Semantic Representations in Reading

连接主义网络，通过大规模并行计算 合作和竞争的相互作用许多简单的神经元样 处理单位，提供了一个统一的框架，研究认知 过程及其神经学实现。 拟议 研究涉及对患有后天性阅读障碍的患者建模， 通过发展和“损害”一个连接模型， 正常的阅读。 基于心理学和神经心理学的数据， 该模型有两条路线，用于推导印刷体的发音， 词：从正字法直接映射到 音系学通过“读出”这个词，和一个语义路线，映射 通过“识别”单词的含义来间接地通过语义学。 中的每一条路由的初步连接主义实现， 隔离成功地复制了有限范围的正常和 异常的阅读行为。 建议制定一个完整的 双路由模型能够解决更广泛的 的协调和相对贡献 阅读采集过程中的每一条路径，成熟熟练的阅读， 部分损伤后的行为和损伤后的恢复。 模型 将在一个更合理的神经生理学 连接主义的形式主义比以前的工作中使用的。 在 此外，一项主要的研究工作将集中在开发一种 足够复杂的语义系统，克服了严重的 现有系统的局限性。 高性能计算 设施将用于满足广泛的计算需求 训练和测试模型。

Connectionist networks, which compute via the massively parallel cooperative and competitive interactions many simple neuron-like processing units, provide a unified framework for studying cognitive processes and their neurological implementation. The proposed research involve modeling acquired reading disorders in patients with brain damage by developing and "lesioning" a connectionist model of normal reading. Based on data from psychology and neuropsychology, the model has two routes for deriving the pronunciation of a printed word: a phonological route that maps directly from orthography to phonology by "sounding-out" the word, and a semantic route that maps indirectly via semantics by "recognizing" the meaning of the word. Preliminary connectionist implementations of each of these routes in isolation have succeeded in reproducing a limited range of normal and abnormal reading behavior. It is proposed to develop a complete dual-route model capable of addressing a much wider range of phenomena, relating to the coordination and relative contribution of each route during reading acquisition, mature skilled reading, behavior after partial damage, and recovery from damage. The model will be developed within a more neurophysiologically plausible connectionist formalism than the one used in the previous work. In addition, a major research effort will focus on developing an adequately sophisticated semantic system that overcomes the severe limitations of existing systems. High-performance computing facilities will be used to meet the extensive computational demands of training and testing the model.