We are interested in social behavior, and in particular, strategic decision-making in humans. We seek to understand the mechanisms by which the brain anticipates actions or consequences of others with value-based goals to produce choices within interpersonal interactions.


To this end, we combine game-theoretic models of behavior with neuroscience techniques to examine functional circuits that translate external information presented in games (e.g. payoffs, rules, and feedback) into internal cognitive states (e.g. beliefs, valuation, and attention) and external choice behaviors (e.g. competition, cooperation, coordination, communication, etc.). This approach helps us to draw formal inferences about the underlying choice processes that span multiple levels of analysis (e.g. behavior, cognition, brain, and genetics), and provide objective metrics with potential diagnostic and assessment utility.  


Our research can be divided into several research foci, each of which explores the neurobehavioral substrates of decision-making under specific motivational or interpersonal context. 




Social and Strategic Learning

Decision-making in the presence of other competitive intelligent agents is fundamental for social behavior. Such decisions require agents to behave strategically, where in addition to learning about the rewards and punishments available in the environment, they also need to anticipate and respond to actions or consequences of others competing for the same rewards. Whereas strategic learning has been extensively investigated at both behavioral and algorithmic levels in the fields of game theory and artificial intelligence, neurocognitive mechanisms underlying strategic behavior remain to be explored. 

Research in our lab aims at developing mechanistic understanding about brain regions that facilitate such decision-making in either simple interpersonal settings or complex social environments where the brain uses simple heuristics to guide behavior. For example,  based on an economic game called “patent race”, in which individuals compete for the same monetary reward during repeated interactions, we identified separable learning signals encoded in partially overlapping but distinct brain regions. Prediction errors arising from trial and error for learning the available rewards and punishments

(reinforcement learning) are processed in the ventral striatum; whereas error signals arising from predicting, interpreting, and responding to actions of social opponents (belief learning) are encoded in both the ventral striatum and rostral anterior cingulate. These results suggest that decisions made within competitive environments are guided by inputs from parallel neural processes––one that is common to adaptive behaviors across a wide range of non-social settings and one that is specific to social interactions. We further tested whether these neural regions were necessary for strategic learning by examining the performance of patients with focal lesions in the regions identified in our fMRI study. We found preserved capacity to learn in economic games following the basal ganglia damage, which suggests a model where higher-order learning processes are dissociable from trial-and-error learning and can be preserved despite basal ganglia damage. Ongoing research projects in our lab aim at exploring the interplay between strategic learning and social structures. 

Representative papers:

Patients with basal ganglia damage show preserved learning in an economic game

L Zhu*, Y Jiang, D Scabini, RT. Knight, M Hsu*, Nature Communications. 2019.  

Dissociable neural representations of reinforcement and belief prediction errors underlie strategic learning

L Zhu, K Mathewson,  M Hsu*. Proceedings of the National Academy of Sciences, 2012.



Communication as Goal-Directed Decision-Making

Social communication is pervasive in nature, ranging from basic animal behaviors such as birds singing for courtship, to complicated human actions such as lying, begging, and apologizing. To date, many neuroscience studies on communication have focused on the cognitive and neural functions involved in the low-level processing of communicative signals such as narratives and facial expressions, whereas behavioral studies in ethology and economics are rooted in a very different tradition. These studies often seek to understand how organisms survive and thrive by sharing and responding to social information concerning the opportunities and challenges encountered in the daily environment. While these studies have produced rich theoretical and empirical findings at the behavioral level, the characterization of their neural underpinnings has been remarkably unexplored.


We seek to understand neurocomputational mechanisms of communicative behavior, taking a unique perspective by investigating inferences and choices in intentional information transmission as goal-directed, strategic decision-making, and by drawing from recent developments in decision neuroscience, computational pragmatics, and the large body of work in signaling games.  Collaborating with developmental psychologists, we also seek to leverage our findings to further elucidate when and how humans acquire the ability to interpret communicative signals and what neural developments underlie this process.

Representative papers:

Reading between the lines: Listener’s vmPFC simulates speaker cooperative choices in communication games

Q Mi, C Wang, C F. Camerer, L Zhu*. Science Advances. 2021

Damage to dorsolateral prefrontal cortex affects tradeoffs between honesty and self-interest

L Zhu, AC  Jenkins, E Set, D Scabini, R Knight, P Chiu, B King-Casas, and Ming Hsu*. Nature Neuroscience, 2014.


If you’re excited about working at the interface of social and biological sciences –– studying human nature using neuroscience, game theory, and computational modeling, this is the lab you need to be in! We are currently looking for talented postdocs and graduate students with training in computer science, mathematics, physics, economics, psychology, neuroscience, or appropriate engineering disciplines.  


  • PostdocWe are interested in candidates with a background in cognitive neuroscience, behavioral economics, machine learning, linguistics, or related fields. Expertise in natural language processing, EEG/MEG, TMS, eye tracking, and computational modeling is particularly welcome. The appointment typically lasts for two years and can be extended contingent upon research performance. Salary is highly competitive and will be commensurate with experience and qualification. 


  • Ph.D. Student: Students interested in our Ph.D. positions are strongly recommended to apply for a short-term research intern in our lab first (e.g. summer intern). Qualified applicants will have the opportunity of working with our graduate students and getting to know the lab better. The length of the intern may vary depending on individual cases.   


  • Undergraduate Student: We are happy to offer a research assistantship for talented undergraduate students who are interested in our work and hope to enhance the learning experience through engaging in academic research. Positions are open throughout the year. Students from all majors are welcome to apply. 



© 2015 Lusha Zhu Lab

School of Psychological and Cognitive Sciences, PKU-Tsinghua Center for Life SciencesIDG/McGovern Institute for Brain Research

Peking University

Address: 52 Haidian Rd, Wang Kezhen Bldg,  Room 1104, Haidian, Beijing, China,  100871




北京大学 心理与认知科学学院北大-清华生命科学联合中心 IDG/麦戈文脑科学研究所

地址:北京市海淀区海淀路52号王克桢楼1104, 100871