Attraction and repulsion in perception and working memory as complementary outcomes of learning

Visual perception is imperfect. Fortunately, the environment contains an incredible amount of structure that observers can learn to exploit through experience. As a result, representations of the environment that are perceived and stored in memory are often biased towards or away from other stimuli, reference points, and expectations that are acquired across varying timescales. We propose that this myriad of bias phenomena reflects a shared adaptive principle where the brain optimizes its noisy sensory representations to support behavior within its resource constraints. Under this principle, experience is stabilized through the integration of mutually informative stimuli, whilst the discriminability between stimuli is maintained through adaptation. We suggest that this principle emerges naturally from predictive and adaptive coding frameworks that can operate across multiple levels of processing and predict attraction and repulsion even within the same task or stimulus. Viewing these biases as emergent properties of a hierarchical statistical learning system offers new insight into how the brain balances stability and flexibility when shaping our perception and memory of the world.