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

When encountering a novel situation, an intelligent agent needs to find out which actions are most beneficial for interacting with that environment. One purported mechanism for narrowing down the scope of possible actions is the concept of action affordance. Here, we delve into the neuro-computational mechanisms accounting for how action affordance shapes value-based learning in a novel environment by utilizing a novel task alongside computational modeling of behavioral and fMRI data collected in humans. Our findings indicate that rather than simply exerting an initial or persistent bias on value-driven choices, action affordance is better conceived of as an independent system that concurrently guides action-selection alongside value-based decision-making. 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 plays a central role in exerting meta-control over the two systems while the posterior parietal cortex integrates the predictions from these two controllers of what action to select, so that the action-selection process dynamically takes into account both the expected value and appropriateness of particular actions for a given scenario.