Improving methodology of quantifier comprehension experiments Jakub Szymanik, Marcin Zajenkowski Abstract: Our investigation enriches and explains some data obtained by McMillan and Troiani. We have shown that the computational model correctly predicts that quantifiers computable by finite-automata are easier to understand than quantifiers recognized by push-down automata. It improves results of McMillan, which compared only first-order quantifiers with higher-order quantifiers, putting in one group quantifiers recognized by finite-automata as well as those recognized by push-down automata. Moreover, we have assessed differences between logical, parity, and numerical quantifiers. Additionally, decreased reaction time in the case of proportional quantifiers over ordered universes supports findings of McMillan, who attributed the hardness of these quantifiers to the necessity of using working memory.