codec lab

About

The Computation and Decision-Making Lab, led by Dr. Mark Ho, investigates the computational principles that underlie cognition, decision-making, and social interaction in humans and intelligent machines. We take an interdisciplinary approach, drawing on ideas and methods from cognitive science, social psychology, neuroscience, and artificial intelligence to answer questions such as:

How do people teach and communicate?
How do groups coordinate their thoughts, plans, and actions to achieve joint goals?
How do people plan and learn in complex, uncertain environments?
How do cognitive biases emerge from the avoidance of mental effort?
How can we align interactive machine learning systems with human cognition and values?

Representative Papers

For a complete list, see Papers.

People construct simplified mental representations to plan

Mark K. Ho, David Abel, Carlos G. Correa, Michael L. Littman, Jonathan D. Cohen, Thomas L. Griffiths

Nature (2022).

This paper proposes a theory of value-guided construals: simplified but useful representations that people construct when planning. Also see the Nature's Behind the Paper blog post and arXiv version.

Planning with Theory of Mind

Mark K. Ho, Rebecca Saxe, Fiery Cushman

Trends in Cognitive Sciences (2022).

This paper examines how planning processes shape Theory of Mind, the human capacity to reason about others' mental states.

Mentalizing and heuristics as distinct cognitive strategies in human teaching

Sevan K. Harootonian, Thomas L. Griffiths, Yael Niv, Mark K. Ho

Nature Human Behaviour (2026).

This paper examines how people trade off mental effort against effectiveness in social interactions such as teaching.

Cognitive science as a source of forward and inverse models of human decisions for robotics and control

Mark K. Ho, Thomas L. Griffiths

Annual Review of Control, Robotics, and Autonomous Systems (2022).

This paper provides an overview of computational cognitive science for artificial intelligence, control, and robotics.

People

Mark Ho (Principal Investigator)

My research examines the cognitive, motivational, and social processes that underpin human problem solving. I am especially interested in questions surrounding the structure and interpretation of intentional action, such as: How do people organize their thoughts and actions when pursuing individual or shared goals? How do people interpret and influence the intentional behavior of others using theory of mind? How do complex social phenomena emerge from the interplay of individual intentions? I am also interested in how approaches from computational cognitive science can inform the development of intelligent machines that interact with people.
CV - Personal webpage - Bluesky - Twitter - Google Scholar - GitHub

Marianna Zhang (Postdoc)

I'm a developmental cognitive scientist studying how children learn about social categories. How do children form beliefs about social groups? How do children think about social structures? I'm currently a postdoctoral fellow in psychology advised by Marjorie Rhodes. I received my PhD in psychology from Stanford University, advised by Ellen Markman, and my BA in psychology with a minor in philosophy from the University of Chicago.
Personal webpage

Sounak Banerjee (Postdoc)

My research interests lie in human behavior in complex tasks, specifically where humans need to process large amounts of information in limited time, to make decisions. I primarily rely on statistical and computational modeling to identify how individuals filter and integrate complex information and adapt to dynamic task contexts. I am particularly focused on skill acquisition and expert strategies for optimal behavior in complex paradigms. I completed my PhD in Cognitive Science from Rensselaer Polytechnic Institute under my advisors Dr. Wayne Gray and Dr. Chris Sims, studying team coordination in complex tasks. As postdoctoral associate at the Computation and Decision-Making Lab, I will be conducting research on optimal strategies for teaching people how to drive.
Personal webpage

Sev Harootonian (Postdoc)

Hello! I'm Sevan Harootonian, but I go by Sev. I'm interested in studying mentorship and understanding the cognitive mechanisms that contribute to its success. One project I've been working on examines how people infer others' knowledge to determine the best way to teach them. I use Reinforcement Learning and Bayesian models to figure out what mental strategies people apply in teaching situations. Teaching is just one aspect of mentorship, and I'm also interested in other important factors, such as inspiration and motivation.
Personal webpage

Wasita Mahaphanit (Postdoc)

My research focuses on how people infer and evaluate what is shared between minds through conversation, and how the depth of shared understanding shapes feelings of social connection. As a postdoctoral researcher jointly advised by Mark Ho and Shawn Rhoads (Mt. Sinai), I am extending this work to how people construct and act on models of other minds during joint planning and coordination, and what happens when this process breaks down. I use custom multiplayer web experiments, natural language processing, and computational cognitive modeling to achieve my research aims. I received my PhD in Cognitive Neuroscience from Dartmouth College, co-advised by Luke Chang and Robert Hawkins, and my B.S. from Brown University, where I was a lab manager in Michael J. Frank’s lab.
Personal webpage

Maya Malaviya (Ph.D. Student)

In my PhD, I am excited to explore decision-theoretic and program synthesis models of human cognition and behavior. For instance, how do we represent the tasks we want to accomplish, and when do different representations elicit different attitudes and behaviors? How do we decide what representations to prioritize and communicate? In the past, I have worked as a lab manager in the Computational Cognitive Science & Concepts and Cognition Labs at Princeton, earned my B.A. in Cognitive Science from UC Berkeley, and worked as a computer science educator at the Lawrence Hall of Science.
Personal webpage

Bonnie Yang (Ph.D. Student)

I am interested in how people create abstract representations during planning and problem-solving, particularly in the social domain. Before NYU, I received my B.A.s in Mathematics and Cognitive Science at Barnard College.

