Associative Emotional Learning in Convolutional Neural Networks
2026-07-21 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors created a deep neural network to understand how humans learn to associate emotions like good or bad feelings with things they see. Their model looks at pictures and learns which ones are linked to positive or negative feelings, similar to how people form emotional associations. Over time, the model's internal patterns for the conditioned and unconditioned stimuli become more similar, mirroring findings from human studies. By comparing their model with real human data, the authors showed that deep learning can help explain how emotional learning happens in the brain.
Associative learningEmotional valenceDeep neural networksRescorla-Wagner modelPavlovian conditioningVisual processingNeural representationGeneralizationConditioned stimulusUnconditioned stimulus
Authors
Seowung Leem, Andreas Keil, Mingzhou Ding, Ruogu Fang
Abstract
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.