Time: 12:30 PM, May 21, 2026 (Thursday)
Location: Room 1100, 11th Floor, Lyu Dalong Building, Tsinghua University
1. Fei Wang’s Research Group
Presentation Topic: Study on Transcranial Magnetic Stimulation-Based Intervention for the Self-Advantage Effect
Presenter: Yongfa Zhang
Abstract：
From effortlessly remembering our own birthdays to being exceptionally sensitive to hearing our own names mentioned in conversation, our brains seem naturally wired to favor self-related information. This phenomenon is known as the "self-advantage effect." Regarding the neural mechanisms underlying the self-advantage effect, existing studies have identified multiple relevant brain regions; however, whether these regions represent the "cause" or the "effect" remains unclear, and we still lack the most critical causal evidence. In particular, a question that remains heavily debated in academia is whether external stimulation can be used to intervene in the activity of the neural network responsible for processing self-information, thereby altering self-cognition.
To address this, this study utilizes high-precision magnetic resonance imaging (MRI)-guided Transcranial Magnetic Stimulation (TMS) technology to directly modulate the core nodes within this key network and observe its causal impact on self-processing. To further uncover the underlying intervention mechanisms, this study integrates computational modeling approaches—such as the drift-diffusion model and machine learning—to conduct an in-depth analysis of the associated cognitive and neural mechanisms.
2. Yisi Zhang’s Research Group
Presentation Topic: Study on Collective Problem-Solving Behavior in Common Marmosets Based on a Touchscreen Paradigm
Presenter: Haoxin Xu
Abstract：Collective Problem Solving is a high-level manifestation of social intelligence, yet its underlying neurocomputational mechanisms remain poorly understood. Existing studies have demonstrated that collective advantages are widespread among animals such as fish, insects, and birds. However, most of these advantages can be explained by simple, local interaction rules, making them difficult to generalize to species with complex social structures (such as non-human primates).
To explore collective problem-solving behavior in non-human primates and its neural computational principles, this study established a touchscreen-based collaborative problem-solving behavioral paradigm for common marmosets. By quantitatively analyzing behavioral data under different task difficulties and social combinations, this project aims to systematically characterize the behavioral characteristics, interaction strategies, and cognitive boundaries of marmosets during collective problem solving, thereby providing a foundational framework for subsequent multi-brain neural recordings.
