Adapting Hierarchical Circuits from Planning to Language with Computational Modeling

通过计算建模调整从规划到语言的分层电路

• 批准号: 10709065
• 负责人: Ellie Pavlick
• 金额: $ 37.34万
• 依托单位: BROWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10709065
• 关键词: Affect; Alzheimer' s Disease; Architecture; Artificial Intelligence; Behavioral; Brain; Cognitive; Computer Models; Data; Diagnosis; Goals; Human; Impairment; Language; Learning; Linguistics; Mental disorders; Modeling; Motor; Natural Language Processing; Nervous System Physiology; Neuraxis; Psychiatric therapeutic procedure; Research; Sampling; Semantics; Short-Term Memory; Structure; Testing; Training; Work; base; cognitive ability; cognitive neuroscience; experimental study; improved; insight; language processing; neuromechanism; programs; relating to nervous system; syntax; transfer learning

The goal of this proposal is to use computational modeling in order to test the hypothesis that there exists a shared neural mechanism within the PFC-BG region which underlies both abstract planning in the physical domain (i.e., motor action selection) and abstract language processing. Understanding the neural basis for human language is one of the holy grails of cognitive neuroscience. Recently, powerful models from artificial intelligence, specifically natural language processing (NLP), have demonstrated impressive ability to capture many aspects of linguistic structure, but it is not yet understood how (if at all) these models are comparable to processing in the human brain. This project will use modeling insights from recent work in NLP (specifically, Transformer architectures and transfer learning) in order to develop a new computational model of the PFC-BG region. Following the pilot, the long term goal of this research program is to use the insights generated by these computational models to improve diagnosis and treatment of psychiatric illness in which both abstract planning and language abilities are impaired–e.g., OCD (in which impairments to planning affect discourse coherence) and Alzheimer’s (in which impairments affect interaction between syntax and semantics). The work is organized into three specific aims. Aim 1 will evaluate whether Transformer-based models can account for human behavioral data on cognitive working memory tasks in the motor planning domain. Aim 2 will evaluate whether the same model architectures can support learning of linguistic structure using abstract grammars. Aim 3 will evaluate whether computational mechanisms can be transfered from the motor planning task from Aim 1 to the language task from Aim 2. Here, “transfer” will be quantified by the increase in sample efficiency that results from building the language processing model on top of circuitry that has previously been specialized for the motor planning task, as opposed to training from scratch. Taken together, the results of the proposed experiments will reveal whether it is plausible that basic cognitive abilities (i.e., motor planning) and abstract language depend on shared PFC-BG circuitry, or rather that the tasks depend on structurally similar but physically distinct networks within the brain

本提案的目标是使用计算建模来检验存在的假设