  The correct interpretation of quantifier statements in the context of a
visual scene requires non-trivial inference mechanisms. For the example of
"most", we discuss two strategies which rely on fundamentally different
cognitive concepts. Our aim is to identify what strategy deep learning models
for visual question answering learn when trained on such questions. To this
end, we carefully design data to replicate experiments from psycholinguistics
where the same question was investigated for humans. Focusing on the FiLM
visual question answering model, our experiments indicate that a form of
approximate number system emerges whose performance declines with more
difficult scenes as predicted by Weber's law. Moreover, we identify confounding
factors, like spatial arrangement of the scene, which impede the effectiveness
of this system.
