Speakers of Chinese and English share decodable neuralsemantic representations, which can be elicited by words ineach language. We explore various, common models ofsemantic representation and their correspondences to eachother and to these neural representations. Despite very strongcross-language similarity in the neural data, we find that twoversions of a corpus-based semantic model do not show thesame strong correlation between languages. Behavior-basedmodels better approximate cross-language similarity, butthese models also fail to explain the similarities observed inthe neural data. Although none of the examined modelsexplain cross-language neural similarity, we explore how theymight provide additional information over and above cross-language neural similarity. We find that native speakers’ratings of noun-noun similarity and one of the corpus modelsdo further correlate with neural data after accounting forcross-language similarities.

Distributional learning research has established that humanscan track the frequencies of sequentially presented stimuli inorder to infer the probabilities of upcoming events (e.g., Hasher& Zacks, 1984). Here, we set out to explore anticipation of astimulus after implicit distributional learning. We hypothesizethat as people learn the category frequency informationimplicitly, response times will scale according to the relativefrequency of the stimulus category. Twelve adult participantsviewed photographs of faces, tools, and buildings whileperforming a simple classification task. We found that responsetimes significantly decreased with greater frequencies in thedistribution of stimulus categories. This result suggested thatdistributional information about the internal representations ofthe stimuli could be learned and indicated the possibility thatparticipants anticipated the stimuli proportional to theprobability of the category appearing and thereby reducedresponse times for the more frequent categories.