Miguel Aaron Torres Adame (Ph.D. Student)

I am interested in how humans and artificial agents learn, explore, and make decisions under uncertainty. My work combines psychology, artificial intelligence, and computational modeling to study how internal representations are formed, updated, and used to guide behavior, especially in active learning contexts. Previously, I worked on projects involving autonomous navigation, active concept learning, contextual learning, and risk modeling at the Laboratory 25 of Adaptive Behavior at the National Autonomous University of Mexico (UNAM), where I received my B.S. in Psychology.

Junxi Chen (Ph.D. Student)

I’m interested in understanding the computational mechanisms underlying human meta-learning and planning through reinforcement learning and Bayesian models. Previously, I worked as a postgraduate associate at Yale, advised by Robb Rutledge. I received my B.S. in Computational Neuroscience and Mathematics from the University of Southern California, where I was advised by Payam Piray.

Jing Li (Graduate Student Collaborator)

I am a Ph.D. candidate at the Icahn School of Medicine at Mount Sinai, advised by Angela Radulescu. Motivated by a broad interest in how self-efficacy beliefs arise, update, and steer behavior, I formalize self-efficacy within reinforcement-learning frameworks and test these models in both computational agents and human participants. My work bridges reinforcement learning and computational psychiatry to understand how fluctuations in self-efficacy shape decision making and contribute to manic risk in bipolar disorder. I build self-efficacy models in RL, design behavioral tasks, and ultimately aim to translate these insights into digital diagnostics and adaptive interventions for mood disorders.

Ryan Fayyazi (Graduate Student Collaborator)

Ryan is a PhD student in the Cognition & Perception program, advised by Cate Hartley. At the Ho Lab, Ryan is working on goal-directed exploration, task decomposition, and abstraction for reinforcement learning. Broadly, Ryan is interested in modeling the cognitive algorithms underlying open-ended autotelic decision-making in animals, and building similarly capable artificial agents.

Divya Srinivasan (Lab Manager)

Hi! I’m Divya, graduate student majoring in Computer Science at NYU. I’m interested in language and decision making in humans and machines. I’m fascinated by how humans update their beliefs by combining prior knowledge with new information, and the condition under which this breaks down. I am also interested in learner modeling and diagnostic inference, particularly how observers reason about the underlying cognitive processes that produced a given behavior. Outside the lab, I love crocheting and hiking!

Collaborators, alumni, friends of the lab

Papers

Download all (.bib)

Mentalizing and heuristics as distinct cognitive strategies in human teaching

Sevan Harootonian, Thomas L. Griffiths, Yael Niv, Mark K. Ho

Nature Human Behaviour (2026)

DOI Preprint

Cite

@article{harootonian2026mentalizing,
  title = {Mentalizing and Heuristics as Distinct Cognitive Strategies in Human Teaching},
  author = {Harootonian, Sevan and Griffiths, Thomas L. and Niv, Yael and Ho, Mark K.},
  year = 2026,
  month = aug,
  journal = {Nature Human Behaviour},
  doi = {10.1038/s41562-026-02540-2}
}

The syntax and semantics of goals

David Abel, Mark K. Ho

Topics in Cognitive Science (2026)

DOI arXiv

Cite

@article{abel2026syntax,
  title = {The Syntax and Semantics of Goals},
  author = {Abel, David and Ho, Mark K.},
  year = 2026,
  journal = {Topics in Cognitive Science},
  pages = {e70082},
  doi = {10.1111/tops.70082}
}

Aha! moments correspond to metacognitive prediction errors

Rachit Dubey, Mark K. Ho, Hermish Mehta, Tom Griffiths

Cognition (2026)

DOI PsyArXiv

Cite

@article{dubey2026aha,
  title = {Aha! Moments Correspond to Metacognitive Prediction Errors},
  author = {Dubey, Rachit and Ho, Mark K. and Mehta, Hermish and Griffiths, Tom},
  year = 2026,
  month = sep,
  journal = {Cognition},
  volume = {274},
  pages = {106537},
  doi = {10.1016/j.cognition.2026.106537}
}

Heuristics for meta-planning from a normative model of information search

Ionatan Kuperwajs, Mark K. Ho, Wei Ji Ma

(under review)

Cite

@unpublished{kuperwajs0000heuristics,
  title = {Heuristics for Meta-Planning from a Normative Model of Information Search},
  author = {Kuperwajs, Ionatan and Ho, Mark K. and Ma, Wei Ji},
  year = {0000},
  note = {Under review}
}

Let's be friends! People work together even when there is no incentive to do so

Victor Btesh, Mark K. Ho

Proceedings of the 48th Annual Conference of the Cognitive Science Society (2026)

About

Representative Papers

People

Mark Ho (Principal Investigator)

Marianna Zhang (Postdoc)

Sounak Banerjee (Postdoc)

Sev Harootonian (Postdoc)

Wasita Mahaphanit (Postdoc)

Maya Malaviya (Ph.D. Student)

Bonnie Yang (Ph.D. Student)

Miguel Aaron Torres Adame (Ph.D. Student)

Junxi Chen (Ph.D. Student)

Jing Li (Graduate Student Collaborator)

Ryan Fayyazi (Graduate Student Collaborator)

Divya Srinivasan (Lab Manager)

Collaborators, alumni, friends of the lab

Papers

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