标题： 关注你：基于脑启发的个性化AI模型 显示英文标题

标题： Shifting Attention to You: Personalized Brain-Inspired AI Models

摘要： 人与人工智能的融合为深化我们对信息处理的理解提供了强有力的途径，因为每个系统都能提供独特的计算见解。 然而，尽管人机集成有着广阔的前景，当前的AI模型主要是在大规模数据集上训练的，优化的是群体层面的表现，并缺乏机制来使它们的计算与个体用户的感知语义和神经动态相匹配。 在这里，我们展示了将人类行为洞察和毫秒级神经数据整合到经过微调的CLIP基础模型中，不仅能够捕捉感知的一般化和个性化方面，而且相比未修改的CLIP基线，行为表现提高了两倍以上。 通过在训练过程中嵌入人类归纳偏置并镜像动态神经过程，个性化神经微调改进了对人类相似性判断的预测，并追踪了个体神经反应的时间演变。 我们的工作建立了一个新颖且可解释的框架，用于设计自适应AI系统，在神经科学、个性化医疗和人机交互领域具有广泛的意义。 显示英文摘要

摘要： The integration of human and artificial intelligence offers a powerful avenue for advancing our understanding of information processing, as each system provides unique computational insights. However, despite the promise of human-AI integration, current AI models are largely trained on massive datasets, optimized for population-level performance, lacking mechanisms to align their computations with individual users' perceptual semantics and neural dynamics. Here we show that integrating human behavioral insights and millisecond scale neural data within a fine tuned CLIP based model not only captures generalized and individualized aspects of perception but also over doubles behavioral performance compared to the unmodified CLIP baseline. By embedding human inductive biases and mirroring dynamic neural processes during training, personalized neural fine tuning improves predictions of human similarity judgments and tracks the temporal evolution of individual neural responses. Our work establishes a novel, interpretable framework for designing adaptive AI systems, with broad implications for neuroscience, personalized medicine, and human-computer interaction.