  Numerosity perception is foundational to mathematical learning, but its
computational bases are strongly debated. Some investigators argue that humans
are endowed with a specialized system supporting numerical representation;
others argue that visual numerosity is estimated using continuous magnitudes,
such as density or area, which usually co-vary with number. Here we reconcile
these contrasting perspectives by testing deep networks on the same numerosity
comparison task that was administered to humans, using a stimulus space that
allows to measure the contribution of non-numerical features. Our model
accurately simulated the psychophysics of numerosity perception and the
associated developmental changes: discrimination was driven by numerosity
information, but non-numerical features had a significant impact, especially
early during development. Representational similarity analysis further
highlighted that both numerosity and continuous magnitudes were spontaneously
encoded even when no task had to be carried out, demonstrating that numerosity
is a major, salient property of our visual environment.
