A comment on a neuroimaging study of natural language quantifier comprehension

Abstract

Research presented in this journal by McMillan et al. (2005) is the first attempt to investigate the neural basis of natural language quantifiers (see also McMillan et al. (2006) for evidence on quantifier comprehension in patients with focal neurodegenerative disease and Clark and Grossman (2006) for more general discussion). It was devoted to study brain activity during comprehension of sentences with generalized quantifiers. Using BOLD fMRI the authors examined the pattern of neuroanatomical recruitment while subjects were judging the truth-value of statements containing natural language quantifiers. According to the authors their results verify a particular com- * The author would like to express his appreciation to Theo Janssen for comments on the manuscript.

Quantifiers and Working Memory

“…We created situations similar to the span task to compare numerical quantifiers of low and high rank, parity quantifiers and proportional quantifiers. The results enrich and support the data obtained previously in [1][2][3] and predictions drawn from a computational model [4,5]. …”

“…In [5] it has been hypothesized that the number of states is a good predictor of cognitive load. Indeed, our current results show the difference between numerical quantifiers of low and high ranks.…”

“…Cognitive difficulty of quantifier processing might be better assessed on the basis of complexity of the minimal corresponding automata [4,5]. Taking this perspective, in [3] an analogical reaction time experiment carefully differentiating between the following classes of quantifiers has been conducted (see Table 1).…”

Working Memory Mechanism in Proportional Quantifier Verification

“…McMillan et al (2005) concluded that all quantifiers recruit the right inferior parietal cortex, which is associated with numerosity, but only higherorder quantifiers recruit the prefrontal cortex, which is associated with executive resources, like working memory (WM). Szymanik (2007Szymanik ( , 2009 proposed that the cognitive difficulty of quantifier processing might be assessed on the basis of the complexity of the corresponding minimal automata that could handle the computational task. Zajenkowski (2009, 2010a,b) confirmed this hypothesis in a series of empirical studies by comparing the processing of various classes of quantifiers with respect to their computational complexity.…”

The Computational Complexity of Quantified Reciprocals

“…Do the di erences in the computational complexity really play an important role in the natural language processing as some neuroimaging data suggests (see [27], [28])? For example, we could empirically compare the di erences in shifts from the strong interpretation of reciprocal sentences with bounded and proportional quantiers in antecedents.…”