臨床上失智症的主要特徵之一是記憶功能的衰退，通常以包含回憶和再認的記憶測驗檢驗之，然而這種測驗對提供失智成因的資訊相當有限，因為此類測驗表現通常不是單一認知歷程運作結果，而是經由一些認知歷程（如：記憶儲存與提取）共同運作所致。本研究引介一記憶歷程的「多項歷程樹狀模式」於台灣臨床心理學界。該模式假設記憶歷程由「未儲存」、「中介儲存」和「長期儲存」三個狀態組成；狀態間的轉移服膺一馬可夫學習歷程，其相對應的記憶儲存與提取機率皆以參數表示。我們藉分析收集自台大醫院的臨床資料示範此模式運用；臨床資料包括阿茲海默症、早發性失智、老年失智、輕度認知障礙、血管型失智症、和器質性腦徵候群。為處理臨床常有的組內異質性問題，我們並採用一以「潛在類別」為本的分析方法於此模式。我們用HMMTree軟體做模型適配，結果顯示模型適配指標大致良好，不同成因的失智症具有不同的參數組型。顯示「多項歷程樹狀模式」模式比起傳統分析，更能評估儲存與提取在記憶測量表現中所扮演角色，對於臨床衡鑑和失智成因的瞭解更有幫助。研究侷限，包括樣本代表性及軟體限制，也在本文一併討論。

The main symptom of dementia is the degradation of memory. In clinical settings the common approach to assess the functioning of memory is to take memory tests based on recall and recognition. However, such tests hardly reveal the underlying causes of memory degradation, because scores of these tests typically are not results of a single cognitive process but are influenced by several cognitive processes such as storage and retrieval. In this paper we introduce a cognitive modeling approach called ”multinomial processing tree models” to the clinical psychology community in Taiwan. This model assumes that the memory process consists of unstored, intermediate, and long-term states. The transition among the three states follows a discrete-state Markovian learning process, with the respective probabilities of storage and retrieval being characterized by some parameters. We illustrate the use of the model to clinical data collected from National Taiwan University Hospital, including Alzheimer's disease, early-onset dementia, senile dementia, mild cognitive impairment, vascular dementia, and organic brain syndrome. To assess the within-group heterogeneity commonly seen in clinical settings, we also incorporate a latent class analysis into the model. Parameter estimates via goodness-of-fit using the computer program HMMTree reveal that different types of dementia undergo somewhat differentiable deficits of storage and/or retrieval processes in the immediate and long-term states. We also discuss two limitations of the current study, namely the representativeness of the sample and the restriction of the HMMTree program.