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

Ichien, Nicholas, Los Angeles CA

Investigators

Abstract

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.