联想情绪学习使生物体能够适应性地将愉快或不愉快的结果与预测刺激的存在联系起来。尽管 Rescorla-Wagner 模型等计算模型已经阐明了这一重要功能,，但这些模型的局限性也是众所周知的,，尤其是当它们应用于神经数据时。深度神经网络的出现为关联情感学习建模开辟了另一条途径。在这项工作中，我们提出了一种视觉效价处理,的深度神经网络模型，由一个编码复杂自然场景的视觉模块和一个根据效价,（情感,的关键维度）识别其情感意义的模块组成，并在该模型上测试了一种新颖的巴甫洛夫学习范式。结果表明，通过学习,，模型再现了人类联想学习研究,的几个观察结果，包括联想形成和泛化,，并且条件刺激和非条件刺激的神经表征在单个单元和神经群体水平上变得越来越一致。模型与人体实验数据之间的比较进一步验证了我们的方法。因此，这项研究表明，深度神经网络模型, 与适当的学习算法, 相结合时，可用于对联想情绪/ 效价学习的行为和神经特征进行建模。
Associative emotional learning enables organisms to adaptively link pleasant or unpleasant outcomes to the presence of predictive stimuli. Whereas computational models such as the Rescorla-Wagner model have shed light on this important function, the limitations of these models are also known, especially when they are applied to neural data. The advent of deep neural networks has opened another avenue for modeling associative emotional learning. In this work we proposed a deep neural network model of visual valence processing, consisting of a visual module that encodes complex natural scenes and a module that recognizes their emotional significance in terms of valence, a key dimension of emotion, and tested a novel Pavlovian learning paradigm on the model. The results showed that with learning, the model reproduced several observations from human associative learning studies, including association formation and generalization, and that the neural representations of the conditioned and the unconditioned stimuli became increasingly aligned both at the single unit and at the neural population level. Comparison between the model and human experimental data provided further validation of our approach. This study thus suggests that deep neural network models, when combined with appropriate learning algorithms, can be used to model behavioral and neural signatures of associative emotion/valence learning.
科目:人工智能(cs.AI)
Subjects: Artificial Intelligence (cs.AI)