Liaoning Normal University

Little is known about whether direct and vicarious rewards affect bilingual language control in social learning. We used a dual-electroencephalogram (EEG) to simultaneously record the effects of direct and vicarious rewards on language control when bilinguals switched between their two languages. We found that both direct and vicarious rewards elicited more switch behavior. On an electrophysiological level, although both direct and vicarious rewards elicited Reward-positivity and Feedback-P3 when receiving reward outcomes, direct rewards induced greater reward effects than vicarious rewards. In addition to an N2 effect in language switching, vicarious rewards elicited more pronounced LPCs relative to direct rewards. More important, in the alpha band, there was a predictive effect of behaviors on rewards in binding vicarious rewards and language switching activities. These findings demonstrate that both direct and vicarious rewards influence language control during language selection.

Few-shot learning is one of the important research areas in machine learning. It aims to train models with extremely limited labeled samples and generalize to unseen categories or tasks. The key challenges are learning class-specific representations and modeling relationships between samples under limited supervision. In addition, data perturbations tend to have a more pronounced impact in few-shot settings. To address these challenges, we propose Cross-Memory Attention (CMA). CMA extracts memory features from both the support and query sets and integrates the support memories into the query representations. In this way, our method models long-range dependencies between the support and query sets while maintaining a lower parameter count than standard Transformer-based designs, thereby addressing relationship modeling between samples. Additionally, the Domain Adaptation Module mitigates the impact of data perturbations by training an additional branch with perturbed data. The module constructs a learnable classification space, whose introduction overcomes the limitations of fixed, non-learnable metric learning in the classification space. To enable the use of our Cross-Memory Attention in 5-shot scenarios, we propose a Multi-sample Adaptive Fusion Module that can be applied to any multi-sample learning framework. This module effectively extracts common features from multiple samples, making it versatile and adaptable. Finally, we conduct extensive experiments on four public datasets, validating the effectiveness of our model. The code is made publicly available at https://github.com/Mrliujunwen/cross-memory-fewshot.

Implicit emotion regulation has attracted increasing attention in cognitive neuroscience, and the Shifted-Attention Emotion Appraisal Task (SEAT) provides a well-established paradigm to probe emotion processing and incidental regulation without requiring explicit down-regulation instructions. However, the reproducible neural correlates of distinct SEAT components remain unclear across fMRI studies. We therefore conducted a voxel-wise coordinate-based meta-analysis using Seed-based d Mapping with Permutation of Subject Images (SDM-Psi v6.21) on 10 studies (N = 489), comprising 24 eligible experiments and 152 reported peak coordinates. A random-effects mean analysis was performed, using a commonly applied SDM threshold (voxel-wise p < 0.005, cluster extent ≥ 10 voxels). Across studies, Implicit Emotional Processing in SEAT consistently engaged the medial superior frontal gyrus and the putamen/striatum. Emotion Modulation by Attention Shifting showed convergent activations in the middle frontal gyrus and dorsolateral superior frontal regions. Emotion Modulation by Appraisal reliably recruited dorsolateral prefrontal cortex, the triangular inferior frontal gyrus, and the insula, alongside additional lateral prefrontal clusters. Together, these findings indicate that SEAT elicits reproducible activation patterns involving medial and lateral prefrontal regions as well as subcortical and salience-related structures, supporting its utility for studying implicit emotion processing and regulation.