Perspective-taking is an important ability to imagine the world from another’s point of view. Prior studies have shown that younger adults are more likely to consider the opinions of age-based in-group members relative to out-group members. However, the cause of this priority is still unknown. We conducted three independent studies to explore the effect of intergenerational contact on younger adults’ PT toward older adults and the possible roles of stereotyping and intergroup anxiety. A total of 192 college students completed the (...) Perspective taking Scale in Study 1 after being primed with age-based intergroup relationships. The results indicated that younger adults found it more difficult to take the perspective of older adults than that of their peers. 200 college students completed the Prior Contact Scale, Intergroup Anxiety Scale, Negative Stereotype Scale, and PT Scale in Study 2. The results demonstrated that intergenerational contact improved PT toward older adults by disrupting negative stereotypes, and intergroup anxiety moderated this mediating relationship. A total of 215 college students completed the PT Scale in the context of imagining intergenerational contact in Study 3. Interestingly, imagined contact effectively increased younger adults’ ability to take older adults’ perspectives. The present research verifies that contact is important for influencing younger adults’ emotional and attitudinal factors that are critical to improving younger adults’ ability to take older people’s perspectives. This is of great significance for developing harmonious intergenerational relationships. (shrink)

The brain-computer interface based on functional near-infrared spectroscopy has received more and more attention due to its vast application potential in emotion recognition. However, the relatively insufficient investigation of the feature extraction algorithms limits its use in practice. In this article, to improve the performance of fNIRS-based BCI, we proposed a method named R-CSP-E, which introduces EEG signals when computing fNIRS signals’ features based on transfer learning and ensemble learning theory. In detail, we used the Independent Component Analysis algorithm for (...) the correspondence between the sources of the two signals. We then introduced the EEG signals when computing the spatial filter based on a modified Common Spatial Pattern algorithm. Experimental results on public datasets show that the proposed method in this paper outperforms traditional methods without transfer. In general, the mean classification accuracy can be increased by up to 5%. To our knowledge, it is an innovation that we tried to apply transfer learning between EEG and fNIRS. Our study’s findings not only prove the potential of the transfer learning algorithm in cross-model brain-computer interface, but also offer a new and innovative perspective to research the hybrid brain-computer interface. (shrink)