ArticleNeuron2025
Diverse perceptual biases emerge from Hebbian plasticity in a recurrent neural network model.
Article in Neuron, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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 neural network. We test the model on four datasets covering two sensory modalities in two working memory tasks, a reference memory task, and a novel "one-back task" 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 emerge from a simple local learning rule in a single recurrent region of the brain.
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