Artificial deep neural networks (DNNs) initially inspired by the brain enable computers to
solve cognitive tasks at which humans excel. In the absence of explanations for such …

To learn how cognition is implemented in the brain, we must build computational models
that can perform cognitive tasks, and test such models with brain and behavioral …

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 …

Mechanistic insight is achieved only when experiments are employed to test formal or
computational models. Furthermore, in analogy to lesion studies, phantom perception may …

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

Could a machine think? Could the mind itself be a thinking machine? The computer
revolution transformed discussion of these questions, offering our best prospects yet for …

It has long been argued that only humans could produce and understand language. But
now, for the first time, artificial language models (LMs) achieve this feat. Here we survey the …

Schank (1980) wrote an editorial for Intelligence on “How much intelligence is there in
artificial intelligence?”. In this paper, we revisit this question. We start with a short overview …

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 …

The idea of combining artificial intelligence, in particular deep neural networks, and
computational modeling with neuroscience and cognitive sciences has recently gained …