Flexible cognitive architectures for response inhibition
Imagine you driving down the highway stuck behind a slow car. You glance in the rear-view mirror to check if it is safe to overtake, but before you do so, you hear the siren of an ambulance and abort the overtaking manoeuvre. This type of response inhibition—the ability to stop ongoing responses that have become no longer appropriate—is a central component of executive control and is essential for safe and effective interaction with an ever-changing and often unpredictable world. Inhibitory ability is typically quantified by the stop-signal reaction time, the completion time of an inhibitory process triggered by a signal to stop responding. Because stop-signal reaction times cannot be directly observed, they must be inferred based on a model in which independent inhibitory (“stop”') and response (“go”) processes race with each other to control behavior. I review the limitations of the traditional non-parametric race model framework and show that it cannot be used to investigate response inhibition in the full range of situations and paradigms that are relevant to the study of cognitive control. To address this shortcoming, I outline a flexible parametric approach that generalizes the race model to account for aspects of behavior that are characteristics of real-world stopping, such as choice errors, attentional lapses, and the interaction between the stop and go processes. I propose various parametrizations of the framework, ranging from the descriptive ex-Gaussian distribution to a racing diffusion evidence-accumulation architecture, explore the strengths and weaknesses of the different models, and illustrate their utility with clinical and experimental data in choice-based as well as anticipated-response-based paradigms. I end with discussing the potential of this modeling framework to provide a comprehensive account of the mental processes governing behavior in realistically complex situations, and how it may contribute to the prediction of stopping performance in dynamic settings.
Thanks for the great talk, Dora! Curious what sorts of complex tasks you are wanting to target in the future, if you have some in mind?