Obidziński Michał
Institute of Psychology, Faculty of Christian Philosophy, Cardinal Stefan Wyszyński University, Warsaw, Poland.
Dyslexia. 2021 Feb;27(1):50-61. doi: 10.1002/dys.1655. Epub 2020 Mar 13.
The presented study applies the methods of data mining and prediction models to the subject of memory functioning in developmental dyslexia. This article sets forth the results of an analysis of the decision tree algorithm for the classification of dyslexia/non-dyslexia, based on frequency data from the modified simplified conjoint recognition experiment-a paradigm based on the fuzzy-trace theory used to investigate verbatim and gist memory. This decision tree model was created with the use of the C&RT algorithm, which makes a prediction of the classification with the use of four predictors: the numbers of different types of answers depending on the specific stimuli presented. Seventy-one high school students, 33 with developmental dyslexia, took part in a memory experiment. The model created using the decision tree algorithm has a very good overall validity. Excellent developmental dyslexia classification was accompanied by satisfactory non-dyslexia classification. The decision tree proposed predictors that are supported both theoretically and empirically. The results obtained show an important role of verbatim and gist memory functioning in developmental dyslexia and suggest that the pattern of performance observed in the memory tests can be used as a predictor of the developmental dyslexia disorder. Results encourage further usage of decision trees.
本研究将数据挖掘和预测模型方法应用于发展性阅读障碍的记忆功能主题。本文阐述了基于改良简化联合识别实验的频率数据，对用于阅读障碍/非阅读障碍分类的决策树算法的分析结果，该实验是一种基于模糊痕迹理论的范式，用于研究逐字记忆和要点记忆。此决策树模型是使用C&RT算法创建的，该算法利用四个预测变量对分类进行预测：根据所呈现的特定刺激，不同类型答案的数量。71名高中生参与了记忆实验，其中33名患有发展性阅读障碍。使用决策树算法创建的模型具有非常好的整体效度。对发展性阅读障碍的出色分类伴随着对非阅读障碍的满意分类。决策树提出的预测变量在理论和实证方面都得到了支持。所得结果表明逐字记忆和要点记忆功能在发展性阅读障碍中具有重要作用，并表明在记忆测试中观察到的表现模式可作为发展性阅读障碍疾病的预测指标。研究结果鼓励进一步使用决策树。
