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首页> 外文期刊>Cognitive Neurodynamics >An integrated neural model of semantic memory, lexical retrieval and category formation, based on a distributed feature representation
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An integrated neural model of semantic memory, lexical retrieval and category formation, based on a distributed feature representation

机译:基于分布式特征表示的语义记忆,词汇检索和类别形成的集成神经模型

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摘要

This work presents a connectionist model of the semantic-lexical system. Model assumes that the lexical and semantic aspects of language are memorized in two distinct stores, and are then linked together on the basis of previous experience, using physiological learning mechanisms. Particular characteristics of the model are: (1) the semantic aspects of an object are described by a collection of features, whose number may vary between objects. (2) Individual features are topologically organized to implement a similarity principle. (3) Gamma-band synchronization is used to segment different objects simultaneously. (4) The model is able to simulate the formation of categories, assuming that objects belong to the same category if they share some features. (5) Homosynaptic potentiation and homosynaptic depression are used within the semantic network, to create an asymmetric pattern of synapses; this allows a different role to be assigned to shared and distinctive features during object reconstruction. (6) Features which frequently occurred together, and the corresponding word-forms, become linked via reciprocal excitatory synapses. (7) Features in the semantic network tend to inhibit words not associated with them during the previous learning phase. Simulations show that, after learning, presentation of a cue can evoke the overall object and the corresponding word in the lexical area. Word presentation, in turn, activates the corresponding features in the sensory-motor areas, recreating the same conditions occurred during learning, according to a grounded cognition viewpoint. Several words and their conceptual description can coexist in the lexical-semantic system exploiting gamma-band time division. Schematic exempla are shown, to illustrate the possibility to distinguish between words representing a category, and words representing individual members and to evaluate the role of gamma-band synchronization in priming. Finally, the model is used to simulate patients with focalized lesions, assuming a damage of synaptic strength in specific feature areas. Results are critically discussed in view of future model extensions and application to real objects. The model represents an original effort to incorporate many basic ideas, found in recent conceptual theories, within a single quantitative scaffold.
机译:这项工作提出了语义-词汇系统的连接主义模型。该模型假定语言的词汇和语义方面存储在两个不同的存储中,然后使用生理学习机制,根据先前的经验将它们链接在一起。该模型的特殊特征是:(1)对象的语义方面由特征的集合描述,特征的数量在对象之间可能有所不同。 (2)对单个特征进行拓扑组织以实现相似性原则。 (3)伽玛波段同步用于同时分割不同的对象。 (4)该模型能够模拟类别的形成,前提是如果对象共享某些特征,则它们属于同一类别。 (5)在语义网络中使用同态突触增强和同态突触抑制,以创建不对称的突触模式;这样可以在对象重建过程中为共享的独特功能分配不同的角色。 (6)经常同时出现的特征和相应的词形通过相互的兴奋性突触联系在一起。 (7)语义网络中的特征倾向于抑制在先前的学习阶段中与其无关的单词。模拟表明,学习后,提示的呈现可以唤起整个对象和词汇区域中的相应单词。根据扎根的认知观点,单词表达反过来会激活感觉运动区域中的相应功能,从而重新创建学习过程中发生的相同条件。几个单词及其概念描述可以在利用gamma波段时分的词汇语义系统中共存。显示了示意图示例,以说明区分表示类别的单词和表示单个成员的单词以及评估γ波段同步在启动中的作用的可能性。最后,该模型用于模拟具有局灶性病变的患者,假设特定特征区域的突触强度受损。鉴于将来的模型扩展和将其应用于实际对象,对结果进行了严格的讨论。该模型代表了将最新概念理论中发现的许多基本思想整合到单个定量支架中的原始尝试。

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