Rohling M L, Ellis N R, Scogin F
University of Alabama.
J Gerontol. 1991 Jul;46(4):P137-43. doi: 10.1093/geronj/46.4.p137.
This study investigated the automatic and effortful memory encoding model of Hasher and Zacks (1979) and the potential it may hold for aiding in the differentiation between aging-related memory decline and dementia. College students, normal elders, and dementia patients were compared on a 96-item picturebook task utilizing measures of free recall, recognition accuracy, memory for location, and memory for frequency. There were no differences between students and elders on any of the dependent measures. However, differences were found between elders and patients on each measure, and a discriminant function correctly classified the two groups with 93.3% accuracy. Subsequent discriminant analysis found patients could be correctly classified into diagnostic subgroups, i.e., dementia of the Alzheimer's type (DAT), multi-infarct dementia (MID), and Korsakoff's disease (KD) with 80.8% accuracy. The model holds promise as a guide for clinicians who are asked to make differential diagnoses of memory-impaired individuals.
本研究调查了哈舍和扎克斯（1979年）的自动和费力记忆编码模型，以及该模型在帮助区分与衰老相关的记忆衰退和痴呆症方面的潜在作用。在一项96项的图画书任务中，对大学生、正常老年人和痴呆症患者进行了比较，该任务采用了自由回忆、识别准确性、位置记忆和频率记忆等测量方法。在任何一项相关测量中，学生和老年人之间均无差异。然而，在各项测量中，老年人和患者之间存在差异，且判别函数以93.3%的准确率正确区分了这两组。随后的判别分析发现，患者能够以80.8%的准确率被正确分类到诊断亚组中，即阿尔茨海默病型痴呆（DAT）、多发梗死性痴呆（MID）和柯萨科夫综合征（KD）。该模型有望为被要求对记忆受损个体进行鉴别诊断的临床医生提供指导。
