The goal of the present study was to investigate age-related changes in attentional allocation for shared task representations during joint performance;event-related potentials were recorded while participants perform...The goal of the present study was to investigate age-related changes in attentional allocation for shared task representations during joint performance;event-related potentials were recorded while participants performed a modified visual three-stimulus oddball task, both alone and together with another participant. Younger adults and older adults (14 each) participated in the study. Participants were required to identify rare target stimuli while ignoring frequent standards, as well as rare non-targets assigned to a partner’s action (<i>i.e</i>., no-go stimuli for one’s own task). ERP component, nogo-P3 and P3b were measured to investigate the inhibition and the attentional allocation to the partner’s stimuli. Results showed that younger adults elicited larger frontal nogo P3 and parietal P3b for non-targets in the joint than in the individual condition. Contrary to expectation, older adults induced frontal no-go P3 in the joint condition not in the individual condition. In the sharing of the task with another, the result suggested that the efficiency of matching of incoming information with the representation of the other’s task declined with age, whereas aging did not affect the suppression of incorrect preparation of motor responses instigated by this representation.</i.i.e.<>展开更多
The pansharpening process is for obtaining an enhanced image with both high spatial and high spectral resolutions by fusing a panchromatic（PAN） image and a low spatial resolution multispectral（MS） image. Sparse Pr...The pansharpening process is for obtaining an enhanced image with both high spatial and high spectral resolutions by fusing a panchromatic（PAN） image and a low spatial resolution multispectral（MS） image. Sparse Principal Component Analysis（SPCA） method has been proposed as a pansharpening method, which utilizes sparse coefficients and over-complete dictionaries to represent the remote sensing data. However, this method still has some drawbacks, such as the existence of the block effect. In this paper, based on SPCA, we propose the Sparse over Shared Coefficients（SSC）, in which patches are extracted with a sliding distance of 1 pixel from a PAN image, and the MS image shares the sparse representation coefficients trained from the PAN image independently.The fused high-resolution MS image is reconstructed by K-SVD algorithm and iterations, and residual compensation is applied when the down-sampling constraint is not satisfied. The simulated experiment results demonstrate that the proposed SSC method outperforms SPCA and improves the overall effectiveness.展开更多
文摘The goal of the present study was to investigate age-related changes in attentional allocation for shared task representations during joint performance;event-related potentials were recorded while participants performed a modified visual three-stimulus oddball task, both alone and together with another participant. Younger adults and older adults (14 each) participated in the study. Participants were required to identify rare target stimuli while ignoring frequent standards, as well as rare non-targets assigned to a partner’s action (<i>i.e</i>., no-go stimuli for one’s own task). ERP component, nogo-P3 and P3b were measured to investigate the inhibition and the attentional allocation to the partner’s stimuli. Results showed that younger adults elicited larger frontal nogo P3 and parietal P3b for non-targets in the joint than in the individual condition. Contrary to expectation, older adults induced frontal no-go P3 in the joint condition not in the individual condition. In the sharing of the task with another, the result suggested that the efficiency of matching of incoming information with the representation of the other’s task declined with age, whereas aging did not affect the suppression of incorrect preparation of motor responses instigated by this representation.</i.i.e.<>
文摘The pansharpening process is for obtaining an enhanced image with both high spatial and high spectral resolutions by fusing a panchromatic（PAN） image and a low spatial resolution multispectral（MS） image. Sparse Principal Component Analysis（SPCA） method has been proposed as a pansharpening method, which utilizes sparse coefficients and over-complete dictionaries to represent the remote sensing data. However, this method still has some drawbacks, such as the existence of the block effect. In this paper, based on SPCA, we propose the Sparse over Shared Coefficients（SSC）, in which patches are extracted with a sliding distance of 1 pixel from a PAN image, and the MS image shares the sparse representation coefficients trained from the PAN image independently.The fused high-resolution MS image is reconstructed by K-SVD algorithm and iterations, and residual compensation is applied when the down-sampling constraint is not satisfied. The simulated experiment results demonstrate that the proposed SSC method outperforms SPCA and improves the overall effectiveness.
