Postdoctoral Fellowship: SPRF: A Comprehensive Modeling Framework for Semantic Memory Search

博士后奖学金：SPRF：语义记忆搜索综合建模框架

• 批准号: 2313985
• 负责人: Nicholas Ichien
• 金额: $ 16万
• 依托单位: Ichien, Nicholas
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-02-01 至 2026-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2313985&HistoricalAwards=false
• 关键词: Postdoctoral; Fellowship; SPRF; Comprehensive; Modeling

This award was provided as part of NSF's Social, Behavioral and Economic Sciences (SBE) Postdoctoral Research Fellowships (SPRF) program. The goal of the SPRF program is to prepare promising, early career doctoral-level scientists for scientific careers in academia, industry or private sector, and government. SPRF awards involve two years of training under the sponsorship of established scientists and encourage Postdoctoral Fellows to perform independent research. NSF seeks to promote the participation of scientists from all segments of the scientific community, including those from underrepresented groups, in its research programs and activities; the postdoctoral period is considered to be an important level of professional development in attaining this goal. Each Postdoctoral Fellow must address important scientific questions that advance their respective disciplinary fields. Under the sponsorship of Dr. Sudeep Bhatia at the University of Pennsylvania, this postdoctoral fellowship award supports an early career scientist investigating how adults search their general knowledge to answer simple questions. People search their general knowledge every day when they converse, come up with new ideas, recognize familiar and novel objects, etc. The aim of the proposed research is to build a computer model that searchers a store of knowledge in the same way that humans do. Developing such a computer model enables formal specification of the processes that the mind carries out when it accomplishes certain behaviors, and these models serve as quantitative, mathematical theories of human thought. This project presents a novel and unified computational approach for modeling how people generate ideas from what they know. We aim to evaluate this approach by collecting human responses to these simple tasks and examining how well basic variants of our model can predict these responses. Once we have a good idea that our model coheres with the way that humans search their memory, we will be able examine the memory in more clinical populations. The current proposal details a project which aims build a computational model of semantic knowledge retrieval and test model assumptions using naturalistic human experiments. The proposed model aims to clarify how people retrieve knowledge from memory, an important issue that has received considerable attention from cognitive scientists and psychologists. However, researchers have not yet developed cognitive process models of semantic memory search that can parameterize mechanisms involved in knowledge retrieval, and predict sequences of concepts, features, or relations listed by human participants, in response to arbitrary open-ended knowledge retrieval prompts. There are, for example, millions of potential features and relations that could describe a given target concept. Specifying these features and relations, and modeling the memory search processes that operate on these features and relations, poses significant theoretical and technical challenges for researchers. We plan to address these challenges using new techniques in artificial intelligence known as transformer networks. We will use existing feature norm datasets to train the networks to predict which of millions of distinct features and relations hold for common concepts. We will then use these trained networks to generate a knowledge base constituting the representations over which our proposed models of semantic memory search operate. We will subsequently evaluate our models using individual-level parametric model fitting on a wide range of open-ended knowledge retrieval tasks, including semantic fluency, feature generation, and analog generation. If successful, this project will offer a novel theoretical paradigm that integrates computational models of semantic cognition with cognitive process models of memory search.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项是作为NSF社会、行为和经济科学(SBE)博士后研究奖学金(SPRF)计划的一部分提供的。SPRF计划的目标是为学术界、工业界或私营部门和政府的科学职业生涯培养有前途的、早期职业博士水平的科学家。SPRF奖项包括在知名科学家的赞助下进行两年的培训，并鼓励博士后研究员进行独立研究。国家科学基金会致力于促进科学界所有阶层的科学家参与其研究方案和活动，包括那些来自代表性不足的群体的科学家；博士后阶段被认为是实现这一目标的专业发展的一个重要水平。每个博士后研究员都必须解决推动各自学科领域向前发展的重要科学问题。在宾夕法尼亚大学苏迪普·巴蒂亚博士的赞助下，这个博士后奖学金奖项支持一位早期职业科学家，他研究成年人如何搜索他们的常识来回答简单的问题。人们每天在交谈、提出新想法、识别熟悉的和新的物体时都会搜索他们的一般知识。这项拟议的研究的目的是建立一个计算机模型，让人们像人类一样搜索知识宝库。开发这样的计算机模型能够形式化地规范大脑在完成某些行为时所执行的过程，这些模型是人类思维的量化数学理论。这个项目提供了一种新颖而统一的计算方法，用于对人们如何根据他们所知道的产生想法进行建模。我们的目标是通过收集人类对这些简单任务的反应，并检查我们模型的基本变体能否很好地预测这些反应，来评估这种方法。一旦我们有了一个好的想法，我们的模型与人类搜索记忆的方式相一致，我们将能够检查更多临床人群的记忆。目前的提案详细介绍了一个项目，该项目旨在建立语义知识检索的计算模型，并使用自然主义的人体实验来测试模型假设。提出的模型旨在阐明人们如何从记忆中提取知识，这是认知科学家和心理学家相当关注的一个重要问题。然而，研究人员还没有开发出语义记忆搜索的认知过程模型，该模型可以对知识检索中涉及的机制进行参数化，并对人类参与者列出的概念、特征或关系序列做出预测，以响应任意的开放式知识检索提示。例如，有数以百万计的潜在特征和关系可以描述给定的目标概念。指定这些特征和关系，并对在这些特征和关系上操作的记忆搜索过程进行建模，给研究人员带来了重大的理论和技术挑战。我们计划使用人工智能中的新技术--变压器网络--来应对这些挑战。我们将使用现有的特征范数数据集来训练网络，以预测数百万个不同的特征和关系中的哪些适用于共同的概念。然后，我们将使用这些经过训练的网络来生成知识库，该知识库构成了我们提出的语义记忆搜索模型在其上操作的表示。随后，我们将使用个人级别的参数模型来评估我们的模型，这些模型适用于广泛的开放式知识检索任务，包括语义流畅性、特征生成和模拟生成。如果成功，该项目将提供一种新的理论范式，将语义认知的计算模型与记忆搜索的认知过程模型相结合。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。