Jointly modeling behavioral and EEG measures of proactive control in task switching.

In this study, we implement joint modeling of behavioral and single-trial electroencephalography (EEG) data derived from a cued-trials task-switching paradigm to test the hypothesis that trial-by-trial adjustment of response criterion can be linked to changes in the event-related potentials (ERPs) elicited during the cue-target interval (CTI). Specifically, we assess whether ERP components associated with preparation to switch task and preparation of the relevant task are linked to a response criterion parameter derived from a simple diffusion decision model (DDM). Joint modeling frameworks characterize the brain-behavior link by simultaneously modeling behavioral and neural data and implementing a linking function to bind these two submodels. We examined three joint models: The first characterized the core link between EEG and criterion, the second added a switch preparation input parameter and the third also added a task preparation input parameter. The criterion-EEG link was strongest just before target onset. Inclusion of switch and task preparation parameters did not improve the performance of the criterion-EEG link but was necessary to accurately model the ERP waveform morphology. While we successfully jointly modeled latent model parameters and EEG data from a task-switching paradigm, these findings show that customized cognitive models are needed that are tailored to the multiple cognitive control processes underlying task-switching performance. This is the first paper to implement joint modeling of behavioral measures and single-trial electroencephalography (EEG) data derived from the cue-target interval in a cued-trials task-switching paradigm. Model hyperparameters showed a strong link between response criterion and the pre-target negativity amplitude. Additional parameters (switch preparation, task preparation) were necessary to model the cue-locked ERP waveform morphology. This is consistent with multiple cognitive control processes underlying proactive control and points to the need for more nuanced models of task-switching performance.