Biological intelligence is remarkable in its ability to produce complex behaviour in many
diverse situations through data efficient, generalisable and transferable skill acquisition. It is …

In order to better understand how the brain perceives faces, it is important to know what
objective drives learning in the ventral visual stream. To answer this question, we model …

Recently, a confluence between trends in neuroscience and machine learning has brought
a renewed focus on unsupervised learning, where sensory processing systems learn to …

Vision transformers (ViTs) are top-performing models on many computer vision benchmarks
and can accurately predict human behavior on object recognition tasks. However …

Object recognition is the process by which humans organize the visual world into
meaningful perceptual units. In this Review, we examine the developmental origins and …

Unsupervised adaptation to the spatiotemporal statistics of visual experience is a key
computational principle that has long been assumed to govern postnatal development of …

Biological vision systems are unparalleled in their ability to learn visual representations
without supervision. In machine learning, self-supervised learning (SSL) has led to major …

Eleanor Gibson's groundbreaking research on perception, learning, and development
challenged the tenets of behaviorist and introspectionist psychology and extended the …

The development of sparse edge coding in the mammalian visual cortex depends on early
visual experience. In humans, there are multiple indicators that the statistics of early visual …

What are the core learning algorithms in brains? Nativists propose that intelligence emerges
from innate domain-specific knowledge systems, whereas empiricists propose that …