While lexical norm databases are being developed with a renewed emphasis on perceptual psycholinguistic features, verbs are often neglected. Research on nouns and adjectives utilizes vertical spatial localization ratings, providing an analogue for the inclusion of verbs via vertical directionality ratings. This study demonstrates the feasibility of collecting such ratings for 32 English verbs, as well as the possibility of assessing directionality ratings in other spatial dimensions. Further, ratings were analyzed using distributional semantic models. Results indicate that language statistics are strongly associated with human ratings, providing convergent validity for vertical directionality as a useful psycholinguistic measure. Additionally, a comparison of the predictive performance of LSA and Web 1T 5-gram models on human ratings revealed that the first-order 5-gram model accounted for significant unique variance in the norms, while LSA did not. It may be concluded that spatial verb associations are encoded linguistically by proximal, syntactic dependencies.

Psycholinguistic studies are now more frequently considering the role of embodied information in language processing. While the perceptual features of nouns lend themselves well to cataloguing and norming, verbs also contain important perceptual information that may be useful to researchers. We present preliminary analyses of an ongoing study norming the directionality and strength of motion implied by verbs in three dimensions. We sought to assess the convergent and divergent validity of our pilot database using data derived from other psycholinguistic norm databases and distributional semantic techniques. Results suggest that this norming method is supported by convergent validity while capturing unique psycholinguistic information. More specifically, these norms may contribute to stimulus selection and refinement, generating computational representations of verbs, and novel research designs in future studies.