CogSem

 
Human language is a set of stored lexical items and a system of generative algorithms for combining them into larger representations. So far, research on the neurobiology of lexical access, on the one hand, and composition, on the other, have proceeded largely separately, with little work addressing the impact of composition on the neural representations of individual words. Here we used data from a simple picture naming paradigm to inquire about the impact of phrasal contexts on the representations of individual words within the phrase. We use machine learning algorithms to investigate the extent to which the representation of “house” is the same when “house” is produced as a single word as opposed to when “house” occurs in a phrase such as “red house”.  This avenue of investigation aims to answer whether composition affects word representations equally, or whether structural factors, such as being the syntactic head of a phrase, matter.

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Upcoming Talk:

Dr Brea Chouinard

Departments of Psychology, Computing Science, and Kinesiology, Sport, and Recreation 

University of Alberta

Jan 24th

2020

3:00-4:00 pm

BS-P 319N


 

 

 

 

 

 

 

Decoding words from phrases: Impact of composition on word representations in MEG measurements of picture naming