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NSF的社会，行为和经济科学（SBE）博士后研究奖学金（SPRF）计划的一部分。 SPRF计划的目标是为学术界，工业或私营部门以及政府的科学职业准备有前途的早期职业博士学位科学家。 SPRF奖项涉及在既定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。 NSF试图促进科学界各个细分市场的科学家的参与，包括来自代表性不足的群体的研究计划和活动；博士后时期被认为是实现这一目标的重要水平。每个博士后研究员都必须解决重要的科学问题，以推进各自的学科领域。在宾夕法尼亚大学的Sudeep Bhatia博士的赞助下，该博士后奖学金奖支持了一位早期的职业科学家，调查了成年人如何搜索他们的常识来回答简单的问题。人们每天都在探索他们的常识，当他们交谈，提出新的想法，认识熟悉和新颖的对象等。拟议研究的目的是建立一个计算机模型，该模型以与人类相同的方式搜索知识存储。开发这样的计算机模型可以正式规范思想在完成某些行为时进行的过程，这些模型是人类思想的定量数学理论。该项目提出了一种新颖而统一的计算方法，用于建模人们如何从他们所知道的观点中产生思想。我们旨在通过收集人类对这些简单任务的反应并研究模型的基本变体可以预测这些响应的能力来评估这种方法。一旦我们有一个很好的主意，即我们的模型与人类搜索记忆的方式相遇，我们将能够检查更多临床人群中的记忆。当前的建议详细介绍了一个项目，该项目旨在建立使用自然主义人类实验的语义知识检索和测试模型假设的计算模型。拟议的模型旨在阐明人们如何从记忆中获取知识，这是一个重要的问题，它引起了认知科学家和心理学家的大量关注。但是，研究人员尚未开发出语义记忆搜索的认知过程模型，该模型可以参与知识检索涉及的机制，并预测人类参与者列出的概念，特征或关系序列，以响应任意开放式知识检索提示。例如，有数百万个潜在的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，并建模在这些特征和关系上运作的记忆搜索过程，对研究人员构成了重大的理论和技术挑战。我们计划使用称为变压器网络的人工智能中的新技术来应对这些挑战。我们将使用现有的功能标准数据集来训练网络，以预测数百万个不同特征和关系的共同概念中的哪些。然后，我们将使用这些训练有素的网络来生成一个知识库，构成我们提出的语义内存搜索模型运行的表示形式。随后，我们将使用个人级参数模型拟合在各种开放式知识检索任务上进行评估，包括语义流利性，特征产生和模拟生成。如果成功的话，该项目将提供一种新颖的理论范式，该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的法定任务，并被认为是值得通过基金会的智力优点和更广泛的影响来通过评估来支持的。