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 (stored information shifts toward a central tendency) and repulsion (the current percept shifts away from recent percepts), 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