Data Storage Institute, Agency for Science, Technology and Research, A*STAR, Singapore.
Int J Neural Syst. 2013 Aug;23(4):1350019. doi: 10.1142/S0129065713500196. Epub 2013 May 26.
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".
为了探索组块对工作记忆容量限制的影响,本文提出了一个序列工作记忆中组块的模型,使用具有无胜者竞争的分层双向抑制连接神经网络。基于神经生物学网络中抑制权重存在上限的假设,本文表明组块可以将工作记忆中可记忆项目的数量从“神奇数字 7”增加到 16 个。最佳组块数量和每个组块中可记忆项目的数量为“神奇数字 4”。