Biology subjects

Matzke, D.

Publications and source records attributed to Matzke, D..

4 recordsLinked to original sources

bioRxiv2023

Faster action reprogramming, but not stopping, with proactive cues: Combining EMG and computational modelling in response-selective stop signal tasks

The ability to stop simple ongoing actions has been extensively studied using the stop signal task, but less is known about inhibition in more complex scenarios. Here we used a task requiring bimanual responses to go stimuli, but selective inhibition of only one of those responses following a stop signal. We assessed how proactive cues affect the nature of both the responding and stopping processes, and the well-documented "stopping delay" in the continuing action following successful stopping. In this task, estimates of the speed of inhibition based on a simple-stopping model are inappropriate, and have produced inconsistent findings about the effects of proactive control on motor inhibition. We instead used a multi-modal approach, based on improved methods of detecting and interpreting partial electromyographical (EMG) responses and the recently proposed SIS (simultaneously inhibit and start) model of selective stopping behaviour. Our results provide clear and converging evidence that proactive cues reduce the stopping delay effect by slowing bimanual responses and speeding unimanual responses, with a negligible effect on the speed of the stopping process.

neuroscience↗

bioRxiv2022

Right inferior frontal cortex damage impairs the initiation of inhibitory control, but not its implementation

Inhibitory control is one of the most important control functions in the human brain. Much of our understanding of its neural basis comes from seminal work showing that lesions to the right inferior frontal cortex (rIFC) increase stop-signal reaction time (SSRT), a latent variable that expresses the speed of inhibitory control. However, recent work has identified substantial limitations of the SSRT method. Notably, SSRT is confounded by trigger failures: stop-signal trials in which inhibitory control was never initiated. Such trials inflate SSRT, but are typically indicative of attentional, rather than inhibitory deficits. Here, we used hierarchical Bayesian modeling to identify stop-signal trigger failures in human rIFC lesion patients, non-rIFC lesion patients, and healthy comparisons. Furthermore, we measured scalp-EEG to detect {beta}-bursts, a neurophysiological index of inhibitory control. rIFC lesion patients showed a more than five-fold increase in trigger failure trials and did not exhibit the typical increase of stop-related frontal {beta}- bursts. However, on trials in which such {beta}-bursts did occur, rIFC patients showed the typical subsequent upregulation of {beta} over sensorimotor areas, indicating that their ability to implement inhibitory control, once triggered, is intact. These findings suggest that the role of rIFC in inhibitory control has to be fundamentally reinterpreted.

neuroscience↗

bioRxiv2021

Cognitive process modeling addresses context independence violations in the ABCD Study stop-signal task

The Adolescent Brain Cognitive Development (ABCD) Study is a longitudinal neuroimaging study of unprecedented scale that is in the process of following over 11,000 youth from middle childhood though age 20. However, a design feature of the studys stop-signal task violates "context independence", an assumption critical to current non-parametric methods for estimating stop-signal reaction time (SSRT), a key measure of inhibitory ability in the study. This has led some experts to call for the task to be changed and for previously collected data to be used with caution. We present a formal cognitive process model, the BEESTS-ABCD model, that provides a mechanistic explanation for the impact of this design feature, describes key behavioral trends in the ABCD data, and allows biases in SSRT estimates resulting from context independence violations to be quantified. We use the model to demonstrate that, although non-parametric SSRT estimates generally preserve the rank ordering of participants SSRT values, failing to account for context independence violations can lead to erroneous inferences in several realistic scenarios. Nonetheless, as the BEESTS-ABCD model can be used to accurately recover estimates of SSRT and other mechanistic parameters of interest from ABCD data, the impact of such violations can be effectively mitigated.

neuroscience↗

bioRxiv2019

Reconsidering electrophysiological markers of response inhibition in light of trigger failures in the stop-signal task

We investigate the neural correlates underpinning response inhibition using a parametric ex-Gaussian model of stop-signal task performance, fit with hierarchical Bayesian methods, in a large healthy sample (N=156). The parametric model accounted for trigger failure (i.e., failures to initiate the inhibition process) and returned an SSRT estimate (SSRTEXG3) that was attenuated by {approx}65ms compared to traditional non-parametric SSRT estimates (SSRTint). The amplitude and latency of the N1 and P3 event related potential components were derived for both stop-success and stop-failure trials and compared to behavioural estimates derived from traditional (SSRTint) and parametric (SSRTEXG3, trigger failure) models. Both the fronto-central N1 and P3 peaked earlier and were larger for stop-success than stop-failure trials. For stop-failure trials only, N1 peak latency correlated with both SSRT estimates as well as trigger failure and temporally coincided with SSRTEXG3, but not SSRTint. In contrast, P3 peak and onset latency were not associated with any behavioural estimates of inhibition for either trial type. While overall the N1 peaked earlier for stop-success than stop-failure trials, this effect was not found in poor task performers (i.e., high trigger failure/slow SSRT). These findings are consistent with attentional modulation of both the speed and reliability of the inhibition process, but not for poor performers. Together with the absence of any P3 onset latency effect, our findings suggest that attentional mechanisms are important in supporting speeded and reliable inhibition processes required in the stop-signal task.

neuroscience↗