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Crowding, a fundamental limit in object recognition, is believed to result from excessive integration of nearby items in peripheral vision. To understand its pooling mechanisms, we measured subjects’ internal response distributions in an orientation crowding task. Contrary to the prediction of an averaging model, we observed a pattern suggesting that the perceptual judgement is made based on choosing the largest response across the noise-perturbed items. A model featuring first-stage averaging and second-stage signed-max operation predicts the diverse errors made by human observers under various signal strength levels. These findings suggest that different rules operate to resolve the bottleneck at early and high-level stages of visual processing, implementing a combination of linear and nonlinear pooling strategies. [ABSTRACT FROM AUTHOR]

The latest Coupled Model Intercomparison Project Phase 6 (CMIP6) proposes new shared pathways (SSPs) that incorporate socioeconomic development with more comprehensive and scientific experimental designs; however, few studies have been performed on the projection of future multibasin hydrological changes in China based on CMIP6 models. In this paper, we use the Equidistant Cumulative Distribution Function method (EDCDFm) to perform downscaling and bias correction in daily precipitation, daily maximum temperature, and daily minimum temperature for six CMIP6 models based on the historical gridded data from the high-resolution China Meteorological Forcing Dataset (CMFD). We use the bias-corrected precipitation, temperature, and daily mean wind speed to drive the variable infiltration capacity (VIC) hydrological model, and study the changes in multiyear average annual precipitation, annual evapotranspiration and total annual runoff depth relative to the historical baseline period (1985–2014) for the Chinese mainland, basins and grid scales in the 21st century future under the SSP2-4.5 and SSP5-8.5 scenarios. The study shows that the VIC model accurately simulates runoff in major Chinese basins; the model data accuracy improves substantially after downscaling bias correction; and the future multimodel-mean multiyear average annual precipitation, annual evapotranspiration, and total annual runoff depth for the Chinese mainland and each basin increase relative to the historical period in near future (2020–2049) and far future (2070–2099) under the SSP2-4.5 and SSP5-8.5 scenarios. The new CMIP6-based results of this paper can provide a strong reference for extreme event prevention, water resource utilization and management in China in the 21st century. [ABSTRACT FROM AUTHOR]