High reward enhances perceptual learning.

Studies of perceptual learning have revealed a great deal of plasticity in adult humans. In this study, we systematically investigated the effects and mechanisms of several forms (trial-by-trial, block, and session rewards) and levels (no, low, high, subliminal) of monetary reward on the rate, magnitude, and generalizability of perceptual learning. We found that high monetary reward can greatly promote the rate and boost the magnitude of learning and enhance performance in untrained spatial frequencies and eye without changing interocular, interlocation, and interdirection transfer indices. High reward per se made unique contributions to the enhanced learning through improved internal noise reduction. Furthermore, the effects of high reward on perceptual learning occurred in a range of perceptual tasks. The results may have major implications for the understanding of the nature of the learning rule in perceptual learning and for the use of reward to enhance perceptual learning in practical applications

Learning Granularity-Unified Representations for Text-to-Image Person Re-identification

Text-to-image person re-identification (ReID) aims to search for pedestrian images of an interested identity via textual descriptions. It is challenging due to both rich intra-modal variations and significant inter-modal gaps. Existing works usually ignore the difference in feature granularity between the two modalities, i.e., the visual features are usually fine-grained while textual features are coarse, which is mainly responsible for the large inter-modal gaps. In this paper, we propose an end-to-end framework based on transformers to learn granularity-unified representations for both modalities, denoted as LGUR. LGUR framework contains two modules: a Dictionary-based Granularity Alignment (DGA) module and a Prototype-based Granularity Unification (PGU) module. In DGA, in order to align the granularities of two modalities, we introduce a Multi-modality Shared Dictionary (MSD) to reconstruct both visual and textual features. Besides, DGA has two important factors, i.e., the cross-modality guidance and the foreground-centric reconstruction, to facilitate the optimization of MSD. In PGU, we adopt a set of shared and learnable prototypes as the queries to extract diverse and semantically aligned features for both modalities in the granularity-unified feature space, which further promotes the ReID performance. Comprehensive experiments show that our LGUR consistently outperforms state-of-the-arts by large margins on both CUHK-PEDES and ICFG-PEDES datasets. Code will be released at https://github.com/ZhiyinShao-H/LGUR.Comment: Accepted by ACM Multimedia 202

Gender-Differential Associations between Attention Deficit and Hyperactivity Symptoms and Youth Health Risk Behaviors

Attention deficit and hyperactivity disorder (ADHD) is one of the common developmental disorders that generally receives clinical attention at learning ages, and some symptoms may persist in young adulthood.1 Past research has demonstrated a consistent association between ADHD and youth health risk behaviors (e.g., cigarette smoking), which often develop during adolescence and contribute to early morbidity and mortality among young adults.2 However, ADHD symptoms are not routinely screened in adolescents and emerging adults during their visits to healthcare providers.3 The six-item Adult Self-Report Scale (ASRS-6) for ADHD has been validated in the young population for screening purposes.4 This short form is time-saving and also provides a comparable predictivity of ADHD diagnosis as that of the original long version.5 Although accumulating evidence has demonstrated the association between ADHD symptoms and youth health risk behaviors, this issue has scarcely been explored in the Taiwanese youth population.6 Therefore, this study was conducted to validate the psychometric property of the Chinese version of ASRS-6 and examine the gender-stratified association between ADHD symptoms and youth health risk behaviors