Value Representations Shape Learning Under Changing Goals

On March 9, 2016, at the Four Seasons Hotel in Seoul, AlphaGo defeated world champion Lee Sedol 4-1, marking a milestone in machine intelligence. But unlike humans, AlphaGo pursued a single fixed goal. Humans continually revise their goals and preferences, raising the question of how learned value representations support such flexibility. Previous work on goal-dependent learning has focused on goal selection or maintenance, largely neglecting how the structure of value representations affects learning when goals change. Here, using two multi-goal learning paradigms combined with computational modelling, we compare two learning architectures: reweighting existing values versus relearning them. We show that participants using feature-based representations adjusted more quickly and monitored decisions more effectively by reusing learned feature values. In contrast, participants relying on a single composite value were forced to relearn after each switch. These findings show that value representation structure shapes learning efficiency and behavioural flexibility in multi-goal environments.