How to analyse electrophysiological responses to naturalistic language with time-resolved multiple regression

Naturalistic language processing cannot be approached with the analysis methods constructed to handle well-controlled experiments. Language is a multi- and cross-level phenomenon, with sequential interdependencies and correlations between various lexical dimensions. A recently-developed method allows the analysis of neural time series during natural story comprehension: time-resolved multiple regression. It consists in modelling continuous brain recordings with multiple regression after embedding linguistic features in a temporal-extension matrix (a distributed-lags model). It identifies neural correlates of linguistic processes, accounting for temporal interdependencies – simultaneously for, e.g. acoustics, phonology and semantics. This has resulted in impactful discoveries about how brains process coherent speech, potentially broadening the class of phenomena that can be studied. I discuss the method conceptually, highlight caveats, and relate it to similar as well as to traditional methods, all with a particular consideration for analysing the processing of coherent narratives. In a practical example, the word frequency-dependent N400 effect is estimated from a half-hour continuous narrative.