To explore the influence of chunking on the capacity limits of working memory, a model for chunking in sequential working memory is proposed, using hierarchical bidirectional inhibition-connected neural networks with winnerless competition. With the assumption of the existence of an upper bound to the inhibitory weights in neurobiological networks, it is shown that chunking increases the number of memorized items in working memory from the "magical number 7" to 16 items. The optimal number of chunks and the number of the memorized items in each chunk are the "magical number 4".