Center for Learning and Experimental Psychopathology, University of Leuven, Tiensestraat 102, Leuven, 3000, Belgium.
Nihon University, Tokyo, Japan.
Behav Res Methods. 2017 Jun;49(3):835-852. doi: 10.3758/s13428-016-0753-x.
In the present study, we explored the linguistic nature of specific memories generated with the Autobiographical Memory Test (AMT) by developing a computerized classifier that distinguishes between specific and nonspecific memories. The AMT is regarded as one of the most important assessment tools to study memory dysfunctions (e.g., difficulty recalling the specific details of memories) in psychopathology. In Study 1, we utilized the Japanese corpus data of 12,400 cue-recalled memories tagged with observer-rated specificity. We extracted linguistic features of particular relevance to memory specificity, such as past tense, negation, and adverbial words and phrases pertaining to time and location. On the basis of these features, a support vector machine (SVM) was trained to classify the memories into specific and nonspecific categories, which achieved an area under the curve (AUC) of .92 in a performance test. In Study 2, the trained SVM was tested in terms of its robustness in classifying novel memories (n = 8,478) that were retrieved in response to cue words that were different from those used in Study 1. The SVM showed an AUC of .89 in classifying the new memories. In Study 3, we extended the binary SVM to a five-class classification of the AMT, which achieved 64%-65% classification accuracy, against the chance level (20%) in the performance tests. Our data suggest that memory specificity can be identified with a relatively small number of words, capturing the universal linguistic features of memory specificity across memories in diverse contents.
在本研究中，我们通过开发一种能够区分特定记忆和非特定记忆的计算机分类器，探索了自传体记忆测试（AMT）中产生的特定记忆的语言性质。AMT 被认为是研究精神病理学中记忆功能障碍（例如，难以回忆记忆的具体细节）的最重要的评估工具之一。在研究 1 中，我们利用了日本语料库中 12400 个带有观察者评定特异性的线索回忆记忆的数据。我们提取了与记忆特异性特别相关的语言特征，例如过去时态、否定词以及与时间和地点相关的副词短语。基于这些特征，我们训练了一个支持向量机（SVM）来将记忆分为特定和非特定类别，在性能测试中，该 SVM 的曲线下面积（AUC）达到了.92。在研究 2 中，我们测试了经过训练的 SVM 在分类新记忆（n=8478）方面的稳健性，这些新记忆是在与研究 1 中使用的不同线索词的刺激下检索到的。SVM 在分类新记忆方面的 AUC 为.89。在研究 3 中，我们将二进制 SVM 扩展为 AMT 的五分类，在性能测试中，其分类准确率达到了 64%-65%，高于机会水平（20%）。我们的数据表明，记忆特异性可以通过相对较少的词语来识别，这些词语捕捉了不同内容的记忆中记忆特异性的普遍语言特征。
