Computational theories of reinforcement learning suggest that two families of algorithm—model-based and model-free—tightly map onto the classic distinction between automatic and deliberate systems of control: Deliberate evaluative responses are thought to reflect model-based algorithms, which are accurate but computationally expensive, whereas automatic evaluative responses are thought to reflect model-free algorithms, which are error-prone but computationally cheap. This framework has animated research on psychol…