Computational and neural mechanisms underlying the influence of action affordances on value learning

When encountering a novel situation, an intelligent agent must identify which actions are most beneficial for interacting with that environment. One purported mechanism for narrowing down the scope of possible actions is action affordance. Here, we examine the neuro-computational mechanisms accounting for how action affordance shapes value-based learning in a novel environment by combining a novel behavioral task with computational modeling of human behavioral and fMRI data. Our findings indicate that action affordance operates as an independent system that concurrently guides action-selection alongside value-based decision-making rather than simply exerting an initial or persistent bias on value-driven choices. These two systems engage in a competitive process to determine final action selection, governed by a dynamic meta controller. We find that the pre-supplementary motor area and anterior cingulate cortex play a central role in exerting meta-control over the two systems. Meanwhile, the posterior parietal cortex integrates the predictions from these two controllers to determine which action to select. As a result, the action-selection process dynamically takes into account both the expected value and the appropriateness of particular actions for a given scenario.