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 …

Highlights•Trade-off between sampling individual variation versus experimental
variation.•Different studies have allocated resources differently.•We argue that wide …

Extensive sampling of neural activity during rich cognitive phenomena is critical for robust
understanding of brain function. Here we present the Natural Scenes Dataset (NSD), in …

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 …

Cortical regions apparently selective to faces, places, and bodies have provided important
evidence for domain-specific theories of human cognition, development, and evolution. But …

The human brain achieves visual object recognition through multiple stages of linear and
nonlinear transformations operating at a millisecond scale. To predict and explain these …

Deep neural networks (DNNs) optimized for visual tasks learn representations that align
layer depth with the hierarchy of visual areas in the primate brain. One interpretation of this …

Deep neural networks (DNNs) trained on object recognition provide the best current models
of high-level visual cortex. What remains unclear is how strongly experimental choices, such …

Understanding neurocognitive computations will require not just localizing cognitive
information distributed throughout the brain but also determining how that information got …

Extensive sampling of neural activity during rich cognitive phenomena is critical for robust
understanding of brain function. We present the Natural Scenes Dataset (NSD), in which …