Solving the credit assignment problem: explicit and implicit learning of action sequences with probabilistic outcomes

In most problem-solving activities, feedback is received at the end of an action sequence. This creates a credit-assignment problem where the learner must associate the feedback with earlier actions, and the interdependencies of actions require the learner to remember past choices of actions. In two studies, we investigated the nature of explicit and implicit learning processes in the credit-assignment problem using a probabilistic sequential choice task with and without a secondary memory task. We found that when explicit learning was dominant, learning was faster to select the better option in their first choices than in the last choices. When implicit reinforcement learning was dominant, learning was faster to select the better option in their last choices than in their first choices. Consistent with the probability-learning and sequence-learning literature, the results show that credit assignment involves two processes: an explicit memory encoding process that requires memory rehearsals and an implicit reinforcement-learning process that propagates credits backwards to previous choices.