Modeling psycholinguistic effect timecourses with deconvolutional time series regression

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This work proposes a new approach to modeling psycholinguistic time series – deconvolutional time series regression (DTSR) – and applies it to existing datasets in order to show that temporal diffusion of effects may play a major role in subject behavior. DTSR learns best-fit impulse response functions that mediate the temporal relationship between independent and dependent variables, providing fine-grained estimates of the timecourse of an independent variable’s influence on the response. Our results show that, on datasets with sufficiently long time series, DTSR models make better predictions and learn substantially different effect estimates than either LME or GAM.

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