Diverse perceptual biases emerge from Hebbian plasticity in a recurrent neural network model

Perceptual biases offer a glimpse into how the brain processes sensory stimuli. While psychophysics has uncovered systematic biases such as contraction and repulsion, a unifying neural network model for how such seemingly distinct biases emerge from learning is lacking. Here, we show that both contractive and repulsive biases emerge from continuous Hebbian plasticity in a single recurrent neural network. We test our model in three experimental paradigms: a working memory task, a reference memory task, and a novel “one-back task” that we design to test the robustness of the model. We find excellent agreement between model predictions and experimental data without fine-tuning the model to any particular paradigm. These results show that apparently contradictory perceptual biases can in fact emerge from a simple local learning rule in a single recurrent region of the brain.