AI 中文总结
研究卷积神经网络中的联想情感学习，提出含视觉模块和效价识别模块的模型，测试新学习范式。结果显示模型重现人类联想学习观察结果，神经表征趋同，与人类实验数据比较进一步验证，表明可用于模拟相关行为和神经特征。
AI 中文摘要
联想情感学习使生物体能够将愉快或不愉快的结果与预测性刺激的存在进行适应性关联。尽管像Rescorla-Wagner模型这样的计算模型揭示了这一重要功能，但它们也存在局限性，尤其是应用于神经数据时。深度神经网络的出现为联想情感学习建模开辟了新途径。本文提出了一种视觉效价处理的深度神经网络模型，包括编码复杂自然场景的视觉模块和根据效价识别其情感意义的模块，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，该模型重现了人类联想学习研究中的一些观察结果，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越来越一致。模型与人类实验数据的比较进一步验证了我们的方法。因此，本研究表明，深度神经网络模型与适当的学习算法相结合，可用于模拟联想情感/效价学习的行为和神经特征。
英文摘要
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.
CommentsThe article has been accepted for publication in Neural Computation
