Sequential learning from a scale-invariant representation of remembered time

可以说，记忆的主要适应功能不是记住过去，而是预测未来。 对过去经验的了解与对当前状态的理解相结合，使生物体能够预测未来的回报或避免即将发生的危险。 这项研究的目标是开发一种数学理论，描述人们如何利用过去的历史和现在的表现来预测未来。 该理论将建立在一个数学模型的基础上，该模型可以有效地将导致当前时刻的历史压缩成可以由大脑维持的表示。 研究人员使用三种技术来追求理论的发展。 首先，为了观察人类学习者的行为是否与假设一致，研究人员将以本科生为研究对象进行一系列行为实验。 这些实验向受试者展示了一系列根据隐藏序列选择的符号，并要求他们预测在不同阶段会出现的符号。 其次，研究人员将进行计算机模拟，在大量自然语言上训练方程。 语言具有丰富的时间结构，由单词和单词组合的方式来定义。 第三，研究人员将努力扩展假设的数学，使其能够描述学习和记忆中的各种现象。 大规模的目标是重新定位认知心理学的几个子领域-情景记忆，语义记忆，条件反射和间隔时间-围绕对人类大脑如何表征和利用时间历史的理解。如果成功，拟议的研究可能会产生深远的实际影响。 它可以提供对儿童和成人如何学习的洞察力，从而产生更好的教学工具。 如果成功，它也将代表着完全自动化的自然语言处理向前迈进了一大步。 电子通信的兴起产生了大量的文本-甚至比一小群人类读者所能阅读的还要多。 目前，可以从大量文本中提取知识的算法在广泛的应用中得到了应用，如论文评分和其他教育应用到智能应用。 研究人员预计，这些方程在从自然文本中提取知识方面比几种广泛使用的算法要好得多。 最后，可能会有一些有用的技术，可以利用人类的效率水平，从过去和现在预测未来。

It can be argued that the primary adaptive function of memory is not to remember the past, but to predict the future. Knowledge of past experiences combined with an understanding of the present state gives organisms the ability to anticipate future rewards or avoid impending danger. The goal of the proposed research is to develop a mathematical theory describing how people use past history and a representation of the present to predict the future. The theory will be built on a mathematical model of how the history leading up to the present moment can be efficiently compressed into a representation that could be maintained by the brain. The investigators pursue the development of the theory using three techniques. First, in order to see if human learners behave in a way consistent with the hypothesis, the investigators will conduct a series of behavioral experiments using undergraduate students as research subjects. The experiments present the subjects with a series of symbols chosen according to a hidden sequence and ask them to predict the symbols that will follow at various stages. Second, the investigators will conduct computer simulations to train the equations on a large body of naturally-occuring language. Language has a rich temporal structure defined by the way words, and combinations of words, follow one another. Third, the investigators will work to extend the mathematics of the hypothesis to enable it to describe a wide range of phenomena in learning and memory. The large scale goal is to reorient several subfields of cognitive psychology---episodic memory, semantic memory, conditioning, and interval timing---around an understanding of how temporal history is represented and utilized by the human brain.If successful, the proposed research could have far-reaching practical impacts. It could provide insight into how children and adults learn, leading to better instructional tools. If successful, it would also represent a large step forward in completely automated natural language processing. The rise of electronic communication has led to vast quantities of text---more than could ever be read by even a small army of human readers. Algorithms that can extract knowledge from large quantities of text currently find use in applications as far-ranging as essay grading and other educational applications to intelligence uses. The investigators anticipate that the equations will be much better at extracting knowledge from natural text than several widely-used algorithms. Finally, there may be useful technologies that exploit the ability to predict the future from the past and the present with the level of efficiency that humans can.