Stop-signal Paradigm Research Articles

A differential impact of action–effect temporal contiguity on different measures of response inhibition in the Go\\No-Go and Stop-signal paradigms

Response inhibition refers to suppressing a prepotent motor response and is often studied and discussed as an act of cognitive control. Much less attention was given to the potential contribution of motor control processes to response inhibition. Accumulated empirical findings show that a perceptual effect temporally contiguous with a response improves motor control performance. In the current study, we followed this work by manipulating action–effect temporal contiguity to enhance motor performance and investigated its impact on response selection and inhibition. In two experiments, we integrated a Go/No-Go (GNGT; Experiment 1) and a Stop-signal (SST; Experiment 2) task with the Effect–Motivation task, previously used to capture the facilitating impact of action–effect temporal contiguity on response times (RTs). Replicating previous findings, RTs were shorter following temporally contiguous compared to Lagged action–effect in Go trials in both the GNGT (Experiment 1) and SST (Experiment 2). Notably, an Immediate action–effect improved response inhibition in the GNGT (Experiment 1) but did not modulate Stop-signal reaction time (SSRT) in the SST (Experiment 2). Unexpectedly, the error rate on Go trials was higher in the Immediate effect condition in Experiment 2. We interpret the findings to suggest that an action’s (Immediate) perceptual effect may promote response inhibition performance by enhancing selective association between the Go stimuli and the Go response and not by improving cognitive control ability. The findings also imply that an Immediate action–effect may hamper action control (e.g., by increasing general readiness to respond), at least when action control does not benefit from automatic stimulus–response association.

A hybrid approach to dynamic cognitive psychometrics

Dynamic cognitive psychometrics measures mental capacities based on the way behavior unfolds over time. It does so using models of psychological processes whose validity is grounded in research from experimental psychology and the neurosciences. However, these models can sometimes have undesirable measurement properties. We propose a “hybrid” modeling approach that achieves good measurement by blending process-based and descriptive components. We demonstrate the utility of this approach in the stop-signal paradigm, in which participants make a series of speeded choices, but occasionally are required to withhold their response when a “stop signal” occurs. The stop-signal paradigm is widely used to measure response inhibition based on a modeling framework that assumes a race between processes triggered by the choice and the stop stimuli. However, the key index of inhibition, the latency of the stop process (i.e., stop-signal reaction time), is not directly observable, and is poorly estimated when the choice and the stop runners are both modeled by psychologically realistic evidence-accumulation processes. We show that using a descriptive account of the stop process, while retaining a realistic account of the choice process, simultaneously enables good measurement of both stop-signal reaction time and the psychological factors that determine choice behavior. We show that this approach, when combined with hierarchical Bayesian estimation, is effective even in a complex choice task that requires participants to perform only a relatively modest number of test trials.

Convolutional neural networks for classifying healthy individuals practicing or not practicing meditation according to the EEG data

Convolutional neural networks for classifying healthy individuals practicing or not practicing meditation according to the EEG data

Lower synaptic density associated with gaming disorder: an 18F-SynVesT-1 PET imaging study

BackgroundInternet gaming disorder (IGD) is an ideal model to study the mechanisms underlying synaptic deficits in addiction as it eliminates the confounding effects of substance use. Synaptic loss and deficits...

Irrelevant emotional expressions interfered with response inhibition: the role of contrast emotions

ABSTRACT Irrelevant emotional faces would facilitate or inhibit response inhibition, depending on how these faces are paired with different emotional faces. In previous studies, angry faces were either paired with neutral, happy, or fearful faces in the response inhibition task, potentially leading to mixed results. This is the first study where all four irrelevant emotional faces (happy, angry, fearful, and neutral) were used simultaneously and presented in the same block as a stop-signal in the stop-signal paradigm. Participants were required to respond to the go signals. Occasionally, a stop-signal with irrelevant facial expressions was presented, where participants were required to withhold their motor response. All stop signals with irrelevant emotional facial expressions in comparison to neutral facial expressions interfered with the response inhibition process. Our results extend previous findings by suggesting that approach and avoidance reactions to facial expressions depend on the contrasting emotions presented in the task.

Boosting inhibition control process by knitting at school.

