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Studies have shown that verbal humor takes longer processing time than non-humorous materials. Since processing time is often used to assess cognitive resource consumption, many researchers believe that verbal humor processing requires more cognitive resources than non-humor processing. However, some studies yielded the opposite result, so it is still controversial whether verbal humor processing consumes more cognitive resources than non-humor processing. Cognitive resources from a limited pool can be automatically allocated to meet the task demands in light of the difficulty of the tasks and the level of performance. Thus, in the current behavioral study, we employed a dual-task paradigm to access the cognitive resource consumption of verbal-humor processing by examining the performance of a subsequent task and further to identify which element, cognitive element or affective element, contributes more to the resource consumption. The current study utilized a 3×2 within-subject design with sentence type (humorous, positive, neutral) and arithmetical operation (simple, difficult) as the independent variables, and Reaction Times (RTs) and accuracy of the arithmetical operation as dependent variables. By collecting and analyzing the data of 82 participants, we found that RTs of arithmetical operations after verbal-humor processing were significantly longer than those after positive and neutral sentence processing, and there was an interaction effect between sentence type and task difficulty, which indicated that verbal-humor processing consumed more cognitive resources than non-humor processing. Whereas, the RTs after processing positive sentences were significantly shorter than those after neutral sentences, suggesting that resource consumption was not attributed to positive emotion arousal. In conclusion, the current study confirms that verbal-humor processing did consume more cognitive resources than non-humorous materials, and although both the cognitive element a

Stacking is the process of overlaying inferred species potential distributions for multiple species based on outputs of bioclimatic envelope models (BEMs). The approach can be used to investigate patterns and processes of species richness. If data limitations on individual species distributions are inevitable, but how do they affect inferences of patterns and processes of species richness? We investigate the influence of different data sources on estimated species richness gradients in China. We fitted BEMs using species distributions data for 334 bird species obtained from (1) global range maps, (2) regional checklists, (3) museum records and surveys, and (4) citizen science data using presence-only (Mahalanobis distance), presence-background (MAXENT), and presence–absence (GAM and BRT) BEMs. Individual species predictions were stacked to generate species richness gradients. Here, we show that different data sources and BEMs can generate spatially varying gradients of species richness. The environmental predictors that best explained species distributions also differed between data sources. Models using citizen-based data had the highest accuracy, whereas those using range data had the lowest accuracy. Potential richness patterns estimated by GAM and BRT models were robust to data uncertainty. When multiple data sets exist for the same region and taxa, we advise that explicit treatments of uncertainty, such as sensitivity analyses of the input data, should be conducted during the process of modeling.