Abstract: This talk proposes Deconvolutional Time Series Regression (DTSR), a general-purpose regression technique for modeling sequential data in which effects can reasonably be assumed to be temporally diffuse. DTSR jointly learns linear effect estimates and temporal convolution parameters from parallel temporal sequences of dependent variable(s) and independent variable(s), using the convolution function to assign time-varying weight to the history of each independent variable in computing the prediction for a given regression target. DTSR successfully recovers true latent convolution functions from synthetic data, and on real-world data from several psycholinguistic experiments DTSR both (1) significantly outperforms competing approaches in terms of prediction error on unseen data and (2) provides plausible, fine-grained, and fairly modality-invariant estimates of the time-course of each regressor’s influence on the dependent measure. These results support the superiority of DTSR to standard modeling approaches like linear mixed-effects regression for a range of experiment types.