Across two experiments, the presented research explored the impact of a knitting bout on elementary school pupils' inhibition abilities. They proposed an accurate measure of the pupils' inhibition abilities through the use of a stop-signal paradigm. In order to take into account, the differentiation between cool and hot inhibitions abilities, the emotional content of the stimuli was manipulated across experiments. Neutral materials were used in Experiment 1 when emotionally charged materials were in Experiment 2. The findings of both experiments highlighted a beneficial impact of the knitting bout on children's inhibition abilities. While the results of Experiment 1 showed an optimization of inhibition abilities for the knitting session group in comparison to the control group, Experiment 2 revealed a disappearance of the effect of the emotional content on these abilities as well. Proposals as to why EF could be sensitive to knitting practice are discussed.

A dual-task approach to inform the taxonomy of inhibition-related processes.

Response inhibition is key to controlled behavior and is commonly investigated with the stop-signal paradigm. The authors investigated how response inhibition is situated within a taxonomy of control processes by combining multiple forms of control within dual tasks. Response inhibition, as measured by stop-signal reaction time (SSRT), was impaired when combined with shape matching, but not the flanker task, and when combined with cued task switching, but not predictable task switching, suggesting that response inhibition may be weakly or variably impaired when combined with selective attention and set shifting demands, respectively. Response inhibition was also consistently impaired when combined with the N-back or directed forgetting tasks, putative measures of working memory. Impairments of response inhibition by other control demands appeared to be primarily driven by task context, as SSRT slowing was similar for trials where control demands were either high (e.g., task switch) or low (e.g., task stay). These results demonstrate that response inhibition processes are often impaired in the context of other control demands, even on trials where direct engagement of those other control processes is not required. This suggests a taxonomy of control in which response inhibition overlaps with related control processes, especially working memory. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Three threats to the validity of the Reading the Mind in the Eyes Test: A commentary on

Inhibitory control is an executive function that is closely and bidirectionally related to sports practice. The objective of this systematic review and meta-analysis was to study the effect of this relationship when response suppression is assessed within the Stop-Signal Paradigm. Twenty-four articles met the inclusion criteria and were selected for qualitative analysis, of which 11 studies were further analyzed through meta-analytic techniques. The standardized mean difference (SMD) was estimated for the stop-signal reaction time, and the influence of moderator variables was assessed. Athletes showed shorter stop-signal reaction time than non-athlete controls (SMD=0.44; 95% CI=0.14, 0.73), and this effect was mediated by age (SMD=−0.56; 95% CI=−1.11, −0.01). Athletes’ superior stop-signal reaction time may be a result of extensive practice in cognitively demanding competitive environments. Young athletes can benefit the most from sports practice. In addition, engaging individuals in more cognitively demanding activities may obtain better response suppression enhancements, although the evidence in the stop-signal task is limited. Finally, some stop-signal task methodological aspects should be considered in future research.

Comparing anticipatory and stop-signal response inhibition with a novel, open-source selective stopping toolbox

Response inhibition is essential for terminating inappropriate actions and, in some cases, may be required selectively. Selective stopping can be investigated with multicomponent anticipatory or stop-signal response inhibition paradigms. Here we provide a freely available open-source Selective Stopping Toolbox (SeleST) to investigate selective stopping using either anticipatory or stop-signal task variants. This study aimed to evaluate selective stopping between the anticipatory and stop-signal variants using SeleST and provide guidance to researchers for future use. Forty healthy human participants performed bimanual anticipatory response inhibition and stop-signal tasks in SeleST. Responses were more variable and slowed to a greater extent during the stop-signal than in the anticipatory paradigm. However, the stop-signal paradigm better conformed to the assumption of the independent race model of response inhibition. The expected response delay during selective stop trials was present in both variants. These findings indicate that selective stopping can successfully be investigated with either anticipatory or stop-signal paradigms in SeleST. We propose that the anticipatory paradigm should be used when strict control of response times is desired, while the stop-signal paradigm should be used when it is desired to estimate stop-signal reaction time with the independent race model. Importantly, the dual functionality of SeleST allows researchers flexibility in paradigm selection when investigating selective stopping.

Development of a neural network for diagnosing the risk of depression according to the experimental data of the stop signal paradigm.

