The effects of data quantity on performance of temporal response function analyses of natural speech processing

In recent years, temporal response function (TRF) analyses of neural activity recordings evoked by continuous naturalistic stimuli have become increasingly popular for characterizing response properties within the auditory hierarchy. However, despite this rise in TRF usage, relatively few educational resources for these tools exist. Here we use a dual-talker continuous speech paradigm to demonstrate how a key parameter of experimental design, the quantity of acquired data, influences TRF analyses fit to either individual data (subject-specific analyses), or group data (generic analyses). We show that although model prediction accuracy increases monotonically with data quantity, the amount of data required to achieve significant prediction accuracies can vary substantially based on whether the fitted model contains densely (e.g., acoustic envelope) or sparsely (e.g., lexical surprisal) spaced features, especially when the goal of the analyses is to capture the aspect of neural responses uniquely explained by specific features. Moreover, we demonstrate that generic models can exhibit high performance on small amounts of test data (2–8 min), if they are trained on a sufficiently large data set. As such, they may be particularly useful for clinical and multi-task study designs with limited recording time. Finally, we show that the regularization procedure used in fitting TRF models can interact with the quantity of data used to fit the models, with larger training quantities resulting in systematically larger TRF amplitudes. Together, demonstrations in this work should aid new users of TRF analyses, and in combination with other tools, such as piloting and power analyses, may serve as a detailed reference for choosing acquisition duration in future studies.