Ayers Ayers (bagdad16)

Social anxiety is a future-oriented mood characterized by apprehension about others' negative evaluations in anxiety-provoking social situations that may occur in the future. Episodic future thinking (EFT) is a form of future-oriented cognition that allows a pre-experiencing of our personal futures. The literature suggests that anxious individuals show increased negative expectancies about future events. However, few studies have been conducted on EFT in social anxiety. The current study investigated the phenomenological characteristics of EFT in adolescents with high and low social anxiety. Twenty-two high social anxiety (HSA) and 24 low social anxiety (LSA) adolescents simulated one anxiety-provoking social event and one neutral event. They then rated the phenomenological characteristics of the events. HSA adolescents imagined anxiety-provoking social events from an observer perspective more than LSA adolescents. HSA adolescents also imagined anxiety-provoking social events as more negative and containing less clear contextual details than LSA adolescents. In contrast, no group differences were found for neutral events. Moreover, participants imagined more self-referential information for anxiety-provoking social events than neutral events. HSA participants imagined less other-referential information than LSA participants, regardless of the event type. This study used a subclinical sample with high and low social anxiety. The sample size was small, and only adolescents aged 15-17 years were included. It is difficult to generalize the present findings across different anxiety-provoking social events. The specificity of EFT was not evaluated. Our findings highlight the importance of EFT in the psychopathology of adolescent social anxiety. Our findings highlight the importance of EFT in the psychopathology of adolescent social anxiety.Datasets with missing values are very common in real world applications. GAIN, a recently proposed deep generative model for missing data imputation, has been proved to outperform many state-of-the-art methods. But GAIN only uses a reconstruction loss in the generator to minimize the imputation error of the non-missing part, ignoring the potential category information which can reflect the relationship between samples. STAT inhibitor In this paper, we propose a novel unsupervised missing data imputation method named PC-GAIN, which utilizes potential category information to further enhance the imputation power. Specifically, we first propose a pre-training procedure to learn potential category information contained in a subset of low-missing-rate data. Then an auxiliary classifier is determined using the synthetic pseudo-labels. Further, this classifier is incorporated into the generative adversarial framework to help the generator to yield higher quality imputation results. The proposed method can improve the imputation quality of GAIN significantly. Experimental results on various benchmark datasets show that our method is also superior to other baseline approaches. Our code is available at https//github.com/WYu-Feng/pc-gain.In order to become proficient native speakers, children have to learn the morpho-syntactic relations between distant elements in a sentence, so-called non-adjacent dependencies (NADs). Previous research suggests that NAD learning in children comprises different developmental stages, where until 2 years of age children are able to learn NADs associatively under passive listening conditions, while starting around the age of 3-4 years children fail to learn NADs during passive listening. To test whether the transition between these developmental stages occurs gradually, we tested children's NAD learning in a foreign language using event-related potentials (ERPs). We found ERP evidence of NAD learning across the ages of 1, 2 and 3 years. The amplitude of the ERP effect indexing NAD learning, however, decreased with age. The