Semantic memory guides and is shaped by value-based decisions

This award was provided as part of NSF's Social, Behavioral and Economic Sciences (SBE) Postdoctoral Research Fellowships (SPRF) program and SBE's Cognitive Neuroscience 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 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. Daphna Shohamy at Columbia University, this postdoctoral fellowship award supports an early career scientist examining how semantic memory guides and is shaped by value-based decisions. Through everyday experiences, people learn facts about the world (e.g., learning about what fruits are ripe) and then use this knowledge to guide decisions (e.g., choosing ripe fruits to eat). This proposal aims to advance our understanding of how such world knowledge guides decisions and the neural mechanisms involved. The results will have important implications for education, which would benefit from a focus not just on teaching facts through single exposure but on acquiring abstract knowledge across multiple exposures of varying content. This abstract knowledge is especially useful since it can be generalized to novel situations outside the classroom.

It may seem obvious that decisions are guided by semantic memory, but, remarkably, there has been no empirical work addressing the question of how this happens. The proposed two experiments will start to fill this gap by integrating behavior, computational modeling, and functional imaging (fMRI) to investigate the neural mechanisms by which semantic memory and value-based decisions interact to produce adaptive behavior. The first study tests the hypothesis that the value of choices depends on abstract knowledge accrued across multiple experiences with a semantic category. Participants will learn the average value across multiple items of a category to test if this average value is used to determine individual item value and guide decisions. The second study tests the hypothesis that the defining features of a semantic category are learned by making value-based decisions across multiple instances of the category. Participants will associate higher values to stimuli with features by doing a combined categorization and value learning task to test if feature value influences categorization. We hypothesize that semantic and value information are represented in distinct brain regions within the anterior temporal lobe (ATL) and ventral medial pre-frontal cortex (vmPFC), respectively. Category learning models will be used to quantify trial-by-trial variation in semantic information while reinforcement learning models will be used to quantify trial-by-trial variation in value information. This project predicts that semantic information from the ATL serves as input to value computation in the vmPFC while value information from the vmPFC shapes features of semantic representation in the ATL.

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.