Asynchrony, overlaps and delays in sensory-motor signals introduce ambiguity as to which stimuli, actions, and rewards are causally related. Only the repetition of reward episodes help distinguish true cause-effect relationships from coincidental occurrences. In the model proposed here, a novel plasticity rule employs short and long-term changes to evaluate hypotheses on cause-effect relationships. Transient weights represent hypotheses that are consolidated in long-term memory only when they consistently predict or cause future rewards. The main objective of the novel plasticity is to preserve existing network topologies when learning with ambiguous information flows. It also improves learning by biasing the exploration of the stimulus-response space towards actions that in the past occurred before rewards. The transition to long-term plasticity indicates under which conditions beliefs can be consolidated in long-term memory, also suggesting a solution to the plasticity-stability dilemma.