Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, Ontario, Canada.
Hippocampus. 2010 Aug;20(8):894-901. doi: 10.1002/hipo.20735.
Human problem solving relies on multiple strategies supported by dynamic neural network interactions. The transitive inference (TI) problem solving task can be accomplished by the extraction of relations among stimuli or by responding to reinforcement histories of items using associative learning. Relational and associative strategies are assumed to rely on the hippocampus and caudate nucleus, respectively; which compete to control behavior. However, we found that increased recruitment of both systems in TI is correlated with greater accuracy and awareness, and reduced associative responding to single items. Contrary to prior assumptions, the hippocampus and caudate interact cooperatively to facilitate successful TI. We suggest that the dynamics of the relationship between the hippocampus and caudate depends critically upon task demands.
人类解决问题依赖于多种策略，这些策略由动态神经网络相互作用支持。传递推理 (TI) 问题解决任务可以通过提取刺激之间的关系或通过使用联想学习来响应项目的强化历史来完成。关系和联想策略分别被认为依赖于海马体和尾状核；它们相互竞争以控制行为。然而，我们发现 TI 中两个系统的募集增加与更高的准确性和意识相关，并且对单个项目的联想反应减少。与先前的假设相反，海马体和尾状核相互协作以促进 TI 的成功。我们认为，海马体和尾状核之间关系的动态取决于任务需求。
