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.