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 psycho-physics has uncovered systematic biases such as contraction (stored information shifts towards 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 neural network. We test the model in four different datasets, two sensory modalities and three experimental paradigms: two working memory tasks, a reference memory task, and a novel "one-back task" that we designed 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.