These days, the ability to predict the result of the development of the system is the guarantee of the successful functioning of the system. Improving the quality and volume of information, complicating its presentation, the need to detect hidden connections makes it ineffective, and most often impossible, to use classical statistical forecasting methods. Among the various forecasting methods, methods based on the use of artificial neural networks occupy a special place. The main objective of our work is to create a neural network that predicts the risk of depression in a person using data obtained using a motor control performance testing system. The stop-signal paradigm (SSP) is an experimental technique to assess a person's ability to activate deliberate movements or inhibit movements that have become inadequate to external conditions. In modern medicine, the SSP is most commonly used to diagnose movement disorders such as Parkinson's disease or the effects of stroke. We hypothesized that SSP could serve as a basis for detecting the risk of affective diseases, including depression. The neural network we are developing is supposed to combine such behavioral indicators as: the amount of missed responses, amount of correct responses, average time, the amount of correct inhibition of movements after stop-signal onset. Such a combination of indicators will provide increased accuracy in predicting the presence of depression in a person. The artificial neural network implemented in the work allows diagnosing the risk of depression on the basis of the data obtained in the stop-signal task. An architecture was developed and a system was implemented for testing motor control indicators in humans, then it was tested in real experiments. A comparison of neural network technologies and methods of mathematical statistics was carried out. A neural network was implemented to diagnose the risk of depression using stop-signal paradigm data. The efficiency of the neural network (in terms of accuracy) was demonstrated on data with an expert assessment for the presence of depression and data from the motor control testing system.

Estimating the Time to Do Nothing: Toward Next-Generation Models of Response Inhibition

Controlled behavior requires response inhibition, which is a cognitive function that involves withholding action as goals change. Response inhibition is often assessed using the stop-signal paradigm, in which participants respond to most stimuli but periodically withhold their response when a subsequent stop signal occurs. The stop-signal paradigm rests on the theoretical foundation of the independent race model, which assumes a stop racer that races independently against a go racer; behavior is determined by which racer finishes first. We highlight work showing violations of the keystone independence assumption of existing stop models and discuss promising new models of response inhibition.

Brain-inspired meta-reinforcement learning cognitive control in conflictual inhibition decision-making task for artificial agents

Conflictual cues and unexpected changes in human real-case scenarios may be detrimental to the execution of tasks by artificial agents, thus affecting their performance. Meta-learning applied to reinforcement learning may enhance the design of control algorithms, where an outer learning system progressively adjusts the operation of an inner learning system, leading to practical benefits for the learning schema. Here, we developed a brain-inspired meta-learning framework for inhibition cognitive control that i) exploits the meta-learning principles in the neuromodulation theory proposed by Doya, ii) relies on a well-established neural architecture that contains distributed learning systems in the human brain, and iii) proposes optimization rules of meta-learning hyperparameters that mimic the dynamics of the major neurotransmitters in the brain. We tested an artificial agent in inhibiting the action command in two well-known tasks described in the literature: NoGo and Stop-Signal Paradigms. After a short learning phase, the artificial agent learned to react to the hold signal, and hence to successfully inhibit the motor command in both tasks, via the continuous adjustment of the learning hyperparameters. We found a significant increase in global accuracy, right inhibition, and a reduction in the latency time required to cancel the action process, i.e., the Stop-signal reaction time. We also performed a sensitivity analysis to evaluate the behavioral effects of the meta-parameters, focusing on the serotoninergic modulation of the dopamine release. We demonstrated that brain-inspired principles can be integrated into artificial agents to achieve more flexible behavior when conflictual inhibitory signals are present in the environment.

The Asymmetric Laplace Gaussian (ALG) Distribution as the Descriptive Model for the Internal Proactive Inhibition in the Standard Stop Signal Task.

Measurements of response inhibition components of reactive inhibition and proactive inhibition within the stop-signal paradigm have been of particular interest to researchers since the 1980s. While frequentist nonparametric and Bayesian parametric methods have been proposed to precisely estimate the entire distribution of reactive inhibition, quantified by stop signal reaction times (SSRT), there is no method yet in the stop signal task literature to precisely estimate the entire distribution of proactive inhibition. We identify the proactive inhibition as the difference of go reaction times for go trials following stop trials versus those following go trials and introduce an Asymmetric Laplace Gaussian (ALG) model to describe its distribution. The proposed method is based on two assumptions of independent trial type (go/stop) reaction times and Ex-Gaussian (ExG) models. Results indicated that the four parametric ALG model uniquely describes the proactive inhibition distribution and its key shape features, and its hazard function is monotonically increasing, as are its three parametric ExG components. In conclusion, the four parametric ALG model can be used for both response inhibition components and its parameters and descriptive and shape statistics can be used to classify both components in a spectrum of clinical conditions.

