Research Projects


How do cognitive processes shape collective behavior in complex social-ecological systems?


Social-ecological systems are systems in which agents interact with each other and their environment. In such systems, the environment shapes the agents' experience and actions, and in turn collective action of agents changes social and physical aspects of the environment. We are interested in the way cognitive abilities interact with environmental affordances, to shape collective behaviors such as social norms, social structure, institutions and culture.


Current research in the lab follows one of the main topics below. However, we also collaborate on many other topics and projects, such as information sharing, advice giving, decision making and moral psychology. Check out the publication page for more information.


Social Norms

Social norms prescribe behaviors that most group members follow, most of the time. Some behaviors are easier to track and detect than others, which has implications for norm emergence and resilience. For example, active behaviors are easier to detect than omissions. We use multiplayer video games and computational modeling to study these processes, and the way environmental affordances shape the emergence of norms.

Relevant papers:

  • Hertz, U. (2025). Increased complexity of social category markers leads to diverse rule-based categorizations and reduced intergroup bias. Proceedings of the Royal Society B, 292(2051), 20250659. [Read]
  • Hertz, U., Köster, R., Janssen, M. A., & Leibo, J. Z. (2025). Beyond the matrix: Experimental approaches to studying cognitive agents in social-ecological systems. Cognition, 254, 105993. [Read] [Accepted version]
  • Hertz, U. (2024). A cognitive approach to learning, monitoring, and shifting social norms. Current Opinion in Psychology, 101917. [Read] [Accepted version]
  • Nafcha, O., & Hertz, U. (2024). Asymmetric cognitive learning mechanisms underlying the persistence of intergroup bias. Communications Psychology, 2(1), 1–12. [Read]
  • Hertz, U. (2021). Learning how to behave: cognitive learning processes account for asymmetries in adaptation to social norms. Proceedings of the Royal Society B. (Featured in PNAS Journal Club, June 18th 2021) [Read]

Human-AI interaction

As AI agents become more prevalent, they become a part of our social landscape, giving advice, sharing information and demonstrating normative behaviors. We examine how humans' social expectations, shaped by interacting with humans, extend to AI agents, and how this shapes cooperation and interactions between humans and AI agents. We are also interested in the way we could design AI systems to facilitate cooperation and interactions with humans.

Relevant papers:

  • Gazit, L., Arazy, O., & Hertz, U. (2025). Whose agent are you? Relational norms shape expectation from algorithmic and human advisors in social decisions. Computers in Human Behavior: Artificial Humans, 100218. [Read]
  • Gazit, L., Arazy, O., & Hertz, U. (2023). Choosing between human and algorithmic advisors: The role of responsibility sharing. Computers in Human Behavior: Artificial Humans, 100009. [Read] [Preprint]

Cognitive Archeology

Cognitive evolution is conceptualized as comprising the asynchronous development of distinct cognitive systems, highlighting the importance of reconstructing the evolutionary history of each of these components. We aim to characterize this evolutionary process by examining the archeological evidence. We study the way habitation and movement style, such as cave dwelling, shaped social cognition and social structure, and how complex problem-solving abilities are reflected in complex stone tools. We use lab experiments, computational simulations and archeological measurements to characterize these processes.

Relevant papers: soon...


Computational Psychiatry

In recent years, lab-based experimental tasks designed to track specific cognitive abilities, and computational cognitive models that aim to capture individual behavior, have been used to provide a mechanistic understanding of a variety of psychiatric disorders and of individual differences in subclinical populations. We design experimental tasks and fit computational models, mostly in the field of reinforcement learning, and collaborate with researchers to examine how they capture different aspects of disorders. We have used our tasks to examine aspects of social anxiety, learning disabilities, schizophrenia and depression.

Relevant papers:

  • Gabay, Y., Jacob, L., Mansour, A., & Hertz, U. (2025). Computational markers show specific deficits for dyslexia and ADHD in complex learning settings. NPJ Science of Learning, 10(1), 1–10. [Read]
  • Bar-Sella, A., Sayda, D., Mansour, M., Nof, A., Hertz, U., & Zilcha-Mano, S. (2024). Changing attachment orientation: Uncovering the role of shifting the emotion regulation tendency. Journal of Counseling Psychology, 71(5), 402–414. [Read]
  • Pereg, M., Hertz, U., Ben-Artzi, I., & Shahar, N. (2024). Disentangling the contribution of individual and social learning processes in human advice-taking behavior. NPJ Science of Learning, 9(1), 4. [Read]
  • Heimer, O., & Hertz, U. (2024). The spread of affective and semantic valence representations across states. Cognition, 244, 105714. [Read] [PDF]
  • Zaatri, S., Aderka, I. M., & Hertz, U. (2022). Blend in or stand out: social anxiety levels shape information-sharing strategies. Proceedings of the Royal Society B, 289(1975), 20220476. [Read]
  • Agassi, O. D., Hertz, U., Shani, R., Derakshan, N., Wiener, A., & Okon-Singer, H. (2022). Using clustering algorithms to examine the association between working memory training trajectories and therapeutic outcomes among psychiatric and healthy populations. Psychological Research. [Read]
  • Jiryis, T., Magal, N., Fructher, E., Hertz, U., & Admon, R. (2022). Resting-state heart rate variability (HRV) mediates the association between perceived chronic stress and ambiguity avoidance. Scientific Reports, 12(1), 17645. [Read]