Robots are expected to perform actions in a human environment where they will have to learn both how and when to act. Social human robot interaction could provide the robot with external feedback to guide them. In this paper, the focus is put on managing correctly negative signals thus stressing the importance of being aware of its own goal. In previous works, we developed bio-inspired models for action planning which enabled a robot to adapt its space representations and thus its behavior in the context of latent learning with rewards. Though, as the action selection is based on a local readout of a propagated gradient, the current goal is not explicitly available. To determine it, the implemented mechanisms are : first, to select and inhibit one of the potential goals and then, to monitor if this inhibition changes the current behavior of the agent. If so, the inhibited goal is the one pursued. As a result, negative signals can then be used to directly modulate the strength of the current goal and change the agent's behavior.