Differential asymmetries for recognizing nouns and verbs: Where are they?

Abstract

To support categorical representation in the brain for grammatical class, it is necessary to show that noun-verb differences are attributable to parts of speech and not to covarying semantic factors. Prior visual-half field investigations of noun-verb processing have confounded grammatical class with imageability. The current study included numerous tests of differential noun-verb processing across visual fields for stimuli equated for imageability. Task (lexical decision, pronunciation) and list context (blocked vs. mixed lists) variables were examined in 168 right-handed participants. There was no reliable reduction of the right visual field advantage for moderately imageable nouns as compared with verbs. If there are qualitative hemisphere differences in single-word noun and verb recognition, these may be attributable to semantic dimensions that tend to covary with grammatical class.

Bilingual language lateralization: A meta-analytic tale of two hemispheres☆

“…Thus, outcomes from the L1 and L2 analyses of orthographic and semantic language components are consistent with each other and with predictions in the literature for reduced LH dominance for these components (e.g., Vaid, 1984a). Also like the L1 analysis, the L2 analysis supported predictions in the literature (e.g., Chiarello et al, 2002) by revealing a robust and reliable LH effect for single word responses (d = 0.41; 95% CI = 0.30, 0.51; k = 35), and homogeneity was retained, Q W (34) = 50.62, n.s.…”

“…Results revealed that orthographic (d = 0.18; 95% CI = 0.06, 0.32; k = 43, Q W (42) = 40.36, p = 0.81) and semantic processing (d = 0.13; 95% CI = −0.09, 0.35; k = 12, Q W (11) = 16.11, p = 0.17) showed very weak LH lateralization and bilateral activation, respectively, consistent with predictions in the literature (e.g., Vaid, 1984a). In contrast, responses to single words (d = 0.34; 95% CI = 0.22, 0.46; k = 28, Q W (27) = 46.85, p < 0.05) showed increased LH involvement, also as predicted in the literature (e.g., Chiarello, Liu, Shears, & Kacinik, 2002). For phonological word pair judgments, a reliable bilateral effect was detected (d = 0.32; 95% CI = −0.06, 0.70; k = 8, Q W (7) = 7.10, p = 0.53), consistent with some current theories of speech perception (e.g., .…”

Effect of verb argument structure on picture naming in children with and without specific language impairment (SLI)

“…Several other studies have also found that nouns are processed more quickly than verbs (e.g. Sereno 1999, Tyler et al 2001, Dietrich et al 2001), but this effect can disappear if imageability is balanced (Chiarello et al 2002, but see Kauschke and Stenneken 2008). Therefore, the differences between nouns and verbs may be attributable to imageability and the degree to which the word was a good label for the target picture.…”

The British Sign Language (BSL) norms for age of acquisition, familiarity, and iconicity

“…In spoken language research, hundreds of studies have highlighted the importance of various lexical properties, especially when previous theoretical conclusions have been called into doubt because of experimental confounds with one or another uncontrolled lexical variable (see, e.g., Chiarello, Liu, Shears, & Kacinik, 2002). In turn, this has led to more and more sophisticated studies designed to unravel the contributions of various lexical variables, using normative samples of thousands of words (e.g., subjective ratings of age of acquisition [AoA], imageability, and familiarity for thousands of English words; Bird, Franklin, & Howard, 2001;Cortese & Fugett, 2004;Cortese & Khanna, 2008;Gilhooly & Logie, 1980;StadthagenGonzalez & Davis, 2006), in addition to massive amounts of information derived from text corpora (e.g., frequency of occurrence in the British National Corpus sample of 100 million words, www.natcorp.ox.ac.uk) and extremely large data sets, such as the English Lexicon Project (Balota et al, 2007), containing word naming and lexical decision latencies for more than 40,000 English words (see Baayen, 2005, for further discussion).…”