Release the BEESTS: Bayesian Estimation of Ex-Gaussian STop-Signal reaction time distributions

Unravelling the dynamics of response force: Investigating motor restraint and motor cancellation through go/no-go and stop-signal tasks

Prior research has found that the go/no-go (GNG) task primarily reflects participants' motor-restraint process, while the stop-signal task (SST) primarily represents participants' motor-cancellation process. However, traditional binary keyboards used in these experiments are unable to capture the subtleties of sub-threshold response-force dynamics. This has led to the neglect of potential sub-threshold motor-inhibition processes. In two experiments, we explored sub-threshold inhibition by using a custom force-sensitive keyboard to record response force in both GNG and SST. In experiment 1, participants displayed increased response force when correctly rejecting no-go targets in the GNG task compared to the baseline. In addition, they exhibited higher response force in hit trials than in false alarms, revealing engagement of both motor-restraint and motor-cancellation processes in GNG. Initially, participants utilised motor restraint, but if it failed to prevent inappropriate responses, they employed motor cancellation to stop responses before reaching the keypress threshold. In experiment 2, we used participants' average response-force amplitude and response-force latency in SST stop trials to characterise the motor-cancellation process. Average amplitude significantly predicted false-alarm rates in the GNG task, but the relationship between response latency and false-alarm rates was insignificant. We hypothesised that response latency reflects reactive inhibition control in motor cancellation, whereas average amplitude indicates proactive inhibition control. Our findings underscore the complexity of motor inhibition.

Failing to attend versus failing to stop: Single-trial decomposition of action-stopping in the stop signal task

The capacity to stop impending or ongoing actions contributes to executive control over behavior. Action-stopping, however, is difficult to directly quantify. It is therefore assayed via computational modeling of behavior in the stop signal task to estimate the latency of stopping (stop signal reaction time, SSRT) and, more recently, the reliability of stopping in terms of the distribution of SSRTs (standard deviation, SD-SSRT) and the frequency with which one outright fails to react to a stop signal (trigger failures, TF). Critically, the validity of computational estimates remains unknown because we currently have no direct readouts of behavior against which to compare them. Here, we developed a method for providing single-trial behavioral readouts of SSRT and trigger failures. The method relies on an adaptation of the stop signal task in which participants respond by moving a computer mouse. In two online experiments, we used movement kinematics to quantify stopping performance (SSRT, SD-SSRT, and TF), and then applied the standard Race Model and recent BEESTS model in order to examine the convergent validity of the methods. Overall, we demonstrate good correspondence between kinematics- and model-based estimates of stopping performance at the group and individual level. We conclude that the new method provides valid estimates of stopping performance that, unlike model-based estimates, can be read out at the level of single trials. Our approach might therefore be useful for interrogating single-trial neurophysiological correlates of stopping and for large-scale, online studies of behavioral stopping.

Proactive cues facilitate faster action reprogramming, but not stopping, in a response-selective stop signal task

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 (interference effect) 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 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.

No effects of 1 Hz offline TMS on performance in the stop-signal game

Stopping an already initiated action is crucial for human everyday behavior and empirical evidence points toward the prefrontal cortex playing a key role in response inhibition. Two regions that have been consistently implicated in response inhibition are the right inferior frontal gyrus (IFG) and the more superior region of the dorsolateral prefrontal cortex (DLPFC). The present study investigated the effect of offline 1 Hz transcranial magnetic stimulation (TMS) over the right IFG and DLPFC on performance in a gamified stop-signal task (SSG). We hypothesized that perturbing each area would decrease performance in the SSG, albeit with a quantitative difference in the performance decrease after stimulation. After offline TMS, functional short-term reorganization is possible, and the domain-general area (i.e., the right DLPFC) might be able to compensate for the perturbation of the domain-specific area (i.e., the right IFG). Results showed that 1 Hz offline TMS over the right DLPFC and the right IFG at 110% intensity of the resting motor threshold had no effect on performance in the SSG. In fact, evidence in favor of the null hypothesis was found. One intriguing interpretation of this result is that within-network compensation was triggered, canceling out the potential TMS effects as has been suggested in recent theorizing on TMS effects, although the presented results do not unambiguously identify such compensatory mechanisms. Future studies may result in further support for this hypothesis, which is especially important when studying reactive response in complex environments.

