An Empirical and Computational Test of Linguistic Relativity

To what extent does the correlation between grammatical gender and conceptual sex in many languages result in speakers having an implicit association between sex and the concepts of inanimate objects? This question was examined in an artificial gender-learning task similar to Phillips and Boroditsky (2003). The task required native English speakers to learn the grammatical gender of nouns denoting inanimate objects (e.g., a fork) as well as humans (e.g., a man). The speakers then rated the similarity of pictures of the inanimate objects and the humans. Consistent with Phillips and Boroditsky's results, speakers rated an object and human as more similar when their nouns' gender was consistent than when it was inconsistent. Furthermore, this consistency effect occurred for objects that were paired with pictures of humans in which no explicit association of gender had been learned. A connectionist model tested hypotheses about the associative links that underlie the consistency effects in the ratings as well as how the speed of learning affects those associations. Together the empirical data and the model simulations demonstrate that associative connections between inanimate object concepts and conceptual properties of sex are unnecessary for the consistency effects.