Tracking semantic relatedness: Numeral classifiers guide gaze to visual world objects

Directing visual attention towards items mentioned in utterances can optimize understanding the unfolding spoken language and preparing to behave appropriately in different world situations. In particular, numeral classifiers not only specify semantic classes of nouns but could 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; whereas container classifiers are pragmatically attached and the 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. Shortly after 200 ms the same occurred for the container classifiers, but with the default only after 764 ms. These latencies were significantly different from each other indicating that the stronger the semantic relatedness between a classifier and its noun, the more efficient the deployment of overt attention.