  The present work investigates whether different quantification mechanisms
(set comparison, vague quantification, and proportional estimation) can be
jointly learned from visual scenes by a multi-task computational model. The
motivation is that, in humans, these processes underlie the same cognitive,
non-symbolic ability, which allows an automatic estimation and comparison of
set magnitudes. We show that when information about lower-complexity tasks is
available, the higher-level proportional task becomes more accurate than when
performed in isolation. Moreover, the multi-task model is able to generalize to
unseen combinations of target/non-target objects. Consistently with behavioral
evidence showing the interference of absolute number in the proportional task,
the multi-task model no longer works when asked to provide the number of target
objects in the scene.
