Object similarity can improve visual working memory (VWM) performance in the change-detection task, but impair the recognition performance when it occurs at retrieval of VWM in the recognition task. The effect of direction similarity is an issue that has not been well resolved. Furthermore, electrophysiological evidence in support of the mechanisms that underlie the effects of similarity is still scarce. In the current study, we conducted three behavioural experiments to examine the effects of direction similarity on memory performance with regard to both the encoding and retrieval phases of VWM and one event-related potential (ERP) experiment to explore the neural signatures of direction similarity in VWM. Our behavioural studies indicated that direction similarity improved performance when it occurred at the encoding phase but impaired performance when it occurred at the retrieval phase. Moreover, the ERP experiment showed that the amplitude of the contralateral delay activity (CDA) increased with the increasing set size for similar but not dissimilar directions. In addition, the CDA amplitude for similar directions was lower than that for dissimilar directions at set size 2. Taken together, these findings suggest that direction similarity at encoding has a positive effect on VWM performance and at retrieval has a negative effect. Given that VWM capacity depends on information load and the number of objects, the positive effect of similarity may be attributed to reduced information load of memory objects.
Color has an important role in object recognition and visual working memory (VWM). Decoding color VWM in the human brain is helpful to understand the mechanism of visual cognitive process and evaluate memory ability. Recently, several studies showed that color could be decoded from scalp electroencephalogram (EEG) signals during the encoding stage of VWM, which process visible information with strong neural coding. Whether color could be decoded from other VWM processing stages, especially the maintaining stage which processes invisible information, is still unknown. Here, we constructed an EEG color graph convolutional network model (ECo-GCN) to decode colors during different VWM stages. Based on graph convolutional networks, ECo-GCN considers the graph structure of EEG signals and may be more efficient in color decoding. We found that (1) decoding accuracies for colors during the encoding, early, and late maintaining stages were 81.58%, 79.36%, and 77.06%, respectively, exceeding those during the pre-stimuli stage (67.34%), and (2) the decoding accuracy during maintaining stage could predict participants’ memory performance. The results suggest that EEG signals during the maintaining stage may be more sensitive than behavioral measurement to predict the VWM performance of human, and ECo-GCN provides an effective approach to explore human cognitive function.
Purpose
This paper aims to propose a normal and tangential contact stiffness model to investigate the contact characteristics between rough surfaces of machined joints based on fractal geometry and contact mechanics theory considering surface asperities interaction.
Design/methodology/approach
The fractal geometry theory describes surface topography and Hertz contact theory derives the asperities elastic, elastic-plastic and plastic contact deformation. The joint normal and tangential contact stiffness are obtained. The experiment method for normal and tangential contact stiffness are introduced.
Findings
The relationship between dimensionless normal contact load and dimensionless normal and tangential contact stiffness are analyzed in different plasticity index. The results show that they are nonlinear relationships. The normal and tangential contact stiffness are obtained based on theoretical and experimental methods for milling and grinding machined specimens. The results indicate that the present model for the normal and tangential contact stiffness are consistent with experimental data, respectively.
Originality/value
The normal and tangential contact stiffness models are constructed by using the fractal geometry and the contact mechanics theory considering surface asperities interaction, which includes fully elastic, elastic-plastic and fully plastic contacts deformation. The present method can generate a more reliable calculation result as compared with the contact model no-considering asperities interaction.
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