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

大脑利用相似性从已知到未知进行归纳。例如，当一个人遇到一种新的水果，必须决定它是否可食用，这个人必须判断它与已知的可食用和不可食用的食物有多相似。然而，考虑到的相似性类型很重要。椰子看起来像一块石头（视觉相似），但要决定是否可食用，关键是它挂在一棵多叶的树上（语义相似）。在国家科学基金会的资助下，罗彻斯特大学的Rajeev Raizada博士正在研究大脑是如何利用相似性对不断变化的环境做出适应性反应的。了解了诸如视觉相似性和语义相似性等相似类型是如何在大脑中编码的，就有可能从神经信号中解码它们。在这个项目中，Raizada博士将脑成像与计算建模和行为测试相结合。他正在开发一种新的神经解码方法，根据人们感知到的刺激的计算模型来预测大脑模式的相似性。此外，这些方法旨在调查不同人的大脑活动模式。该项目中正在开发的新型计算方法可能会产生重大的广泛影响，例如，这些技术支持脑机接口，试图恢复与闭锁患者的沟通。此外，大脑中语义相似性的建模对语义性痴呆等疾病也有影响。这也可能对技术产生影响。大脑对变化的环境做出灵活的反应，但相比之下，人工系统往往太脆弱了。当面对与熟悉的环境相似但不完全相同的情况时，他们会崩溃。对大脑如何从已知到未知进行概括的认识有可能改变我们对大脑如何实现其适应性的认识，为赋予具有类似技能的人工系统开辟了新的途径。

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.