Collaborative Research: Knowledge Transmission Through Iterated Learning

人类的许多知识不是从我们周围的物质世界学到的，而是从其他人那里学到的。例如，每一代新的物理学家不必重新发现牛顿力学定律;这些定律从一个物理学家传给下一个。 语言、音乐和其他复杂的知识也会出现同样的“迭代学习”，可以说，这些知识不容易从头开始重新学习。 尽管迭代学习在人类文化和社会中无处不在，但人们对知识从一个人传递到另一个人时发生变化的认知过程知之甚少。 在美国国家科学基金会的支持下，Kalish博士和Griffiths博士将通过测试和扩展将文化进化的数学与人类认知过程统一起来的理论来研究这个问题。 他们将进行一系列实验，旨在重现实验室环境中的迭代学习过程，并通过开发计算模型来补充实验，该模型旨在根据观察到的学习数据测试理论预测。 主要的问题是，在传播过程中可能发生的相对较小的知识扰动是否会在多次迭代过程中产生较大的影响。 这些实验的结果有望为我们提供一些重要的和一般的人类认知理论，以及知识的文化传播理论的限制。

Much of human knowledge is not learned from the physical world around us, but from other people. For example, each new generation of physicists does not have to rediscover the laws of Newtonian mechanics; these laws are passed on from one physicist to the next. This same kind of "iterated learning" occurs for language, music, and other kinds of complex knowledge that are not easily relearned from scratch, so to speak. Despite the ubiquity of iterated learning in human culture and society, very little is known about the cognitive processes by which knowledge changes when it is transmitted from one person to the next. With support from the National Science Foundation, Dr. Kalish and Dr. Griffiths will investigate this issue by testing and extending theories that unify the mathematics of cultural evolution with the processes of human cognition. They will conduct a series of experiments that are designed to reproduce the processes of iterated learning in a laboratory environment, and the experiments will be complemented by the development of computational models designed to test the theoretical predictions against the observed learning data. The main issue is whether relatively small perturbations in knowledge that may occur in the act of transmission will or will not add up to have large effects over the course of many iterations. The results of these experiments promise to provide us with some important and general constraints on theories of human cognition, as well as theories of cultural transmission of knowledge.