Coordination Funds

研究单位竞技场的总体目标是促进理解语义知识表示如何在神经架构中出现和编码-在人脑网络和人工智能（AI）模型中。我们将研究不同抽象层次的知识表示，从对同一对象的不同感官实例的不变性（例如，从不同的角度看对象）到概括不同对象之间的共性的分类表示，到表示这样的对象的语言学意义和可以通过多个输入模态访问的抽象概念-语义表示（如通过看到图像以及通过听到婴儿哭泣或阅读单词“婴儿”来激活概念“婴儿”）。理解这种表示背后的编码和计算仍然是认知神经科学和人工智能建模的挑战。我们建议，人工智能模型与机器学习和认知计算神经科学专业知识相结合，可以利用系统地扩展我们对分层组织知识的理解，沿着人类思维和大脑中的这种抽象梯度，同时为人工智能模型开发提供灵感。为此，竞技场将把实验心理学和认知神经科学的悠久传统与近年来面向人工智能的计算机模型的爆炸性发展相结合，以解决以下研究问题：（1）大脑中越来越抽象的语义知识的表示背后的计算原理是什么，以及如何将其整合到人工智能模型中？(2)是什么原理让抽象表征在发育中的大脑中出现，以及如何将这些原理整合到人工智能模型中？(3)大脑如何能够比当前的人工智能模型更自主地学习这种表示，我们如何模仿这种表示？(4)如何才能有效地访问和导航大脑和人工智能模型中抽象知识的高维表示以进行行为？推导出语义表示的计算上明确的解释将使我们更接近于破译人类大脑中的认知过程，并可能为更通用和更像人类的人工智能铺平道路。为了实现这一雄心勃勃的目标，竞技场联合了来自计算机科学，心理学和认知神经科学领域的PI，包括这些领域交叉点的桥梁教授。结合起来，竞技场PI在人工智能和其他计算建模方法方面拥有丰富的经验，并在人类行为和大脑过程的实验研究方面拥有长期的经验。竞技场研究单位的跨学科性质将成为跨学科协调共同研究兴趣的催化剂，并建立正式的结构，使我们能够系统地培养跨学科的协同作用。

The overarching goal of the research unit ARENA is to advance understanding of how semantic knowledge representations can emerge and are encoded in neural architectures – in human brain networks and in artificial intelligence (AI) models. We will study knowledge representations across varying levels of abstraction, ranging from invariance against different sensory instances of the same object (e.g., seeing an object from different angles) to categorical representations summarizing commonalities across different objects, to linguistic meanings denoting such objects and abstract conceptual-semantic representations that can be accessed through multiple input modalities (like the concept ‘baby’ being activated by seeing the image as well as by hearing a baby crying or reading the word ‘baby’). Understanding the coding and computations underlying such representations remains a challenge for cognitive neuroscience and AI modeling alike. We propose that AI models in combination with machine learning and cognitive computational neuroscience expertise can be harnessed to systematically extend our understanding of hierarchically organized knowledge along such gradients of abstractness in the human mind and brain, and at the same time serve as inspiration for AI model development. To this end, ARENA will integrate the long tradition of experimental psychology and cognitive neuroscience with the explosive development in AI-oriented computer models in recent years to tackle the following research questions: (1) What are the computational principles underlying the representation of increasingly abstract semantic knowledge in the brain and how can these be integrated into AI models? (2) What are the principles that allow for abstract representations to emerge in the developing brain and how can these be incorporated into AI models? (3) How can the brain learn such representations so much more autonomously than current AI models and how can we mimic this? (4) How can these high-dimensional representations of abstract knowledge in the brain and in AI models be accessed and navigated efficiently for behavior? Deriving computationally explicit accounts of semantic representations will bring us closer to deciphering cognitive processes in the human brain and may pave the way for more versatile and human-like AIs. To achieve this ambitious goal, ARENA unites PIs from the fields of computer science, psychology, and cognitive neuroscience, including a bridge professor at the intersection of these fields. Combined, ARENA PIs have extensive experience in AI and other computational modeling approaches and long-standing experience in experimental investigations of human behavior and brain processes. The interdisciplinary nature of the ARENA research unit will serve as catalyst for aligning common research interests across disciplines, and to establish formal structures that allow us to systematically cultivate interdisciplinary synergies.