Deep neural networks (DNNs) once showed increasing alignment with primate perception as they improved on vision benchmarks, raising hopes that advances in artificial intelligence (AI) would naturally yield better models of biological vision. However, we present accumulating evidence that this alignment is now plateauing – and in some cases worsening – as DNNs scale to human or even superhuman accuracy. This divergence between artificial and biological perception may reflect the acquisition of visual strategies distinct from those of primates, and these findings challenge the view that advances in AI will naturally translate to progress in neuroscience. We argue that vision science must chart its own course, developing algorithms grounded in biological visual systems rather than optimizing for internet data.

Vision–language deep neural network models better explain human visual cortex activity than vision-only models. Damaging brain connections between visual and language areas reverses this pattern, suggesting that human visual perception is modulated by language.

Health sciences

A mode of brain organization that connects visual and bodily reference frames may translate raw sensory impressions into more abstract formats that are useful for action, social cognition and semantic processing.

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The selective revision of beliefs in light of new evidence has been considered one of the hallmarks of human-level rationality. However, tests of this ability in other species are lacking. We examined...

Tree shrews show a primate-like hierarchical organization in their visual pathway and object decoding accuracy, along with strongly face-selective cells, demonstrating how core computational principle...

Predictions of future events have a major impact on how we process sensory signals. However, it remains unclear how the brain keeps predictions online in anticipation of future inputs. Here, we combin...

Millions of students arriving at campuses are now using artificial intelligence. Worries abound.

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