Predictive coding networks capture human neural representations missing in supervised DNNs

Neuroscientific learning theories propose that the brain acquires knowledge by constructing internal world models. Supervised learning, the dominant approach in deep neural networks (DNN), relies on external category labels, making it difficult to reconcile with biological learning. There is an increasing trend towards more biologically valid approaches, such as predictive (minimize future surprise) or contrastive (minimize response to expected, maximize to unexpected inputs) objectives, but these approaches typically rely on pretrained DNN models that vary widely in architecture, size, and hyperparameters, making direct comparisons difficult. Here, we isolate the effect of learning by comparing small, identical networks trained with predictive, contrastive, and supervised learning objectives, as well as local (layer-restricted) vs. global (full backpropagation) learning. We show that brain representations after statistical learning are better modeled by a predictive local target than a supervised or contrastive target, and that during learning, the brain attenuates category-specific representations while retaining predictive ones. We additionally show that predictive objectives explain brain variance that standard supervised DNNs do not, and that this variance is tied to predictive rather than stimulus or mismatch processing. These findings demonstrate a controlled approach for testing the algorithmic basis of learning and identify prediction as a core learning mechanism.