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Weiping Yang

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YNIMG Journal 2025 Journal Article

Tasting emotions: An in-depth fmri study exploring gustatory and visual cross-modal associations across various spatio-temporal regions of the human brain

  • Jie Chen
  • Yuansheng Liu
  • Lina Huang
  • Luming Hu
  • Xueying Li
  • Liuqing Wei
  • Weiping Yang
  • Simin Zhao

This study investigates how taste influences emotional face recognition, focusing on the cross-modal interaction between gustatory and visual stimuli. While prior research has primarily examined how visual cues modulate taste perception, the reverse direction-how taste shapes visual processing in emotional contexts-remains underexplored. Using a combination of task-based functional MRI (task-fMRI) and resting-state fMRI (rs-fMRI), we examined the neural mechanisms by which taste modulates the perception of emotional faces. Behaviorally, sour tastes facilitated faster recognition of disgusted faces, while sweet tastes enhanced the detection of pleasant expressions. Neuroimaging results revealed that these emotionally congruent taste-face pairings elicited distinct activation patterns in the early visual cortex, including a significant interaction effect in the right calcarine gyrus (primary visual cortex, V1). Task-fMRI also showed modulation in the medial cingulate gyrus, fusiform gyrus, and superior frontal regions depending on emotional congruency. Resting-state fMRI revealed sustained alterations in intrinsic connectivity within the medial cingulate and paracingulate cortex following cross-modal dissonance, suggesting lasting neural effects beyond stimulus presentation. Together, these findings demonstrate the dynamic and enduring influence of taste on emotional face processing and offer novel insights into the neural basis of multisensory affective integration. By integrating task-based and resting-state fMRI, this study provides a comprehensive framework for understanding how affectively salient gustatory inputs shape social perception through both early perceptual and sustained neural mechanisms.

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NeurIPS Conference 2023 Conference Paper

Exploiting Contextual Objects and Relations for 3D Visual Grounding

  • Li Yang
  • Chunfeng Yuan
  • Ziqi Zhang
  • Zhongang Qi
  • Yan Xu
  • Wei Liu
  • Ying Shan
  • Bing Li

3D visual grounding, the task of identifying visual objects in 3D scenes based on natural language inputs, plays a critical role in enabling machines to understand and engage with the real-world environment. However, this task is challenging due to the necessity to capture 3D contextual information to distinguish target objects from complex 3D scenes. The absence of annotations for contextual objects and relations further exacerbates the difficulties. In this paper, we propose a novel model, CORE-3DVG, to address these challenges by explicitly learning about contextual objects and relations. Our method accomplishes 3D visual grounding via three sequential modular networks, including a text-guided object detection network, a relation matching network, and a target identification network. During training, we introduce a pseudo-label self-generation strategy and a weakly-supervised method to facilitate the learning of contextual objects and relations, respectively. The proposed techniques allow the networks to focus more effectively on referred objects within 3D scenes by understanding their context better. We validate our model on the challenging Nr3D, Sr3D, and ScanRefer datasets and demonstrate state-of-the-art performance. Our code will be public at https: //github. com/yangli18/CORE-3DVG.

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v2026.09.13