Measuring and modeling object similarity in the brain: combining conceptual and perceptual representations

大脑中物体相似性的测量和建模：结合概念和感知表征

• 批准号: 1228261
• 负责人: Rajeev Raizada
• 金额: $ 48.67万
• 依托单位: University of Rochester
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1228261&HistoricalAwards=false
• 关键词: Measuring; modeling; object; similarity; brain

The brain uses similarity to generalize from the known to the unknown. For example, when a person encounters a new type of fruit and has to decide whether or not it is edible, the person must judge how similar it is to already known edible and inedible items. However, the type of similarity that is taken into account matters. A coconut can look like a rock (visual similarity), but for making a decision about edibility the fact that it hangs from a leafy tree (semantic similarity) is key. With funding from the National Science Foundation, Dr. Rajeev Raizada of Rochester University is investigating how the brain uses similarity to respond adaptively to changing circumstances. With an understanding of how types of similarity, such as visual and semantic similarity, are encoded in the brain, it should be possible to decode them from neural signals. In this project, Dr. Raizada is combining brain imaging with computational modeling and behavioral testing. He is developing novel methods of neural decoding to predict the similarity of brain patterns on the basis of computational models of the stimuli that people are perceiving. In addition, the methods are designed to investigate patterns across different people's brain activations. The novel computational methods being developed in the project could have significant broader impacts, for example, such techniques underpin brain-computer interfaces that attempt to restore communication to locked-in patients. Moreover, the modeling of semantic similarity in the brain has implications for disorders such as semantic dementia. There are also possible implications for technology. The brain responds flexibly to changing circumstances, but artificial systems, in contrast, are all too often brittle. When confronted with circumstances similar, but not identical, to familiar ones, they break down. Insights into how the brain generalizes from the known to the unknown have the potential to transform our knowledge of how the brain achieves its adaptability, opening up new avenues for endowing artificial systems with similar skills.

大脑利用相似性从已知概括到未知。例如，当一个人遇到一种新型水果并必须决定它是否可以食用时，他必须判断它与已知的可食用和不可食用的物品有多相似。然而，考虑的相似性类型很重要。椰子可能看起来像一块岩石（视觉上的相似性），但对于做出可食用性的决定来说，它悬挂在枝繁叶茂的树上（语义上的相似性）这一事实是关键。在美国国家科学基金会的资助下，罗切斯特大学的 Rajeev Raizada 博士正在研究大脑如何利用相似性来适应性地应对不断变化的环境。了解了相似类型（例如视觉和语义相似性）如何在大脑中编码后，应该可以从神经信号中解码它们。在这个项目中，Raizada 博士将大脑成像与计算建模和行为测试相结合。他正在开发神经解码的新方法，以根据人们感知的刺激的计算模型来预测大脑模式的相似性。此外，这些方法旨在研究不同人的大脑激活模式。该项目中正在开发的新颖计算方法可能会产生更广泛的影响，例如，此类技术支持脑机接口，试图恢复与锁定患者的通信。此外，大脑中语义相似性的建模对语义痴呆等疾病也有影响。对技术也可能产生影响。大脑对不断变化的环境做出灵活的反应，但相比之下，人工系统往往很脆弱。当遇到与熟悉的情况类似但又不相同的情况时，他们就会崩溃。对大脑如何从已知概括到未知的见解有可能改变我们对大脑如何实现适应性的认识，为赋予人工系统类似的技能开辟新途径。