A neurocognitive framework for understanding how experience shapes object representations

用于理解经验如何塑造对象表征的神经认知框架

• 批准号: 9767863
• 负责人: Neal W Morton
• 金额: $ 6.56万
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9767863
• 关键词: Affect; Area; Association Learning; Behavior; Brain; Categories; Child; Code; Cognitive; Complex; Comprehension; Computer Analysis; Computer Simulation; Computing Methodologies; Decision Making; Detection; Development; Diagnosis; Event; Functional Magnetic Resonance Imaging; Genetic Recombination; Hippocampus (Brain); Individual; Investigation; Knowledge; Lead; Learning; Light; Maintenance; Measures; Medial; Memory; Modeling; Neurocognitive; Object Attachment; Patients; Pattern; Perception; Perceptual learning; Performance; Prefrontal Cortex; Process; Reaction Time; Research; Resolution; Rodent; Semantics; Series; Shapes; Signal Transduction; Stimulus; System; Temporal Lobe; Testing; Variant; Visual impairment; Work; base; classical conditioning; computer framework; experience; flexibility; improved; insight; neural circuit; neuroimaging; neurotransmission; novel; object shape; operation; recruit; rehabilitation strategy; relating to nervous system; theories; visual object processing; visual processing; way finding

7. PROJECT SUMMARY/ABSTRACT The perceptual system uses prior experience to predict features of incoming stimuli, fill in missing detail, and recognize novel objects based on their relationships to similar, previously experienced objects. Predictions based on prior experience can influence acquisition of object knowledge, causing them to be integrated into representations of previously experienced stimuli. By integrating information acquired across multiple events, people can make novel decisions based on associations that have not been explicitly learned; this ability is thought to be critical for a number of complex behaviors such as semantic learning and spatial navigation. Despite the importance of this integration process, investigations of the neural circuits that shape object representations through predictive mechanisms have only recently begun to examine how information is integrated across separate experiences. Research has demonstrated that the ventral temporal cortex (VTC), hippocampus, and areas of the prefrontal cortex (PFC) are critically involved in integrating new content into existing object representations; however, many questions remain about how these regions work together to combine information from separate learned associations. Theoretical work suggests that integration involves a series of operations, including prediction based on existing associations, detection of an overlap with prior experience, and resolution of interference between competing associations; these processes cannot be separated with simple comparisons based on subsequent behavior. We will use a novel analysis strategy using neuroimaging and computational modeling that will allow us to determine how integration is accomplished in the brain. We will use high-resolution whole-brain functional magnetic resonance imaging (fMRI) to measure activity in regions of the VTC, hippocampus, and PFC, both during learning of associations that overlap with prior experience, and during a task that requires making novel decisions about associations that have not been directly observed. Neural signals measured during learning and testing will be used to constrain the behavior of a computational model of memory integration. Our modeling framework is based on the temporal context model (TCM), which describes operations involved in the construction and maintenance of a temporal context representation that is thought to serve as an overlapping code for bridging between related experiences. The model will be simultaneously constrained by multiple neural measures that provide estimates of variability in each of the computational mechanisms described by the model, allowing us to determine the relationship between different neural signals and the specific computations underlying associative learning. An improved understanding of how prior experience shapes object representations and affects new learning will provide insight into processes that affect perception and comprehension of real-world scenes. Furthermore, the proposed work will develop a neurocognitive modeling framework that will allow construction of personalized models for the memory systems of individual people, making it possible to create more targeted treatments for perceptual and learning deficits.

7.项目总结/摘要 感知系统使用先前的经验来预测传入刺激的特征，填充缺失的细节， 根据它们与类似的、以前经历过的物体的关系来识别新的物体。预测的基础 先前的经验可以影响对象知识的获得，使它们被整合到 以前经历过的刺激。通过整合跨多个事件获取的信息， 人们可以根据尚未明确学习的联想做出新颖的决定;这种能力是 它被认为是许多复杂行为的关键，如语义学习和空间导航。尽管 这种整合过程的重要性，对塑造物体表征的神经回路的研究 通过预测机制，直到最近才开始研究信息是如何整合到 不同的经历。研究表明，腹侧颞叶皮层（VTC），海马， 前额叶皮层（PFC）的区域在将新内容整合到现有对象中起着关键作用 然而，关于这些区域如何协同工作以联合收割机组合信息，仍然存在许多问题 从不同的学习协会。理论工作表明，整合涉及一系列操作， 包括基于现有关联的预测、与先前经验的重叠的检测以及解决 相互竞争的协会之间的干扰;这些过程不能用简单的比较分开 基于随后的行为。我们将使用一种新的分析策略，使用神经成像和计算 这将使我们能够确定整合是如何在大脑中完成的。我们将使用高分辨率 全脑功能磁共振成像（fMRI）来测量VTC区域的活动， 海马体和PFC，无论是在学习与先前经验重叠的联想过程中，还是在任务过程中， 这需要对尚未被直接观察到的关联做出新的决定。神经信号 在学习和测试过程中测量的数据将用于限制记忆计算模型的行为 一体化我们的建模框架是基于时间上下文模型（TCM），它描述了操作 参与时间上下文表示的构建和维护，该时间上下文表示被认为是 在相关经历之间建立桥梁的重叠代码。该模型将同时受到以下约束： 多个神经测量，其提供所描述的每个计算机制中的可变性的估计 通过模型，使我们能够确定不同神经信号与特定神经元之间的关系。 联想学习背后的计算。更好地理解先前的经验如何塑造对象 表征和影响新的学习将提供对影响感知和 对真实世界场景的理解。此外，拟议的工作将开发一个神经认知模型， 这个框架将允许为个人的记忆系统构建个性化的模型， 从而有可能为感知和学习缺陷创造更有针对性的治疗方法。