The remarkable successes of convolutional neural networks (CNNs) in modern computer
vision are by now well known, and they are increasingly being explored as computational …

Primates, including humans, can typically recognize objects in visual images at a glance
despite naturally occurring identity-preserving image transformations (eg, changes in …

Under the guidance of a formal exemplar model of categorization, we conduct comparisons
of natural-science classification learning across four conditions in which the nature of the …

What might sound like the beginning of a joke has become an attractive prospect for many
cognitive scientists: the use of deep neural network models (DNNs) as models of human …

Many successful formal models of human categorization have been developed, but these
models have been tested almost exclusively using artificial categories, because deriving …

Humans outperform object recognizers despite the fact that models perform well on current
datasets, including those explicitly designed to challenge machines with debiased images …

In this paper, we introduce an AI-mediated framework that can provide intelligent feedback
to augment human cognition. Specifically, we leverage deep reinforcement learning (DRL) …

Perceptual judgment of image similarity by humans relies on rich internal representations
ranging from low-level features to high-level concepts, scene properties and even cultural …

A core problem in visual object learning is using a finite number of images of a new object to
accurately identify that object in future, novel images. One longstanding, conceptual …

Although deep neural networks (DNNs) are state-of-the-art artificial intelligence systems, it is
unclear what insights, if any, they provide about human intelligence. We address this issue …