Associative Emotional Learning in Convolutional Neural Networks
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 e...
| Authors | Seowung Leem, Andreas Keil, Mingzhou Ding et al. |
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| Primary category | cs.AI |
| Published | 21 July 2026 |
| Last updated at source | 22 July 2026 |
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