标题： 将注意力转向您：个性化的脑启发人工智能模型 显示英文标题

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

摘要： 将人类智能与人工智能相结合代表了一个科学机遇，以推进我们对信息处理的理解，因为每个系统都提供了独特的计算见解，可以增强和告知对方。 将人类认知原理与人工智能结合有可能产生更可解释且功能对齐的计算模型，同时通过系统的模型比较和表征分析提供一个正式框架来研究感知、学习和决策背后的神经机制。 在本研究中，我们引入了个性化的脑启发建模，该模型整合了人类行为嵌入和神经数据，以与认知过程对齐。 我们采取了分步的方法，在一个我们称之为CLIP-基于人类分析（CLIP-HBA）的更大框架内，对对比语言-图像预训练（CLIP）模型进行微调，使用大规模行为决策、群体水平的神经数据，最后是参与者水平的神经数据。 我们发现，在行为数据上进行微调增强了其预测人类相似性判断的能力，同时间接地使其与通过MEG捕获的动态表示对齐。 为了进一步获得对认知过程时间演变的机制性见解，我们引入了一个专门在毫秒级MEG神经动力学上微调的模型（CLIP-HBA-MEG）。 该模型在与人类神经处理的时间对齐方面得到了增强，同时在行为对齐方面仍显示出改进。 最后，我们在参与者特定的神经数据上训练了个性化模型，有效地捕捉了个性化的神经动力学，并突出了个性化AI系统的潜力。 这些个性化系统对医学、认知研究、人机接口和AI发展领域具有深远的影响。 显示英文摘要

摘要： The integration of human and artificial intelligence represents a scientific opportunity to advance our understanding of information processing, as each system offers unique computational insights that can enhance and inform the other. The synthesis of human cognitive principles with artificial intelligence has the potential to produce more interpretable and functionally aligned computational models, while simultaneously providing a formal framework for investigating the neural mechanisms underlying perception, learning, and decision-making through systematic model comparisons and representational analyses. In this study, we introduce personalized brain-inspired modeling that integrates human behavioral embeddings and neural data to align with cognitive processes. We took a stepwise approach, fine-tuning the Contrastive Language-Image Pre-training (CLIP) model with large-scale behavioral decisions, group-level neural data, and finally, participant-level neural data within a broader framework that we have named CLIP-Human-Based Analysis (CLIP-HBA). We found that fine-tuning on behavioral data enhances its ability to predict human similarity judgments while indirectly aligning it with dynamic representations captured via MEG. To further gain mechanistic insights into the temporal evolution of cognitive processes, we introduced a model specifically fine-tuned on millisecond-level MEG neural dynamics (CLIP-HBA-MEG). This model resulted in enhanced temporal alignment with human neural processing while still showing improvement on behavioral alignment. Finally, we trained individualized models on participant-specific neural data, effectively capturing individualized neural dynamics and highlighting the potential for personalized AI systems. These personalized systems have far-reaching implications for the fields of medicine, cognitive research, human-computer interfaces, and AI development.