标题： 脑到模型迁移：使用人类神经活动作为教师来训练感觉和决策模型 显示英文标题

标题： Brain2Model Transfer: Training sensory and decision models with human neural activity as a teacher

摘要： 迁移学习通过利用大型预训练教师模型中的丰富特征表示，增强了新感官和决策模型的训练。 认知神经科学表明，人脑会创建低维、抽象的表示，以实现高效的感官运动编码。 重要的是，大脑可以在比人工模型所需更少的数据点和计算能力的情况下学习这些表示。 我们引入了脑到模型迁移学习（B2M），这是一种框架，其中人类感官和决策任务中的神经活动作为训练人工神经网络的教师模型。 我们提出了两种B2M策略：（1）脑对比迁移，通过对比目标对齐脑活动和网络激活；以及（2）脑潜在迁移，通过基于脑源特征的监督回归，将相似认知任务中的潜在动态投影到学生网络上。 我们在基于记忆的决策中验证了B2M，并使用循环神经网络进行场景重建，用于自动驾驶。 结果表明，受益于基于脑的迁移的学生网络收敛更快，并且比单独训练的网络具有更高的预测准确性。 我们的研究结果表明，大脑的表示对人工学习者是有价值的，为更高效地学习复杂决策表示铺平了道路，这通过纯粹的人工训练将是昂贵或缓慢的。 显示英文摘要

摘要： Transfer learning enhances the training of novel sensory and decision models by employing rich feature representations from large, pre-trained teacher models. Cognitive neuroscience shows that the human brain creates low-dimensional, abstract representations for efficient sensorimotor coding. Importantly, the brain can learn these representations with significantly fewer data points and less computational power than artificial models require. We introduce Brain2Model Transfer Learning (B2M), a framework where neural activity from human sensory and decision-making tasks acts as the teacher model for training artificial neural networks. We propose two B2M strategies: (1) Brain Contrastive Transfer, which aligns brain activity and network activations through a contrastive objective; and (2) Brain Latent Transfer, which projects latent dynamics from similar cognitive tasks onto student networks via supervised regression of brain-derived features. We validate B2M in memory-based decision-making with a recurrent neural network and scene reconstruction for autonomous driving with a variational autoencoder. The results show that student networks benefiting from brain-based transfer converge faster and achieve higher predictive accuracy than networks trained in isolation. Our findings indicate that the brain's representations are valuable for artificial learners, paving the way for more efficient learning of complex decision-making representations, which would be costly or slow through purely artificial training.