Humans live in a sea of competing and complementary motivations. One reason that our behaviour is so complex and interesting stems from the hundreds of separate desires and goals that influence our actions. But we do not come into this world with these complex and often subtle motivators. Instead, humans learn to achieve or maintain intermediate goals that tend to facilitate the achievement of our base-level drives. We amass wealth, for example, because it facilitates the satiation of other drives such as hunger, security, or even procreation. Even though it can often be the case that a person will focus on the intermediate goal rather than the true priorities, these sub-goals are usually beneficial. Neural schema mechanism is a new autonomous agent control structure that makes use of both neural network and symbolic constructs to learn sensory motor correlations and abstract concepts through its own experience. The mechanism can also learn which intermediate states or goals should be achieved or avoided based on its primitive drives. In addition, a psychological theory of consciousness is modeled that allows the system to come up with creative action sequences to achieve goals even under situations of incomplete knowledge. The result is an architecture for robust action selection that learns not only how to achieve primitive drives, but also learns appropriate sub-goals that are in service of those drives. It does this in a way that is cognitively plausible and provides clear benefits to the performance of the system.