Learning is implicit when an individual acquires new information without intending to do so. The distinction between implicit and explicit knowledge may hinge on whether a person is conscious of the regularity with a conscious rather than unconscious mental state. Computational modeling has played a central role in deconstructing early verbal theories of the nature of what is learned in implicit learning paradigms. On the theoretical and conceptual applications of implicit learning, this chapter addresses three central issues: whether performance in implicit learning situations result in abstract knowledge; whether the data and the modeling suggest the involvement of single or multiple systems; and whether modeling is relevant to addressing the conscious versus unconscious nature of the acquired knowledge. Implicit learning has proven to be a rich domain for exploration of the differences between information processing with and without consciousness.