Computational complexity explains neural differences in quantifier verification

Abstract

Different classes of quantifiers provably require different verification algorithms with different complexity profiles. The algorithm for proportional quantifiers, like ‘most’, is more complex than that for nonproportional quantifiers, like ‘all’ and ‘three’. We tested the hypothesis that different complexity profiles affect ERP responses during sentence verification, but not during sentence comprehension. In experiment 1, participants had to determine the truth value of a sentence relative to a previously presented array of geometric objects. We observed a sentence-final negative effect of truth value, modulated by quantifier class. Proportional quantifiers elicited a sentence-internal positivity compared to nonproportional quantifiers, in line with their different verification profiles. In experiment 2, the same stimuli were shown, followed by comprehension questions instead of verification. ERP responses specific to proportional quantifiers disappeared in experiment 2, suggesting that they are only evoked in a verification task and thus reflect the verification procedure itself. Our findings demonstrate that algorithmic aspects of human language processing are subjected to the same formal constraints applicable to abstract machines.