"Learning, memory, development, and evolution"

这项建议侧重于构建和测试学习、记忆、发展和进化的计算模型，以更好地理解这些过程在人类中是如何工作的。为了学习和发展，我们使用受大脑工作原理启发的人工神经网络。这样的模型有助于理解心理发展中的各种问题：为什么儿童在思维中经历阶段，为什么儿童犯错误，以及儿童认知发展障碍的原因和补救措施。好的模型实现了一个具体的、易于学习的现象版本，在这个版本中，不重要的细节被剥离，以便更容易地理解基本原理。在这项建议中，我们更具体地关注于模拟儿童对他人思维的想法的发展，特别是作为行为基础的信念和欲望的发展。孩子们经历了一个早期阶段，在这个阶段，他们认为其他人和他们自己拥有同样的信仰。后来，孩子们开始意识到，其他人有时可能怀有错误的信念。我们还建议对记忆提取如何将这些记忆置于不稳定状态进行建模，从而使它们能够重新整合。在新的相关信息存在的情况下，检索到的记忆可以改变。这对目击者错误的证词以及创伤后应激障碍的治疗都有影响。我们还将模拟婴儿在自己的学习中发挥积极作用的能力，例如，通过放弃对他们来说太难学习的任务。这表明，他们可能有能力监控自己在学习上的进展，并有意识在缺乏进展的情况下转向其他事情。最后，我们还模拟了不同类型的学习能力在代理种群中的生物进化，这些代理相互作用、解决问题、根据其适应度进行繁殖，最终死亡。我们感兴趣的是个体学习、模仿和关于世界的理论从一个代理人传递到另一个代理人之间的相互作用。我们的目标是发现这些不同的学习技术是如何通过自然选择创造出来的，以及它们如何反过来影响生物进化的过程。********

This proposal focuses on building and testing computational models of learning, memory, development, and evolution to better understand how these processes work in humans. For learning and development, we use artificial neural networks inspired by principles of how brains work. Such models contribute to understanding of a variety of issues in psychological development: why children go through stages in their thinking, why children make the mistakes that they do, and the causes and remedies for disorders in children's cognitive development. Good models implement a concrete, easy-to-study version of phenomena, in which unimportant details are stripped away so that the basic principles can be more easily understood. In this proposal, we concentrate more specifically on simulating the development of children's ideas about other people's minds, particularly the beliefs and desires that underlie behaviour. Children go through an early stage in which they assume that other people share the same beliefs as they themselves possess. Then later children come to appreciate that other people may sometimes harbour false beliefs. We also propose to model how memory retrieval puts those memories in an unstable state allowing them to be reconsolidated. In the presence of new, relevant information, retrieved memories can be changed. This has implications for faulty eyewitness testimony as well as treating post-traumatic stress. We will also simulate the ability of infants to take an active role in their own learning, e.g., by quitting tasks that are too difficult for them to learn. This suggests that they might have the ability to monitor their own progress in learning and the sense to move on to something else when there is lack of progress. Finally, we also simulate the biological evolution of different kinds of learning ability in populations of agents that interact, solve problems, reproduce according to their fitness, and eventually die. We are interested in the interplay between individual learning, imitation, and the passing of theories about the world from one agent to another. We aim to discover how these different learning techniques are created by natural selection and how they, in turn, affect the course of biological evolution. ********