Abstract: Title: Do grid codes afford generalization and flexible decision-making? Scientific question: Grid-like codes in the human entorhinal cortex (EC) have been proposed to take part in learning and representing structural knowledge, which enables efficient planning and model-based decision-making. However, it has not yet been established whether these grid-like codes serve to transfer learning and to generalize previous experiences for inferences in novel situations. Do grid codes provide consistent context-invariant representations of structural relationships or do they flexibly switch their reference frame according to the current task demand to plan/discover optimal decision policy? Here we aim to reveal the constraints, conditions, and potential transformations of grid codes in the human brain that would afford flexible generalization.