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.

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.

Partial response electromyography as a marker of action stopping

Response inhibition is among the core constructs of cognitive control. It is notoriously difficult to quantify from overt behavior, since the outcome of successful inhibition is the lack of a behavioral response. Currently, the most common measure of action stopping, and by proxy response inhibition, is the model-based stop signal reaction time (SSRT) derived from the stop signal task. Recently, partial response electromyography (prEMG) has been introduced as a complementary physiological measure to capture individual stopping latencies. PrEMG refers to muscle activity initiated by the go signal that plummets after the stop signal before its accumulation to a full response. Whereas neither the SSRT nor the prEMG is an unambiguous marker for neural processes underlying response inhibition, our analysis indicates that the prEMG peak latency is better suited to investigate brain mechanisms of action stopping. This study is a methodological resource with a comprehensive overview of the psychometric properties of the prEMG in a stop signal task, and further provides practical tips for data collection and analysis.

Dynamic targeting enables domain-general inhibitory control over action and thought by the prefrontal cortex

Over the last two decades, inhibitory control has featured prominently in accounts of how humans and other organisms regulate their behaviour and thought. Previous work on how the brain stops actions and thoughts, however, has emphasised distinct prefrontal regions supporting these functions, suggesting domain-specific mechanisms. Here we show that stopping actions and thoughts recruits common regions in the right dorsolateral and ventrolateral prefrontal cortex to suppress diverse content, via dynamic targeting. Within each region, classifiers trained to distinguish action-stopping from action-execution also identify when people are suppressing their thoughts (and vice versa). Effective connectivity analysis reveals that both prefrontal regions contribute to action and thought stopping by targeting the motor cortex or the hippocampus, depending on the goal, to suppress their task-specific activity. These findings support the existence of a domain-general system that underlies inhibitory control and establish Dynamic Targeting as a mechanism enabling this ability.

D2-Like Receptor Expression in the Hippocampus and Amygdala Informs Performance on the Stop-Signal Task in Parkinson's Disease

The stop-signal task is a well-established assessment of response inhibition, and in humans, proficiency is linked to dorsal striatum D2 receptor availability. Parkinson's disease (PD) is characterized by changes to efficiency of response inhibition. Here, we studied 17 PD patients (6 female and 11 male) using the stop-signal paradigm in a single-blinded d-amphetamine (dAMPH) study. Participants completed [18F]fallypride positron emission topography (PET) imaging in both placebo and dAMPH conditions. A voxel-wise analysis of the relationship between binding potential (BPND) and stop-signal reaction time (SSRT) revealed that faster SSRT is associated with greater D2-like BPND in the amygdala and hippocampus (right cluster qFDR-corr = 0.026, left cluster qFDR-corr = 0.002). A region of interest (ROI) examination confirmed this association in both the amygdala (coefficient = -48.26, p = 0.005) and hippocampus (coefficient = -104.94, p = 0.007). As healthy dopaminergic systems in the dorsal striatum appear to regulate response inhibition, we interpret our findings in PD to indicate either nigrostriatal damage unmasking a mesolimbic contribution to response inhibition, or a compensatory adaptation from the limbic and mesial temporal dopamine systems. These novel results expand the conceptualization of action-control networks, whereby limbic and motor loops may be functionally connected.SIGNIFICANCE STATEMENT While Parkinson's disease (PD) is characteristically recognized for its motor symptoms, some patients develop impulsive and compulsive behaviors (ICBs), manifested as repetitive and excessive participation in reward-driven activities, including sex, gambling, shopping, eating, and hobbyism. Such cognitive alterations compel a consideration of response inhibition in PD. To investigate inhibitory control and assess the brain regions that may participate, we assessed PD patients using a single-blinded d-amphetamine (dAMPH) study, with [18F]fallypride positron emission topography (PET) imaging, and stop-signal task performance. We find a negative relationship between D2-like binding in the mesial temporal region and top-signal reaction time (SSRT), with greater BPND associated with a faster SSRT. These discoveries indicate a novel role for mesolimbic dopamine in response inhibition, and advocate for limbic regulation of action control in this clinical population.

