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 …

Understanding object representations requires a broad, comprehensive sampling of the
objects in our visual world with dense measurements of brain activity and behavior. Here …

Cognitive scientists often infer multidimensional representations from data. Whether the data
involve text, neuroimaging, neural networks, or human judgments, researchers frequently …

Deep neural networks have reached human-level performance on many computer vision
tasks. However, the objectives used to train these networks enforce only that similar images …

To study visual and semantic object representations, the need for well-curated object
concepts and images has grown significantly over the past years. To address this, we have …

Robustness has become an important consideration in deep learning. With the help of
explainable AI, mismatches between an explained model's decision strategy and the user's …

In areas of machine learning such as cognitive modeling or recommendation, user feedback
is usually context-dependent. For instance, a website might provide a user with a set of …

Interest in understanding and factorizing learned embedding spaces through conceptual
explanations is steadily growing. When no human concept labels are available, concept …

A large portion of human knowledge comprises “abstract” concepts that lack readily
perceivable properties (eg,“love” and “justice”). Since abstract concepts lack such …

A central question for cognitive science is to understand how humans process visual
objects, ie, to uncover human low-dimensional concept representation space from high …