Humans and machines rarely have access to explicit external feedback or supervision, yet
manage to learn. Most modern machine learning systems succeed because they benefit …

Biological and artificial information processing systems form representations that they can
use to categorize, reason, plan, navigate, and make decisions. How can we measure the …

Should we care whether AI systems have representations of the world that are similar to
those of humans? We provide an information-theoretic analysis that suggests that there …

Daily experiences often involve the processing of multiple sequences, yet storing them
challenges the limited capacity of working memory (WM). To achieve efficient memory …

Whether supervised or unsupervised, human and machine learning is usually characterized
as event-based. However, learning may also proceed by systems alignment in which …

Humans have the remarkable ability to learn new categories from few examples, but how
few examples can we actually learn from? Recent studies suggest it may be possible to …

Aligning large language models (LLMs) behaviour with human intent is critical for future AI.
An important yet often overlooked aspect of this alignment is the perceptual alignment …

Measuring the human alignment of trained models is gaining traction because it is not clear
to which extent artificial image representations are proper models of the visual brain …

Humans can readily generalize their learning to new visual concepts, and infer their
associated meanings. How do people align the different conceptual systems learned from …

Humans can readily generalize their learning to new visual concepts, and infer their
associated meanings. How do people align the different conceptual systems learned from …