S A Park

Seongmin A Park

I am a project scientist in the team of Erie Boorman at the center for mind and brain in the University of California, Davis. I received my Ph.D. in Culture Technology with Jeaeseung Jeong at KAIST. I was a postdoctoral researcher in the team of Jean-Claude Dreher at CNRS.

In my research, I strive to investigate how the human brain implements reinforcement learning (RL) and decision making (DM) while combining with concepts and tools proposed by animal models, behavioral economics, and machine learning. By incorporating behavioral experiments, neuroimaging (fMRI and EEG), and computational models, my research addresses how the brain transforms the complex experiences into low-dimensional representation of abstract knowledge and further promotes behavioral flexibility in ever-changing environments.

CV Google Scholar

Contact

seongmin.a.park [at] gmail.com

267 Cousteau Pl, Davis, CA 95618, USA

Research Interests

There have been long-standing interests in the brain’s ability to construct map-like representations that encode relationships between entities, concepts, events, and even abstract task states. The exciting findings on the hippocampus (HC) and entorhinal cortex (EC) support this idea of ‘cognitive maps’. These findings demonstrate that this system organizes not only spatial but also non-spatial relational information to guide goal-directed decision making. Having a cognitive map affords the brain the ability to infer unlearned relationships as if finding a novel route in a physical space, planning goal-directed decisions with sparse and delayed feedback, and generalizing previous experiences to make novel decisions that were never made before. This behavioral flexibility is a hallmark of human intelligence but remains a major challenge in artificial intelligence (AI).

The neural mechanisms for constructing cognitive maps have been primarily examined in a physical space or in continuous task dimensions where agents can readily infer their current state from continuous sensory feedbacks (e.g. visual, auditory, vestibular). On the other hand, little is known about how the brains construct a structural representation in abstract knowledge dimensions where agents often learn discrete relationships from separate experiences in the absence of explicit sensory feedback.

Considering that many everyday decisions require comparisons between discrete decision options in which we have learned their feature values in separate experiences (e.g. who to collaborate with, which team will be victorious, or who will be a better political leader), it is important to examine how the brain represents the relationships between abstract concepts or discrete entities and how subjective representation guides different decisions and judgments across individuals.

My research focuses on how the brain learns latent states in abstract task spaces, integrates separately sampled discrete relationships between entities into a coherent representation and uses the cognitive map to guide novel decisions.

Preprint

Goal-oriented predictive representations in the human hippocampus
J Crivelli-Decker, A Clarke, SA Park, DJ Huffman, ED Boorman, C Ranganath BiorXiv 10.1101/2021.08.18.456881

Do grid codes afford generalization and flexible decision-making?
LQ Yu *, SA Park *, SC Sweigart, ED Boorman ☨, MR Nassar ☨ arXiv :2106.16219

2022

Neural mechanisms of credit assignment for inferred relationships in a structured world
PP Witkowski, SA Park, ED Boorman Neuron , (110) 1–11

2021

Inferences on a Multidimensional Social Hierarchy Use a Grid-like Code
SA Park, DS Miller, ED Boorman Nature Neuroscience , 24 (9) 1292–1301 [Supplementary Info]

Protocol for building a cognitive map of structural knowledge in humans by integrating abstract relationships from separate experiences
SA Park, DS Miller, ED Boorman STAR Protocols , 2 (2), 100423

Complementary Structure-Learning Neural Networks for Relational Reasoning
J Russin, M Zolfaghar, SA Park, ED Boorman, RC O'Reilly Proc. Annu. Meet. Cogn. Sci. Soc. 2021

The orbital frontal cortex, task structure, and inference
ED Boorman, PP Witkowski, Y Zhang, SA Park Behavioral Neuroscience , 135 (2), 291

Cognitive maps and novel inferences: a flexibility hierarchy
ED Boorman, SC Sweigart, SA Park Current Opinion in Behavioral Sciences , 38, 141-149

2020

Map making: Constructing, combining, and inferring on abstract cognitive maps
SA Park, DS Miller, H Nili, C Ranganath, ED Boorman Neuron , 10.1016/j.neuron.2020.06.030 [Supplementary Info]

2019

Modeling Other Minds: Bayesian Inference Explains Human Choices in Group Decision Making
K Khalvati, SA Park, S Mirbagheri, R Philippe, M Sestito, JC Dreher, and RPN Rao Science Advances , 10.1126/sciadv.aax8783

Neural computations underlying strategic social decision-making in groups
SA Park, M Sestito, ED Boorman, JC Dreher Nature communications , 10.1038/s41467-019-12937-5

A Bayesian Theory of Conformity in Collective Decision Making
K Khalvati, S Mirbagheri, SA Park, JC Dreher, RPN Rao Advances in Neural Information Processing Systems (NIPS) , 9699-9708

2018

Wait and you shall see: sexual delay discounting in hypersexual Parkinson’s disease
R Girard, I Obeso, S Thobois, SA Park, T Vidal, E Favre, M Ulla, E Broussolle, P Krack, F Durif, JC Dreher Brain , 142 (1), 146-162

2017

Integration of individual and social information for decision-making in groups of different sizes
SA Park, S Goiame, DA O'Connor, JC Dreher Plos Biology , 15 (6), e2001958

2016

A probabilistic model of social decision making based on reward maximization
K Khalvati, SA Park, JC Dreher, RPN Rao Advances in Neural Information Processing Systems (NIPS) , 2901-2909

2015

Reappraising abstract paintings after exposure to background information
SA Park, K Yun, J Jeong Plos One , PloS one 10 (5), e0124159

2013

TV programs that denounce unfair advantage impact women’s sensitivity to defection in the public goods game
SA Park, S Jeong, J Jeong Social neuroscience , 8 (6), 568-582