People
Matthew Lieberman
Lab Director
Dr. Lieberman is the director of the SCN lab and considered one of the founders of the field of ‘social neuroscience’. He received his PhD from Harvard in 1999 and has been doing neuroimaging since 1998. He is the bestselling author of Social: Why our brains are wired to connect. He has had many interests over the years, but all of his interests now coalesce around CEEing (see research). His work has received worldwide attention from places like the New York Times, HBO, BBC, Time Magazine, Rolling Stone, and Scientific American. Dr. Lieberman won the American Psychological Association’s Distinguished Scientific Award for Early Career Contribution to Psychology (2007), the Society for Experimental Social Psychology Career Trajectory Award (2015), and UCLA’s Distinguished Teaching Award (2020). Science magazine named him one of the “Top 50 Science Stars of Twitter”. He is co-founder and chief scientist of Resonance Inc. which uses artificial intelligence to help members of large communities (e.g. college students, companies, neighborhoods) find new deeper connections. Finally, he is very excited to finally be teaching a class on consciousness.
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Kevin Tan
Graduate student
Kevin is a PhD student and NSF research fellow advised by Matt Lieberman and Carolyn Parkinson. He is interested in characterizing the systems and computational properties of the social brain, particularly the neurocognitive pathways within the default network. Kevin probes the social brain using intracranial electrophysiology, fMRI and EEG, for which he develops novel techniques in machine learning, statistics, and signal processing. Kevin is the lead developer of electroCUDA, a GPU-based preprocessing and analysis package for neural field potential recordings.
Ashley Binnquist
Graduate student
I’m primarily interested in understanding what type of shared neural patterns underlie productive communication between individuals in conflict. More specifically, my research focuses on communication between ideologically polarized individuals, known as cross-ideological communication. Due to the social nature of my questions I am especially interested in the use and development of functional near infrared spectroscopy (fNIRS) for gathering data in more realistic social settings. Currently, I am working on developing a machine learning algorithm to predict time-dependent brain states during cross-ideological communication. Some questions I hope to answer with my research include: What brain states, at the individual level, relate to more productive communication? Do shared (or coordinated) brain states between communication partners lead to less observed conflict? Does depth of bonding before a difficult communication lead to more empathy and perspective taking? My hope is that the insights from my research will help to encourage more communication between polarized individuals and improve productivity for when those conversations do occur.
Stephanie Dolbier
Graduate student
It is common for conversations about controversial topics to quickly devolve into an unproductive mess, leaving both sides feeling like the other side is stupid, immoral, or ignorant. As such, many people tend to avoid interactions with their ideological opponents, leaving little room for convincing, compromising, or even understanding. Stephanie is interested in finding ways to improve the quality of discourse around heated topics and encouraging people to have those difficult conversations - if only to better understand what goes through their opponents’ minds. Currently, her research is focused on developing interventions to help people become more open-minded, avoid cognitive biases, and stay level-headed when talking about emotionally charged political issues.
Leezet Matos
Graduate student
I am a fourth-year PhD student who is motivated to conduct research that can benefit marginalized communities by combating racism and anti-Blackness. To this end, my current work within the SCN lab is centered on two foundational questions: 1) what are the similarities/differences between the way people of different racial groups see, or neurocognitively make sense of, our racialized social world, and 2) what individual differences (and interventions) drive similarity in understanding of our racialized social world across racial groups? My program of research uses social neuroscience and computational linguistics to answer these questions, with the eventual goal of evaluating the effectiveness of two social psychological interventions in promoting reliable understanding of racially marginalized individuals by nonmarginalized individuals.
Christina Huber
Graduate Student
Christina is a third-year graduate student in the SCN Lab. She previously received her B.A. in Psychology from Stanford University, then worked at Dartmouth's Social Neuroscience Lab with Meghan Meyer as a lab manager. Leveraging social neuroscience tools like fMRI and fNIRS, Christina seeks to explore the brain bases of naive realism—how our immediate, effortless beliefs about the world come to feel like reality. Her projects aim to uncover strategies to shift beliefs about situations, narratives, and ideologies, in order to better inform open-mindedness building interventions.
Bear Goldstein
Graduate Student
Bear studies the neuroscience of teams. He is particularly interested in how teamwork allows us to not only achieve beyond our individual capabilities but also forge meaningful interpersonal connections. He uses mobile and motion-tolerant neuroimaging to explore the neural and behavioral responses associated with real-time team dynamics while maintaining naturalistic interaction. Through a social-neuroscientific approach to teamwork, Bear aims to shed light on the underpinnings of positive team dynamics and the ways in which we may improve team outcomes, including creativity, cohesiveness, and collective success.
Grace Miao
Graduate Student
Grace Qiyuan Miao is a Graduate Student at UCLA Communication Science, specializing in cognitive and computational communication. Grace’s research interests focus on communicative dynamics and meaningful connections in human society. Specifically, she uses interdisciplinary methodologies to unpack multimodal components in interactions — such as conversations, eye movements, body languages, and neurocognitive processes — to understand how people coordinate, collaborate, and connect. In her free time, she enjoys traveling, snowboarding, cooking, and tasting.
Louisa Lyu
Graduate Student
Louisa is a first-year graduate student in the SCN and the CSNL lab. Her research focuses on using the large language models to change the psychological lenses people use to see the world. Specifically, she develops chatbots and uses neuroimaging methods, such as functional near-infrared spectroscopy (fNIRS), to investigate whether certain cognitive perspectives can be cultivated or weakened. For example, her work explores whether individuals with a tendency toward paranoia can adopt a less paranoid outlook through interventions like interactions with perspective-shaping chatbots. Additionally, Louisa is interested in examining the potential of other intervention techniques, such as real-time neuromodulation, to produce changes in perspective.