Frontal cortex function derives from hierarchical predictive coding
The frontal lobes are essential for human volition and goal directed behavior, yet their function remains unclear. While various models have highlighted working memory, reinforcement learning, and cognitive control as key functions, a single framework for interpreting the range of effects observed in prefrontal cortex has yet to emerge. Here we show that a simple computational motif based on predictive coding can be stacked hierarchically to learn and perform arbitrarily complex goal directed behavior. The resulting Hierarchical Error Representation (HER) model simulates a wide array of findings from fMRI, ERP, single units, and neuropsychological studies of both lateral and medial prefrontal cortex. Additionally, the model compares favorably with current machine learning approaches, learning more rapidly and with comparable performance, while self organizing representations into efficient hierarchical groups and managing working memory storage. By reconceptualizing lateral prefrontal activity as anticipating prediction errors, the HER model provides a novel unifying account of prefrontal cortex function with broad implications both for understanding the frontal cortex and building more powerful machine learning applications.