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概括。例如，当一个人遇到一种新的水果，必须决定它是否可以食用时，这个人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，所考虑的相似性类型很重要。椰子看起来像一块石头（视觉相似性），但对于决定可食用性来说，它挂在多叶树上的事实（语义相似性）是关键。在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不断变化的环境。随着对相似性类型（如视觉和语义相似性）如何在大脑中编码的理解，应该有可能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。他正在开发新的神经解码方法，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大脑激活的模式。该项目中开发的新计算方法可能会产生更广泛的影响，例如，这些技术支持脑机接口，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有影响。这也可能对技术产生影响。大脑对环境变化的反应很灵活，但相比之下，人工系统往往很脆弱。当遇到与熟悉的环境相似但不完全相同的情况时，他们就会崩溃。对大脑如何从已知到未知进行概括的洞察，有可能改变我们对大脑如何实现其适应性的认识，为赋予人工系统类似的技能开辟新的途径。

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.