  Vision transformers (ViTs) are top performing models on many computer vision
benchmarks and can accurately predict human behavior on object recognition
tasks. However, researchers question the value of using ViTs as models of
biological learning because ViTs are thought to be more data hungry than
brains, with ViTs requiring more training data to reach similar levels of
performance. To test this assumption, we directly compared the learning
abilities of ViTs and animals, by performing parallel controlled rearing
experiments on ViTs and newborn chicks. We first raised chicks in impoverished
visual environments containing a single object, then simulated the training
data available in those environments by building virtual animal chambers in a
video game engine. We recorded the first-person images acquired by agents
moving through the virtual chambers and used those images to train self
supervised ViTs that leverage time as a teaching signal, akin to biological
visual systems. When ViTs were trained through the eyes of newborn chicks, the
ViTs solved the same view invariant object recognition tasks as the chicks.
Thus, ViTs were not more data hungry than newborn visual systems: both learned
view invariant object representations in impoverished visual environments. The
flexible and generic attention based learning mechanism in ViTs combined with
the embodied data streams available to newborn animals appears sufficient to
drive the development of animal-like object recognition.
