Many-Two-One: Diverse Representations Across Visual Pathways Emerge from A Single Objective

How the human brain supports diverse behaviours has been debated for decades. The canonical view divides visual processing into distinct "what" and "where/how" streams – however, their origin and independence remain contested. Here, using deep neural network models that accurately predict hours of brain recordings, we computationally characterise how cortex processes dynamic vision. Despite the diversity of cortical regions and thereby supported tasks, we identify two fundamental computations that explain neural activity across visual cortex: object and appearance-free motion recognition. Strikingly, a single objective underlies both: these inherent computations in the brain emerge from optimising for understanding world dynamics, and their arrangement is highly distributed and smooth across cortex rather than strictly aligning with the two visual streams. Our results suggest that the human brain’s ability to integrate complex information across seemingly distinct representational pathways may originate from the single goal of modelling the world.