Instituto de Biocomputación y Física de Sistemas Complejos, Universidad de Zaragoza, Zaragoza, Spain.
PLoS One. 2011;6(8):e22651. doi: 10.1371/journal.pone.0022651. Epub 2011 Aug 1.
Alzheimer's Disease irremediably alters the proficiency of word search and retrieval processes even at its early stages. Such disruption can sometimes be paradoxical in specific language tasks, for example semantic priming. Here we focus in the striking side-effect of hyperpriming in Alzheimer's Disease patients, which has been well-established in the literature for a long time. Previous studies have evidenced that modern network theory can become a powerful complementary tool to gain insight in cognitive phenomena. Here, we first show that network modeling is an appropriate approach to account for semantic priming in normal subjects. Then we turn to priming in degraded cognition: hyperpriming can be readily understood in the scope of a progressive degradation of the semantic network structure. We compare our simulation results with previous empirical observations in diseased patients finding a qualitative agreement. The network approach presented here can be used to accommodate current theories about impaired cognition, and towards a better understanding of lexical organization in healthy and diseased patients.
阿尔茨海默病在疾病早期就会不可逆转地改变单词搜索和检索过程的熟练程度。这种干扰在某些特定的语言任务中有时会出现矛盾，例如语义启动。在这里，我们关注阿尔茨海默病患者中明显的超启动副作用，这在文献中已经得到了很长时间的证实。先前的研究表明，现代网络理论可以成为深入了解认知现象的有力补充工具。在这里，我们首先表明，网络建模是一种合适的方法，可以解释正常受试者的语义启动。然后，我们转向认知能力下降的情况：超启动可以很容易地理解为语义网络结构逐渐退化的结果。我们将我们的模拟结果与之前在患病患者中的经验观察结果进行比较，发现定性一致。本文提出的网络方法可用于适应关于认知障碍的当前理论，并更好地理解健康和患病患者的词汇组织。