Hold your horses: Differences in EEG correlates of inhibition in cancelling and stopping an action

Behavioral adaptation to changing contextual contingencies often requires the rapid inhibition of planned or ongoing actions. Inhibitory control has been mostly studied using the stop–signal paradigm, which conceptualizes action inhibition as the outcome of a race between independent GO and STOP processes. Inhibition is predominantly considered to be independent of action type, yet it is questionable whether this conceptualization can apply to stopping an ongoing action. To test the claimed generality of action inhibition, we investigated behavioral stop–signal reaction time (SSRT) and scalp electroencephalographic (EEG) activity in two inhibition contexts: Using variants of the stop–signal task, we asked participants to cancel a prepared–discrete action or to stop an ongoing–rhythmic action in reaction to a STOP signal. The behavioral analysis revealed that the discrete and rhythmic SSRTs were not correlated. The EEG analysis showed that the STOP signal evoked frontocentral activity in the time and frequency domains (Delta/Theta range) in a task–specific manner: The P3 onset latency was the best correlate of discrete SSRT whereas N2/P3 peak-to-peak amplitude was the best correlate of rhythmic SSRT. These findings do not support a conceptualization of inhibition as action–independent but rather suggest that the differential engagement of both components of the N2/P3–complex as a function of action type pertains to functionally independent inhibition subprocesses.

Don't Stop Me Now: Neural Underpinnings of Increased Impulsivity to Temporally Predictable Events.

Although the benefit of temporal predictability for behavior is long-established, recent studies provide evidence that knowing when an important event will occur comes at the cost of greater impulsivity. Here, we investigated the neural basis of inhibiting actions to temporally predictable targets using an EEG-EMG method. In our temporally cued version of the stop-signal paradigm (two-choice task), participants used temporal information delivered by a symbolic cue to speed their responses to the target. In a quarter of the trials, an auditory signal indicated that participants had to inhibit their actions. Behavioral results showed that although temporal cues speeded RTs, they also impaired the ability to stop actions as indexed by longer stop-signal reaction time. In line with behavioral benefits of temporal predictability, EEG data demonstrated that acting at temporally predictable moments facilitated response selection at the cortical level (reduced frontocentral negativity just before the response). Likewise, activity of the motor cortex involved in suppression of incorrect response hand was stronger for temporally predictable events. Thus, by keeping an incorrect response in check, temporal predictability likely enabled faster implementation of the correct response. Importantly, there was no effect of temporal cues on the EMG-derived index of online, within-trial inhibition of subthreshold impulses. This result shows that although participants were more prone to execute a fast response to temporally predictable targets, their inhibitory control was, in fact, unaffected by temporal cues. Altogether, our results demonstrate that greater impulsivity when responding to temporally predictable events is paralleled by enhanced neural motor processes involved in response selection and implementation rather than impaired inhibitory control.

More than skin deep: about the influence of self-relevant avatars on inhibitory control

One important aspect of cognitive control is the ability to stop a response in progress and motivational aspects, such as self-relevance, which may be able to influence this ability. We test the influence of self-relevance on stopping specifically if increased self-relevance enhances reactive response inhibition. We measured stopping capabilities using a gamified version of the stop-signal paradigm. Self-relevance was manipulated by allowing participants to customize their game avatar (Experiment 1) or by introducing a premade, self-referential avatar (Experiment 2). Both methods create a motivational pull that has been shown to increase motivation and identification. Each participant completed one block of trials with enhanced self-relevance and one block without enhanced self-relevance, with block order counterbalanced. In both experiments, the manipulation of self-relevance was effective in a majority of participants as indicated by self-report on the Player-Identification-Scale, and the effect was strongest in participants that completed the self-relevance block first. In those participants, the degree of subjectively experienced that self-relevance was associated with improvement in stopping performance over the course of the experiment. These results indicate that increasing the degree to which people identify with a cognitive task may induce them to exert greater, reactive inhibitory control. Consequently, self-relevant avatars may be used when an increase in commitment is desirable such as in therapeutic or training settings.

