent research has demonstrated that mobilized online herbarium specimens provide important, albeit mostly neglected, information on plant phenology. Here, we use the web tool CrowdCurio to crowdsource phenological data from more than 10,000 herbarium specimens representing 30 flowering plant species broadly distributed across the eastern United States. Our results, spanning 120 years and generated from over 2,000 crowdsourcers, clarify numerous aspects of plant phenology. First, they reveal that plant reproductive phenology is significantly advancing in response to warming, which is consistent with previous studies. Second, among those species with broad latitudinal ranges, populations from more southern latitudes are significantly more phenologically sensitive to temperature than those from northern populations. Last, contrary to some recent findings, plants in warmer, less variable climates may be much more dynamic, on average, in their phenological sensitivity. Our results are robust to a variety of confounding factors and span large phylogenetic distances and myriad life histories. These may represent more global trends in the latitudinal gradient of phenological response with myriad potential ecological and evolutionary consequences, and leads us to hypothesize that phenological sensitivity across species' ranges is driven by adaptation to local climates.