认知语言学对草案的传统研究采用的是符号模型 ,这对研究静态草案是有效的 ,但忽略了草案的动态性 ,且难以解释诸如草案的来源、表征、激活规则及其推理等问题。Miikkulainen以分布式连通主义理论为依据提出的“等级特征映射系统”(HirarchicalFeatureMapSystem)有效地弥补了符号研究在解释草案问题方面的不足。该系统能从特殊草案示例中提取草案、路径和角色连接的等级分类。等级分类为每个激活的草案设定了特定的记忆位置。这样 ,不完整的草案及其空缺事件也得以自动填补。 The traditional model of cognitive linguistics uses a symbolic model, which is valid for studying static drafts, but neglects the dynamics of the drafts and makes it difficult to explain such issues as the source, characterization, activation rules and their reasoning of drafts. Miikkulainen’s “HirarchicalFeatureMap System” based on the theory of distributed connectivism effectively makes up for the shortcomings of symbolic research in explaining the draft issues. The system extracts the hierarchical classification of drafts, paths, and role joins from a special draft example. Level classification sets a specific memory location for each active draft. In this way, incomplete drafts and vacancies are also automatically filled.