Coordination Funds

研究单位Arena的首要目标是促进对语义知识表示如何在神经体系结构中--在人脑网络和人工智能(AI)模型中--出现和编码的理解。我们将研究不同抽象程度的知识表征，从对同一对象的不同感官实例的不变性(例如，从不同的角度看到一个对象)到概括不同对象之间共性的范畴表征，再到表示此类对象的语言含义，以及可以通过多个输入通道访问的抽象概念-语义表征(例如，通过看到图像以及通过听到婴儿哭泣或阅读单词“Baby”来激活概念“Baby”)。对于认知神经科学和人工智能建模来说，理解这些表示背后的编码和计算仍然是一个挑战。我们认为，人工智能模型与机器学习和认知计算神经科学的专业知识相结合，可以沿着人脑和大脑的抽象梯度系统地扩展我们对分层组织知识的理解，同时作为人工智能模型开发的灵感。为此，Arena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AI的计算机模型的爆炸性发展结合起来，以解决以下研究问题：(1)大脑中日益抽象的语义知识的表示背后的计算原理是什么，这些原理如何整合到AI模型中？(2)允许抽象表征出现在发育中的大脑中的原理是什么，如何将这些抽象表征整合到AI模型中？(3)大脑如何比当前的AI模型更自主地学习这种表征，我们如何才能模仿它？)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？推导出对语义表征的计算显式描述将使我们更接近于破译人脑中的认知过程，并可能为更多功能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，Arena联合了来自计算机科学、心理学和认知神经科学领域的PI，包括一名位于这些领域交叉点的桥梁教授。总而言之，Arena 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。Arena研究单位的跨学科性质将作为催化剂，协调各学科的共同研究兴趣，并建立正式结构，使我们能够系统地培养跨学科协同效应。

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.