4 citations · 19 across the 24 of their papers we have counts for
25 papers
CogGym: Towards Large-Scale Comparative Evaluation of Human and Machine Cognition
Lance Ying, Jinzhou Wu, Yingshan Susan Wang +53
Understanding and modeling human intelligence are parallel goals shared by artificial intelligence (AI) and cognitive science. As AI systems grow increasingly capable, in what ways…
PlayTrain: An Efficient Reinforcement Learning Framework for LLM-Generated Adaptable JavaScript Games
Ryan Truong, Lance Ying, Samuel J. Gershman +1
While many video-game environments (VGEs) have played crucial roles in advancing reinforcement learning (RL), developing novel VGEs or modifying existing ones to support new featur…
Using Theory of Mind to Arbitrate between Social and Non-social Learning
Lance Ying, Ryan Truong, Joshua B. Tenenbaum +1
Social learning is a powerful mechanism through which agents learn about the world from others. However, humans sometimes choose direct experience over social learning, which can c…
Pragmatic Reasoning in Design
Lance Ying, William Van Uitert, Tan Zhi-Xuan +2
People can often understand and use novel artifacts after only a few interactions, suggesting that design choices communicate underlying affordances and causal structure. We propos…
Post-training makes large language models less human-like
Marcel Binz, Elif Akata, Abdullah Almaatouq +76
Large language models (LLMs) are increasingly used as surrogates for human participants, but it remains unclear which models best capture human behavior and why. To address this, w…
Grounding Social Perception in Intuitive Physics
Lance Ying, Aydan Y. Huang, Aviv Netanyahu +4
People infer rich social information from others' actions. These inferences are often constrained by the physical world: what agents can do, what obstacles permit, and how the phys…
AI Gamestore: Scalable, Open-Ended Evaluation of Machine General Intelligence with Human Games
Lance Ying, Ryan Truong, Prafull Sharma +9
Rigorously evaluating machine intelligence against the broad spectrum of human general intelligence has become increasingly important and challenging in this era of rapid technolog…
Evaluating Language Models' Evaluations of Games
Katherine M. Collins, Cedegao E. Zhang, Graham Todd +9
Reasoning is not just about solving problems -- it is also about evaluating which problems are worth solving at all. Evaluations of artificial intelligence (AI) systems primarily f…
ProToM: Promoting Prosocial Behaviour via Theory of Mind-Informed Feedback
Matteo Bortoletto, Yichao Zhou, Lance Ying +2
While humans are inherently social creatures, the challenge of identifying when and how to assist and collaborate with others - particularly when pursuing independent goals - can h…
Assessing Adaptive World Models in Machines with Novel Games
Lance Ying, Katherine M. Collins, Prafull Sharma +11
Human intelligence exhibits a remarkable capacity for rapid adaptation and effective problem-solving in novel and unfamiliar contexts. We argue that this profound adaptability is f…
Modeling Open-World Cognition as On-Demand Synthesis of Probabilistic Models
Lionel Wong, Katherine M. Collins, Lance Ying +8
When faced with novel situations, people are able to marshal relevant considerations from a wide range of background knowledge and put these to use in inferences and predictions. W…
Language-Informed Synthesis of Rational Agent Models for Grounded Theory-of-Mind Reasoning On-The-Fly
Lance Ying, Ryan Truong, Katherine M. Collins +6
Drawing real world social inferences usually requires taking into account information from multiple modalities. Language is a particularly powerful source of information in social…
What's in the Box? Reasoning about Unseen Objects from Multimodal Cues
Lance Ying, Daniel Xu, Alicia Zhang +3
People regularly make inferences about objects in the world that they cannot see by flexibly integrating information from multiple sources: auditory and visual cues, language, and…
Belief Attribution as Mental Explanation: The Role of Accuracy, Informativity, and Causality
Lance Ying, Almog Hillel, Ryan Truong +3
A key feature of human theory-of-mind is the ability to attribute beliefs to other agents as mentalistic explanations for their behavior. But given the wide variety of beliefs that…
On Benchmarking Human-Like Intelligence in Machines
Lance Ying, Katherine M. Collins, Lionel Wong +6
Recent advances in Artificial Intelligence (AI) have yielded powerful computational models that, by learning from vast amounts of human-generated data, are increasingly posited as…
Hypothesis-Driven Theory-of-Mind Reasoning for Large Language Models
Hyunwoo Kim, Melanie Sclar, Tan Zhi-Xuan +5
Existing LLM reasoning methods have shown impressive capabilities across various tasks, such as solving math and coding problems. However, applying these methods to scenarios witho…
Pragmatic Embodied Spoken Instruction Following in Human-Robot Collaboration with Theory of Mind
Lance Ying, Xinyi Li, Shivam Aarya +6
Spoken language instructions are ubiquitous in agent collaboration. However, in real-world human-robot collaboration, following human spoken instructions can be challenging due to…
Understanding Epistemic Language with a Language-augmented Bayesian Theory of Mind
Lance Ying, Tan Zhi-Xuan, Lionel Wong +2
How do people understand and evaluate claims about others' beliefs, even though these beliefs cannot be directly observed? In this paper, we introduce a cognitive model of epistemi…
A Bayesian Drift-Diffusion Model of Schachter-Singer's Two Factor Theory of Emotion
Lance Ying, Audrey Michal, Jun Zhang
Bayesian inference has been used in the past to model visual perception (Kersten, Mamassian, & Yuille, 2004), accounting for the Helmholtz principle of perception as "unconscious i…
GOMA: Proactive Embodied Cooperative Communication via Goal-Oriented Mental Alignment
Lance Ying, Kunal Jha, Shivam Aarya +3
Verbal communication plays a crucial role in human cooperation, particularly when the partners only have incomplete information about the task, environment, and each other's mental…
Pragmatic Instruction Following and Goal Assistance via Cooperative Language-Guided Inverse Planning
Tan Zhi-Xuan, Lance Ying, Vikash Mansinghka +1
People often give instructions whose meaning is ambiguous without further context, expecting that their actions or goals will disambiguate their intentions. How can we build assist…
Grounding Language about Belief in a Bayesian Theory-of-Mind
Lance Ying, Tan Zhi-Xuan, Lionel Wong +2
Despite the fact that beliefs are mental states that cannot be directly observed, humans talk about each others' beliefs on a regular basis, often using rich compositional language…
Inferring the Goals of Communicating Agents from Actions and Instructions
Lance Ying, Tan Zhi-Xuan, Vikash Mansinghka +1
When humans cooperate, they frequently coordinate their activity through both verbal communication and non-verbal actions, using this information to infer a shared goal and plan. H…
The Neuro-Symbolic Inverse Planning Engine (NIPE): Modeling Probabilistic Social Inferences from Linguistic Inputs
Lance Ying, Katherine M. Collins, Megan Wei +5
Human beings are social creatures. We routinely reason about other agents, and a crucial component of this social reasoning is inferring people's goals as we learn about their acti…