Large language models (LLMs) have come closest among all models to date to mastering
human language, yet opinions about their linguistic and cognitive capabilities remain split …

Deep learning (DL) is being successfully applied across multiple domains, yet these models
learn in a most artificial way: they require large quantities of labeled data to grasp even …

The neuroscience of perception has recently been revolutionized with an integrative
modeling approach in which computation, brain function, and behavior are linked across …

The rise and success of large language models undermines virtually every strong claim for
the innateness of language that has been proposed by generative linguistics. Modern …

Deep neural networks (DNNs) have had extraordinary successes in classifying
photographic images of objects and are often described as the best models of biological …

During real-time language comprehension, our minds rapidly decode complex meanings
from sequences of words. The difficulty of doing so is known to be related to words' …

The many successes of deep neural networks (DNNs) over the past decade have largely
been driven by computational scale rather than insights from biological intelligence. Here …

Deep neural network models of sensory systems are often proposed to learn
representational transformations with invariances like those in the brain. To reveal these …

Functional correspondences between deep convolutional neural networks (DCNNs) and the
mammalian visual system support a hierarchical account in which successive stages of …

In various cultures and at all spatial scales, humans produce a rich complexity of geometric
shapes such as lines, circles or spirals. Here, we propose that humans possess a language …