CompCog: Computational, distributed accounts of human memory: improving cognitive models

CompCog：人类记忆的计算分布式账户：改进认知模型

• 批准号: 1734304
• 负责人: Prasenjit Mitra
• 金额: $ 50万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1734304&HistoricalAwards=false
• 关键词: CompCog; Computational; distributed; accounts; human

Memory is among the most impressive aspects of human cognition, allowing us to learn new words or new ideas from just a few examples. However, the scientific understanding of how this learning occurs is limited. This research project focuses on how learning occurs in the context of memory for language. Within the human mind, there is something like a dictionary that tells people what words mean (semantics) and how words are combined to make grammatical sentences (syntax). How does the mind learn this dictionary from experience with a language? Computer simulations can help science better understand this learning process. This scientific understanding can, in turn, help teach languages in the classroom and aid in the early detection of language deficits, whether it be developmental deficits in children, or age-related deficits in adults. Furthermore, improving the ability of computers to simulate language learning processes can also lead to the development of better technology such as machine translation, web search, and virtual assistants. This project considers how a better understanding of language learning can help us avoid common pitfalls of memory connected to the use of language. For example, humans easily over-generalize and judge a "book by its cover", associating certain occupations or personality traits with a gender. If we know how people come up with associations between words and concepts, we can also detect and prevent prejudices in language to help ensure that artificial intelligence applications, such as web search, do not produce prejudiced results. The project supports an interdisciplinary and diverse team of researchers and students at Penn State, attracting college students to engage with research in cognitive science and artificial intelligence.In this project, the researchers are designing a new model of human memory, the Hierarchical Holographic Model. This computational model helps explain certain aspects of how words and languages are learned. The model draws on the successes of artificial intelligence and deep neural networks, and applies these insights to psychology. With this model, the researchers investigate the question of whether human memory has the ability to detect arbitrarily indirect associations between concepts. The model uses a recursive learning process, building on previously learned knowledge to acquire new knowledge, which allows the model to learn arbitrarily indirect and abstract relationships between words. The researchers consider evidence that sensitivity to abstract relations between words improves the ability of the computer model to learn syntax, such as parts-of-speech, and to use words appropriately to construct grammatical sentences. This work will be assessed against human language data and competing computational models. The success of the computational model should provide evidence that (1) language acquisition depends on indirect associations, and (2) human memory must be able to form indirect associations to facilitate it.

记忆是人类认知中最令人印象深刻的方面之一，它使我们能够从几个例子中学习新单词或新想法。 然而，对这种学习如何发生的科学理解是有限的。 这个研究项目的重点是学习如何在语言记忆的背景下发生。在人类的头脑中，有一种类似于字典的东西，告诉人们单词的意思（语义）以及单词如何组合成合乎语法的句子（语法）。大脑是如何从语言的经验中学习这本词典的？计算机模拟可以帮助科学更好地理解这种学习过程。反过来，这种科学的理解可以帮助在课堂上教授语言，并有助于早期发现语言缺陷，无论是儿童的发育缺陷，还是成人的年龄相关缺陷。此外，提高计算机模拟语言学习过程的能力也可以导致更好的技术的发展，如机器翻译，网络搜索和虚拟助手。 该项目考虑如何更好地理解语言学习可以帮助我们避免与语言使用相关的常见记忆陷阱。 例如，人类很容易过度概括和判断一本书的封面，将某些职业或个性特征与性别联系起来。 如果我们知道人们如何在单词和概念之间建立联系，我们还可以检测和防止语言中的偏见，以帮助确保人工智能应用程序（如网络搜索）不会产生有偏见的结果。 该项目支持宾夕法尼亚州立大学的一个跨学科和多元化的研究人员和学生团队，吸引大学生参与认知科学和人工智能的研究。在这个项目中，研究人员正在设计一种新的人类记忆模型，即层次全息模型。 这个计算模型有助于解释单词和语言是如何学习的某些方面。 该模型借鉴了人工智能和深度神经网络的成功，并将这些见解应用于心理学。 通过这个模型，研究人员研究了人类记忆是否有能力检测概念之间的任意间接关联。 该模型使用递归学习过程，建立在以前学习的知识，以获得新的知识，这使得模型可以学习任意间接和抽象的词之间的关系。研究人员认为，有证据表明，对单词之间抽象关系的敏感性提高了计算机模型学习语法的能力，例如词性，并适当地使用单词来构建语法句子。这项工作将根据人类语言数据和竞争计算模型进行评估。计算模型的成功应该提供证据证明：（1）语言习得依赖于间接联想，（2）人类记忆必须能够形成间接联想来促进它。

项目成果

Toward Cognitively Constrained Models of Language Processing: A Review

• DOI: 10.3389/fcomm.2017.00011
• 发表时间: 2017-09
• 期刊: Frontiers in Communication
• 影响因子: 2.4
• 作者: Margreet Vogelzang;Anne C. Mills;D. Reitter;Jacolien van Rij;P. Hendriks;H. van Rijn

Are {BERT}s Sensitive to Native Interference in L2 Production?

{BERT} 对 L2 生成中的本机干扰敏感吗？

• DOI: 10.18653/v1/2021.insights-1.6
• 发表时间: 2021
• 期刊: Proceedings of the Second Workshop on Insights from Negative Results in NLP
• 作者: Tang, Z;Mitra, P;Reitter, D.
• 通讯作者: Reitter, D.

Like a Baby: Visually Situated Neural Language Acquisition

• DOI: 10.18653/v1/p19-1506
• 发表时间: 2018-05
• 作者: Alexander Ororbia;A. Mali;M. Kelly;D. Reitter

Indirect associations in learning semantic and syntactic lexical relationships.

学习语义和句法词汇关系中的间接关联。

• DOI: 10.1016/j.jml.2020.104153
• 发表时间: 2020
• 期刊: Journal of memory and language
• 影响因子: 4.3
• 作者: Kelly, M.A.;Ghafurian M., West;Reitter, D.
• 通讯作者: Reitter, D.

How Language Processing can Shape a Common Model of Cognition

语言处理如何塑造通用认知模型

• DOI: 10.1016/j.procs.2018.11.047
• 发表时间: 2018
• 期刊: Procedia Computer Science
• 作者: Kelly, Matthew A.;Reitter, David
• 通讯作者: Reitter, David