Abstract
When encountering a novel situation, an intelligent agent needs to find out which actions are most beneficial for interacting with that environment. However, the range of possible actions that could be selected is virtually unlimited, making the problem of determining which subset of actions should be drawn from to begin exploration extremely challenging. 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 decision-making and action-selection 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 posterior parietal cortex plays a central role in integrating 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.
Competing Interest Statement
The authors have declared no competing interest.
