Tomer D. Ullman

About

I am the Morris Kahn Associate Professor of Psychology in the Department of Psychology at Harvard University. I head the Computation, Cognition, and Development lab, with a focus on intuitive theories and people's common-sense reasoning about physics and psychology.

Research topics

• Artificial Intelligence
• Computer Science
• Cognitive science
• Epistemology
• Psychology
• Machine Learning
• Cognitive psychology
• Social psychology
• Data science

Selected publications

• Bayesian Models of Conceptual Development: Learning as Building Models of the World

  Annual Review of Developmental Psychology · 2020 · 84 citations

  1st authorCorresponding

  A Bayesian framework helps address, in computational terms, what knowledge children start with and how they construct and adapt models of the world during childhood. Within this framework, inference over hierarchies of probabilistic generative programs in particular offers a normative and descriptive account of children's model building. We consider two classic settings in which cognitive development has been framed as model building: ( a) core knowledge in infancy and ( b) the child as scientis…

  DOI

• Large Language Models Fail on Trivial Alterations to Theory-of-Mind Tasks

  arXiv (Cornell University) · 2023 · 79 citations

  1st authorCorresponding

  Intuitive psychology is a pillar of common-sense reasoning. The replication of this reasoning in machine intelligence is an important stepping-stone on the way to human-like artificial intelligence. Several recent tasks and benchmarks for examining this reasoning in Large-Large Models have focused in particular on belief attribution in Theory-of-Mind tasks. These tasks have shown both successes and failures. We consider in particular a recent purported success case, and show that small variation…

  DOI

• Physics versus graphics as an organizing dichotomy in cognition

  Trends in Cognitive Sciences · 2025-05-31 · 9 citations

  reviewSenior author
  PublisherDOI

• Agents of Chaos

  arXiv (Cornell University) · 2026-02-23 · 5 citations

  preprintOpen access

  We report an exploratory red-teaming study of autonomous language-model-powered agents deployed in a live laboratory environment with persistent memory, email accounts, Discord access, file systems, and shell execution. Over a two-week period, twenty AI researchers interacted with the agents under benign and adversarial conditions. Focusing on failures emerging from the integration of language models with autonomy, tool use, and multi-party communication, we document eleven representative case s…

  PublisherDOI

• The capacity limits of moving objects in the imagination

  Nature Communications · 2025-07-01 · 3 citations

  articleOpen accessSenior author

  People have capacity limits when tracking objects in direct perception. But how many objects can people track in their imagination? In nine pre-registered experiments (N = 313 total), we examine the capacity limits of mentally simulating the movement of objects in the mind's eye. In a novel Imagined Objects Tracking task, participants continue the motion of animated objects in their mind up to a pre-defined point. When tracking one object in the imagination (Experiment 1a), participants give est…

  PublisherOA PDFDOI

Frequent coauthors

• Joshua B. Tenenbaum

  Massachusetts Institute of Technology

  111 shared

• Tobias Gerstenberg

  40 shared

• Shari Liu

  Johns Hopkins University

  39 shared

• Elizabeth S. Spelke

  Harvard University

  34 shared

• Samuel J. Gershman

  Harvard University

  31 shared

• Josh Tenenbaum

  Massachusetts Institute of Technology

  25 shared

• David A. Lagnado

  21 shared

• Max Kleiman‐Weiner

  Seattle University

  21 shared

Labs

• CoCoDevPI

Similar researchers at Harvard University

• Randy L. Bucknerh-268
• Daniel L. Schacterh-181
• Matthew K. Nockh-126