Studying language in context using the temporal generalization method. (3rd February 2020)

• Record Type: Journal Article
• Authors: Fyshe, Alona
• Is Part Of: Philosophical transactions. Volume 375:Number 1791(2020)
• Publication Date: 2020-02-03
• Subjects: temporal generalization method -- magnetoencephalography -- language -- semantics -- machine learning; Biology -- Periodicals; Science -- Periodicals; 570
• DOI: 10.1098/rstb.2018.0531
• Languages: English
• ISSNs: 0962-8436

Abstract

Abstract : The temporal generalization method (TGM) is a data analysis technique that can be used to test if the brain's representation for particular stimuli (e.g. sounds, images) is maintained, or if it changes as a function of time (King J-R, Dehaene S. 2014 Characterizing the dynamics of mental representations: the temporal generalization method. Trends Cogn. Sci. 18, 203–210. (doi:10.1016/j.tics.2014.01.002 )). The TGM involves training models to predict the stimuli or condition using a time window from a recording of brain activity, and testing the resulting models at all possible time windows. This is repeated for all possible training windows to create a full matrix of accuracy for every combination of train/test window. The results of a TGM indicate when brain activity patterns are consistent (i.e. the trained model performs well even when tested on a different time window), and when they are inconsistent, allowing us to track neural representations over time. The TGM has been used to study the representation of images and sounds during a variety of tasks, but has been less readily applied to studies of language. Here, we give an overview of the method itself, discuss how the TGM has been used to analyse two studies of language in context and explore how the TGM could be applied to further our understanding of semantic composition. This article is part of the theme issue 'Towards mechanistic models of meaning composition'.