One size does not fit all: Decomposing the implementation and differential benefits of social emotion regulation strategies.

Although considerable research has demonstrated the importance of social relationships for well-being, limited work has assessed how people help regulate each other's emotions, a process called social emotion regulation. The present research utilized two experiments in 2020 (N1 = 50, N2 = 268) where people shared and responded to personal experiences to examine: (a) the kinds of regulatory support people offered others; (b) how people felt receiving different types of social feedback about their experiences; and (c) whether the support they offered others shaped how they felt receiving different feedback. When providing feedback to a confederate, participants varied in whether they chose to use validation, which affirms someone's feelings, or one of three types of social reappraisals, which help others change how they think about an emotional experience (i.e., temporal distancing: emphasizing how things change over time; positive focus: focusing on the bright side; and perspective taking: considering others' perspectives). Across studies, when participants received feedback about their own experiences, validation was the most comforting and preferred feedback. In Study 2, temporal distancing emerged as the most comforting, helpful, and preferrable type of social reappraisal and was the only reappraisal perceived as no less helpful than validation. Additionally, participants who provided social reappraisal to the confederate benefited most from receiving this type of support from others. Together, these results highlight the variability in how people use social emotion regulation strategies to support others and demonstrate how such differences in implementation, as well as individual differences in those receiving support, can shape social regulatory outcomes. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Data-Dependent Label Distribution Learning for Age Estimation.

As an important and challenging problem in computer vision, face age estimation is typically cast as a classification or regression problem over a set of face samples with respect to several ordinal age labels, which have intrinsically cross-age correlations across adjacent age dimensions. As a result, such correlations usually lead to the age label ambiguities of the face samples. Namely, each face sample is associated with a latent label distribution that encodes the cross-age correlation information on label ambiguities. Motivated by this observation, we propose a totally data-driven label distribution learning approach to adaptively learn the latent label distributions. The proposed approach is capable of effectively discovering the intrinsic age distribution patterns for cross-age correlation analysis on the basis of the local context structures of face samples. Without any prior assumptions on the forms of label distribution learning, our approach is able to flexibly model the sample-specific context aware label distribution properties by solving a multi-task problem, which jointly optimizes the tasks of age-label distribution learning and age prediction for individuals. Experimental results demonstrate the effectiveness of our approach.