Tracking semantic relatedness: numeral classifiers guide gaze to visual world objects - Lobben, Marit; Bochynska, Agata; Eifring, Halvor; Laeng, Bruno (Frontiers in Language Sciences, 2023)
Directing visual attention towards items mentioned within utterances can optimize understanding the unfolding spoken language and preparing appropriate behaviors. In several languages, numeral classifiers specify semantic classes of nouns but can also function as reference trackers. Whereas all classifier types function to single out objects for reference in the real world and may assist attentional guidance, we propose that only sortal classifiers are inherently attached to the nouns' semantics. By contrast, container classifiers are pragmatically attached, and default classifiers index a noun without specifying the semantics. Using eye tracking and the "visual world paradigm", we found that classifier types affect the rapidity of spontaneously looking at the target objects on a screen. We had Chinese speakers (N=20) listen to sentences and we observed that they looked spontaneously within 150 ms after offset of the Sortal classifier. This did not happen in a control group of non-Chinese speakers. Specifically, after about 200 ms the same occurred for the container classifiers, but with the default only after about 700 ms. These significantly different latencies indicate that the stronger the semantic relatedness between a classifier and its noun, the more efficient the deployment of overt attention.