Rescorla-Wagner model

CNN model of associative emotional learning reproduces human Pavlovian conditioning signatures

Researchers propose a convolutional neural network model of visual valence processing, combining a scene-encoding visual module with an emotional significance module, and test it on a novel Pavlovian learning paradigm. The model reproduces key observations from human associative learning studies, including association formation, generalization, and alignment of neural representations for conditioned and unconditioned stimuli. The work positions deep neural networks as viable computational models of affective learning, offering advantages over classical models like Rescorla-Wagner when applied to neural data.