Stereovision is the ability to perceive fine depth variations from small differences in the two eyes' images. Using adaptive optics, we show that even minute optical aberrations that are not clinically correctable, and go unnoticed in everyday vision, can affect stereo acuity. Hence, the human binocular system is capable of using fine details that are not experienced in everyday vision. Interestingly, stereo acuity varied considerably across individuals even when they were provided identical perfect optics. We also found that individuals' stereo acuity is better when viewing with their habitual optics rather than someone else's (better) optics. Together, these findings suggest that the visual system compensates for habitual optical aberrations through neural adaptation and thereby optimizes stereovision uniquely for each individual. Thus, stereovision is limited by small optical aberrations and by neural adaptation to one's own optics.
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http://dx.doi.org/10.1073/pnas.2100126118 | DOI Listing |
Neurosciences (Riyadh)
January 2025
From the Department of Basic Medical Sciences, College of Medicine, Taibah University, Madinah, Kingdom of Saudi Arabia.
The hippocampus, noted as (HC), plays a crucial role in the processes of learning, memory formation, and spatial navigation. Recent research reveals that this brain region can undergo structural and functional changes due to environmental exposures, including stress, noise pollution, sleep deprivation, and microgravity. This review synthesizes findings from animal and human studies, emphasizing the HC's plasticity in response to these factors.
View Article and Find Full Text PDFNeural Netw
January 2025
School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China; Ministry of Education Key Laboratory for Intelligent Networks and Network Security, Xi'an Jiaotong University, Xi'an, 710049, China. Electronic address:
Graph Neural Networks (GNNs) have received extensive research attention due to their powerful information aggregation capabilities. Despite the success of GNNs, most of them suffer from the popularity bias issue in a graph caused by a small number of popular categories. Additionally, real graph datasets always contain incorrect node labels, which hinders GNNs from learning effective node representations.
View Article and Find Full Text PDFPsychol Sport Exerc
January 2025
Department of magnetic resonance imaging, Beijing Shijitan Hospital, Capital Medical University, 100038 Beijing, China. Electronic address:
Soccer is a sport that requires athletes to be constantly aware of rapidly changing and unpredictable environments and to react adaptively. Previous studies have found that soccer players typically exhibit a vigilance advantage, but the underlying cognitive and neural basis for this is unclear. In this study, 27 soccer players, 17 age-matched artistic gymnasts, and 57 college students were recruited to participate in a psychomotor vigilance task.
View Article and Find Full Text PDFNeural Netw
January 2025
School of Computer Science and Technology, East China Normal University, 200062, Shanghai, China.
Real-world image super-resolution (RISR) has received increased focus for improving the quality of SR images under unknown complex degradation. Existing methods rely on the heavy SR models to enhance low-resolution (LR) images of different degradation levels, which significantly restricts their practical deployments on resource-limited devices. In this paper, we propose a novel Dynamic Channel Splitting scheme for efficient Real-world Image Super-Resolution, termed DCS-RISR.
View Article and Find Full Text PDFNeural Netw
January 2025
Department of Electronic Engineering, Tsinghua University, Beijing, China. Electronic address:
Out-of-graph node representation learning aims at learning about newly arrived nodes for a dynamic graph. It has wide applications ranging from community detection, recommendation system to malware detection. Although existing methods can be adapted for out-of-graph node representation learning, real-world challenges such as fixed in-graph node embedding and data diversity essentially limit the performance of these methods.
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