Improving methodology of quantifier comprehension experiments.

Recently, research devoted to computational modeling of quantifier comprehension has been extensively published in this journal. McMillan et al. (2005) using neuroimaging methods examined the pattern of neuroanatomical recruitment while subjects were judging the truth-value of statements containing natural language quantifiers. The authors were considering two standard types of quantifiers: first-order (e.g., ``all'', ``some'', ``at least 3''), and higher-order quantifiers (e.g., ``more than half'', ``an even number of''). They presented the data showing that all quantifiers recruit the right inferior parietal cortex, which is associated with numerosity, but only higher-order quantifiers recruit the prefrontal cortex, which is associated with executive resources, like working memory. In the latest paper Troiani et al. (2009) assessed quantifier comprehension in patients with corticobasal degeneration (CBD) and healthy subjects. They compared numerical quantifiers, like ``at least 3'', which require magnitude processing, and logical quantifiers, like ``some'', which can be understood using a simple form of perceptual logic. Their findings are consistent with the claim that numerical quantifier comprehension depends on a lateral parietal-dorsolateral prefrontal network, but logical quantifier comprehension depends instead on a rostromedial prefrontal-posterior cingulate network.