Extracting the regularities of our environment is one of our core cognitive abilities. To study the fine-grained dynamics of the extraction of embedded regularities, a method combining the advantages of the artificial language paradigm (Saffran, Aslin, & Newport, 1996) and the serial response time task (Nissen & Bullemer, 1987) was used with a group of Guinea baboons (Papio papio) in a new automatic experimental device (Fagot & Bonte, 2010). After a series of random trials, monkeys were exposed to language-like patterns. We found that the extraction of embedded patterns positioned at the end of larger patterns was faster than the extraction of initial embedded patterns. This result suggests that there is a learning advantage for the final element of a sequence that benefits from the contextual information provided by previous elements.