GRK 2340: Computational Cognition

计算认知研究训练小组追求认知科学和人工智能的重新整合。该项目的学生将接受这两个领域的培训，并将联合收割机从这两个领域的见解相结合，以了解人类和机器的智能。我们已经确定了两个研究领域，在那里这种重新整合将特别富有成效。我们对基本感觉运动过程背后的神经信号了解很多，对推理、解决问题或语言的认知过程也了解不少。然而，解释高层次认知如何从低层次机制中产生是认知科学中一个长期存在的开放性问题。机器学习最近在深度学习方法和递归神经网络方面取得了很大进展。与此同时，认知科学家也探索了类似的想法，例如统一神经学习理论的预测编码。第一个集群中的博士项目将解决问题，例如语法学习，结构化表示或使用神经建模产生复杂行为。因此，整合认知科学和人工智能的思想将使我们能够最终弥合低级和高级认知之间的差距。第二，人类智能处理高度结构化但不完整的知识。因此，潜在的表示和过程能够产生新的概念，并考虑到不确定性。沿着这些思路，类比推理、语言、语用推理和概念形成被认为是理解人类智能的关键。第二组的博士项目将解决这些领域的典型问题，这些问题对人类来说很容易，但对人工智能来说仍然很难。它们包括类比推理、概念创造和语用推理。对于这些认知过程中的每一个，都有来自认知科学的可靠数据和见解，使我们能够对这些过程进行建模。这是一个很大的优势相比，早期的人工智能，在人类认知知之甚少。新的研究培训组将被整合到认知科学博士课程，成立于2002年。研究培训组的学生将受益于一个高度跨学科的环境，尽管如此，它专注于一个共同的主题，并提供了许多项目之间的方法协同作用。

The Research Training Group in Computational Cognition pursues the re-integration of cognitive science and artificial intelligence. Students in the program will be trained in both fields and will combine insights from both fields to understand intelligence in humans and machines. We have identified two areas of research where this re-integration will be particularly fruitful.First, there is a schism between low- and high-level cognition. We understand a lot about the neural signals underlying basic sensorimotor processes, and we know a fair bit about the cognitive processes involved in reasoning, problem solving, or language. However, explaining how high-level cognition can arise from lowlevel mechanisms is a long-standing open problem in cognitive science. Machine learning has recently made great progress on deep learning methods and recurrent neural networks. At the same time, cognitive scientists have explored similar ideas, such as predictive coding for unified neural theories of learning. PhD projects in the first cluster will tackle problems, such as grammar learning, structured representations, or the production of complex behaviors with neural modeling. Thus, integrating ideas from cognitive science and AI will allow us to finally bridge the gap between low- and high-level cognition.Second, human intelligence deals with highly structured, yet incomplete knowledge. Thus, the underlying representations and processes are able to generate new concepts and to take into account uncertainty. Along these lines, analogical reasoning, language, pragmatic inference and concept formation have been proposed as being the key to understand human intelligence. PhD projects in the second cluster will tackle exemplary problems of these domains that are easy for humans, but still hard for AI. They include analogical reasoning, concept invention, and pragmatic inferences. For each of these cognitive processes, there are reliable data and insights from cognitive science that will allow us to model these processes. This is a large advantage compared to the early days of AI where little was known about human cognition.The new Research Training Group will be integrated into the Cognitive Science PhD program that was established in 2002. Students in the Research Training Group will benefit from a highly interdisciplinary environment that is, nevertheless, focused on a common theme and that provides many methodological synergies between projects.