A central challenge for cognitive science is to explain how abstract concepts are acquired
from limited experience. This has often been framed in terms of a dichotomy between …

The recent advent of large language models has reinvigorated debate over whether human
cognitive capacities might emerge in such generic models given sufficient training data. Of …

Artificial intelligence and machine learning (AI/ML) research has aimed to achieve human-
level performance in tasks that require understanding and decision making. Although major …

Solving ill-posed inverse problems typically requires regularization based on prior
knowledge. To date, only prior knowledge that is formulated mathematically (eg, sparsity of …

We recently reported evidence that large language models are capable of solving a wide
range of text-based analogy problems in a zero-shot manner, indicating the presence of an …

Deep neural networks have made tremendous gains in emulating human-like intelligence,
and have been used increasingly as ways of understanding how the brain may solve the …

As generative artificial intelligence (AI) becomes more common in day-to-day life, AI-
generated content (AIGC) needs to be accurate, relevant, and comprehensive. These …

People often imagine everyday objects are something else. A turned over bottle becomes a
car, a teapot becomes a swan. Such pretense is common in play, pedagogy, and narratives …

Advances in computer vision research enable human-like high-dimensional perceptual
induction over analogical visual reasoning problems, such as Raven's Progressive Matrices …

Much of human learning and inference can be framed within the computational problem of
relational generalization. In this project, we propose a Bayesian model that generalizes …