Journal Name: Cognitive Linguistics Issue: Ahead of print.

People implicitly associate the “past” and “future” with “front” and “back” in their minds according to their cultural attitudes toward time. As the temporal focus hypothesis proposes, future-oriented people tend to think about time according to the future-in-front mapping, whereas past-oriented people tend to think about time according to the past-in-front mapping. Whereas previous studies have demonstrated that culture exerts an important influence on people's implicit spatializations of time, we focus specifically on religion, a prominent layer of culture, as potential (...) additional influence on space-time mappings. In Experiment 1 and 2, we observed a difference between the two religious groups, with Buddhists being more past-focused and more frequently conceptualizing the past as ahead of them and the future as behind them, and Taoists more future-focused and exhibiting the opposite space-time mapping. In Experiment 3, we administered a religion prime, in which Buddhists were randomly assigned to visualize the picture of the Buddhas of the Past or the Future. Results showed that the pictorial icon of Dipamkara increased participants' tendency to conceptualize the past as in front of them. In contrast, the pictorial icon of Maitreya caused a dramatic increase in the rate of future-in-front responses. In Experiment 4, the causal effect of religion on implicit space-time mappings was replicated in atheists. Taken together, these findings provide converging evidence for the hypothesized causal role of religion for temporal focus in determining space-time mappings. (shrink)

Coal texture is important for predicting coal seam permeability and selecting favorable blocks for coalbed methane exploration. Drilled cores and mining seam observations are the most direct and effective methods of identifying coal texture; however, they are expensive and cannot be used in unexplored coal seams. Geophysical logging has become a common method of coal texture identification, particularly during the CBM mining stage. However, quantitative methods for identifying coal texture based on geophysical logging data require further study. The support vector (...) machine, a machine learning method, has received great interest due to its remarkable generalization performance, and has been employed to quantitatively identify hard and soft coal using geophysical logging data. In this study, four well-logging curves, the acoustic time difference, caliper log, density, and natural gamma were used for coal texture analysis. Hard coal exhibited higher DEN, GR, lower CAL, and lower AC than soft coal. The accuracy rate of coal texture identification was highest when the linear kernel function was applied, and the maximum training accuracy rate was achieved when the penalty parameter value of the linear kernel increased to 1. The results of verification with a newly cored CBM exploration well indicated that the SVM-based identification method was effective for coal texture analysis. With the increasing availability of data, this method can be employed to distinguish hard and soft coal in a coal-bearing basin under numerous sample learning conditions. (shrink)

ABSTRACTThe Moving Time metaphor and the Moving Ego metaphor are common in English speakers’ conceptualization of time. Previous research has suggested a broad range of factors influencing people’s...