Norman Kenneth A, O'Reilly Randall C
University of Colorado at Boulder, Department of Psychology, Boulder, CO, USA.
Psychol Rev. 2003 Oct;110(4):611-46. doi: 10.1037/0033-295X.110.4.611.
The authors present a computational neural-network model of how the hippocampus and medial temporal lobe cortex (MTLC) contribute to recognition memory. The hippocampal component contributes by recalling studied details. The MTLC component cannot support recall, but one can extract a scalar familiarity signal from MTLC that tracks how well a test item matches studied items. The authors present simulations that establish key differences in the operating characteristics of the hippocampal-recall and MTLC-familiarity signals and identify several manipulations (e.g., target-lure similarity, interference) that differentially affect the 2 signals. They also use the model to address the stochastic relationship between recall and familiarity and the effects of partial versus complete hippocampal lesions on recognition.
作者提出了一个关于海马体和内侧颞叶皮质（MTLC）如何对识别记忆做出贡献的计算神经网络模型。海马体部分通过回忆所学细节来发挥作用。MTLC部分无法支持回忆，但可以从MTLC中提取一个标量熟悉度信号，该信号可追踪测试项目与所学项目的匹配程度。作者进行了模拟，确定了海马体回忆信号和MTLC熟悉度信号在操作特性上的关键差异，并识别了几种不同程度影响这两种信号的操作（例如，目标-诱饵相似性、干扰）。他们还使用该模型来探讨回忆与熟悉度之间的随机关系，以及部分或完全海马体损伤对识别的影响。