Environment and body-brain interplay affect inhibition and decision-making

The fine-tuned interplay of brain and body underlies human ability to cope with changes in the internal and external milieus. Previous research showed that cardiac interoceptive changes (e.g., cardiac phase) affect cognitive functions, notably inhibition that is a key element for adaptive behaviour. Here we investigated the influence on cognition of vestibular signal, which provides the brain with sensory information about body position and movement. We used a centrifuge-based design to disrupt vestibular signal in healthy human volunteers while their inhibition and decision-making functions were assessed with the stop-signal paradigm. Participants performed the standard and a novel, sensorial version of the stop-signal task to determine whether disrupted vestibular signal influences cognition as a function of its relevance to the context. First, we showed that disrupted vestibular signal was associated with a larger variability of longest inhibition latencies, meaning that participants were even slower to inhibit in the trials where they had the most difficulty inhibiting. Second, we revealed that processing of bodily information, as required in the sensorial stop-signal task, also led to a larger variability of longest inhibition latencies, which was all the more important when vestibular signal was disrupted. Lastly, we found that such a degraded response inhibition performance was due in part to the acceleration of decision-making process, meaning that participants made a decision more quickly even when strength of sensory evidence was reduced. Taken together, these novel findings provide direct evidence that vestibular signal affects the cognitive functions of inhibition and decision-making.

The Role of Conjunctive Representations in Stopping Actions

Action selection appears to rely on conjunctive representations that nonlinearly integrate task-relevant features. Here, we tested a corollary of this hypothesis: that such representations are also intricately involved during attempts to stop an action—a key aspect of action regulation. We tracked both conjunctive representations and those of constituent rule, stimulus, or response features through trial-by-trial representational similarity analysis of the electroencephalogram signal in a combined rule-selection and stop-signal paradigm. Across two experiments with student participants (N = 57), we found (a) that the strength of decoded conjunctive representations prior to the stop signal uniquely predicted trial-by-trial stopping success (Experiment 1) and (b) that these representations were selectively suppressed following the onset of the stop signal (Experiments 1 and 2). We conclude that conjunctive representations are key to successful action execution and therefore need to be suppressed when an intended action is no longer appropriate.

OSARI, an Open-Source Anticipated Response Inhibition Task

The stop-signal paradigm has become ubiquitous in investigations of inhibitory control. Tasks inspired by the paradigm, referred to as stop-signal tasks, require participants to make responses on go trials and to inhibit those responses when presented with a stop-signal on stop trials. Currently, the most popular version of the stop-signal task is the ‘choice-reaction’ variant, where participants make choice responses, but must inhibit those responses when presented with a stop-signal. An alternative to the choice-reaction variant of the stop-signal task is the ‘anticipated response inhibition’ task. In anticipated response inhibition tasks, participants are required to make a planned response that coincides with a predictably timed event (such as lifting a finger from a computer key to stop a filling bar at a predefined target). Anticipated response inhibition tasks have some advantages over the more traditional choice-reaction stop-signal tasks and are becoming increasingly popular. However, currently, there are no openly available versions of the anticipated response inhibition task, limiting potential uptake. Here, we present an open-source, free, and ready-to-use version of the anticipated response inhibition task, which we refer to as the OSARI (the Open-Source Anticipated Response Inhibition) task.

The role of dorsal premotor cortex in joint action stopping

SummaryHuman sensorimotor interaction requires mutual behavioral adaptation as well as shared cognitive task representations (Joint Action, JA). Yet, an under-investigated aspect of JA is the neurobehavioral mechanisms employed to stop actions if the context calls for it. Sparse evidence points to the possible contribution of the left dorsal premotor cortex (lPMd) in sculpting movements according to the socio-interactive context. To clarify this issue, we ran two experiments integrating a classical stop signal paradigm with an ecological JA task. The first behavioral study shows longer Stop performance in the JA condition. In the second, we use transcranial magnetic stimulation to inhibit the lPMd or a control site (vertex). Results show that lPMd modulates the JA stopping performance. Action stopping is an important component of JA coordination, and here we provide evidence that lPMd is a key node of a brain network recruited for online mutual co-adaptation in social contexts.