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

• Published in: Psychophysiology 2023-07, Vol.60 (7), p.e14241-n/a
• Main Authors: Karayanidis, Frini, Hawkins, Guy E., Wong, Aaron S. W., Aziz, Fayeem, Hunter, Montana, Steyvers, Mark
• Format: Article
• Language: English
• Subjects: Brain - physiology; Cognitive ability; Cues; EEG; Electroencephalography; ERPs; Event-related potentials; evidence accumulation; Evoked Potentials - physiology; hierarchical Bayesian estimation; Humans; joint modeling; Morphology; Psychomotor Performance; Reaction Time; task switching
• ISSN: 0048-5772; 1469-8986; 1540-5958
• DOI: 10.1111/psyp.14241

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.