Artificial neural networks (ANNs) inspired by biology are beginning to be widely used to
model behavioural and neural data, an approach we call 'neuroconnectionism'. ANNs have …

A rich behavioral literature has shown that human object recognition is supported by a
representation of shape that is tolerant to variations in an object's appearance …

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

In neural decoding research, one of the most intriguing topics is the reconstruction of
perceived natural images based on fMRI signals. Previous studies have succeeded in re …

Transformer models such as GPT generate human-like language and are predictive of
human brain responses to language. Here, using functional-MRI-measured brain responses …

Objects are the core meaningful elements in our visual environment. Classic theories of
object vision focus upon object recognition and are elegant and simple. Some of their …

Prior work has identified cortical regions selectively responsive to specific categories of
visual stimuli. However, this hypothesis-driven work cannot reveal how prominent these …

Models that predict brain responses to stimuli provide one measure of understanding of a
sensory system and have many potential applications in science and engineering. Deep …

The human ventral visual stream has a highly systematic organization of object information,
but the causal pressures driving these topographic motifs are highly debated. Here, we use …

Abstract Language neuroscience currently relies on two major experimental paradigms:
controlled experiments using carefully hand-designed stimuli, and natural stimulus …