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 …
S Piantadosi - Lingbuzz Preprint, lingbuzz, 2023 - lingbuzz.net
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 …
The neuroscience of perception has recently been revolutionized with an integrative modeling approach in which computation, brain function, and behavior are linked across …
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 …
NJ Sexton, BC Love - Science advances, 2022 - science.org
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 …