Common and Unique Inhibitory Control Signatures of Action-Stopping and Attentional Capture Suggest That Actions Are Stopped in Two Stages

The ability to stop an already initiated action is paramount to adaptive behavior. Much scientific debate in the field of human action-stopping currently focuses on two interrelated questions. (1) Which cognitive and neural processes uniquely underpin the implementation of inhibitory control when actions are stopped after explicit stop signals, and which processes are instead commonly evoked by all salient signals, even those that do not require stopping? (2) Why do purported (neuro)physiological signatures of inhibition occur at two different latencies after stop signals? Here, we address both questions via two preregistered experiments that combined measurements of corticospinal excitability, EMG, and whole-scalp EEG. Adult human subjects performed a stop signal task that also contained "ignore" signals: equally salient signals that did not require stopping but rather completion of the Go response. We found that both stop- and ignore signals produced equal amounts of early-latency inhibition of corticospinal excitability and EMG, which took place ∼150 ms following either signal. Multivariate pattern analysis of the whole-scalp EEG data further corroborated that this early processing stage was shared between stop- and ignore signals, as neural activity following the two signals could not be decoded from each other until a later time period. In this later period, unique activity related to stop signals emerged at frontocentral scalp sites, reflecting an increased stop signal P3. These findings suggest a two-step model of action-stopping, according to which an initial, universal inhibitory response to the saliency of the stop signal is followed by a slower process that is unique to outright stopping.SIGNIFICANCE STATEMENT Humans often have to stop their ongoing actions when indicated by environmental stimuli (stop signals). Successful action-stopping requires both the ability to detect these salient stop signals and to subsequently inhibit ongoing motor programs. Because of this tight entanglement of attentional control and motor inhibition, identifying unique neurophysiological signatures of action-stopping is difficult. Indeed, we report that recently proposed early-latency signatures of motor inhibition during action-stopping are also found after salient signals that do not require stopping. However, using multivariate pattern analysis of scalp-recorded neural data, we also identified subsequent neural activity that uniquely distinguished action-stopping from saliency detection. These results suggest that actions are stopped in two stages: the first common to all salient events and the second unique to action-stopping.

A Bayesian Mixture Modelling of Stop Signal Reaction Time Distributions: The Second Contextual Solution for the Problem of Aftereffects of Inhibition on SSRT Estimations

The distribution of single Stop Signal Reaction Times (SSRT) in the stop signal task (SST) has been modelled with two general methods: a nonparametric method by Hans Colonius (1990) and a Bayesian parametric method by Dora Matzke, Gordon Logan and colleagues (2013). These methods assume an equal impact of the preceding trial type (go/stop) in the SST trials on the SSRT distributional estimation without addressing the relaxed assumption. This study presents the required model by considering a two-state mixture model for the SSRT distribution. It then compares the Bayesian parametric single SSRT and mixture SSRT distributions in the usual stochastic order at the individual and the population level under ex-Gaussian (ExG) distributional format. It shows that compared to a single SSRT distribution, the mixture SSRT distribution is more varied, more positively skewed, more leptokurtic and larger in stochastic order. The size of the results' disparities also depends on the choice of weights in the mixture SSRT distribution. This study confirms that mixture SSRT indices as a constant or distribution are significantly larger than their single SSRT counterparts in the related order. This result offers a vital improvement in the SSRT estimations.

Understanding and Supporting Inhibitory Control: Unique Contributions From Proactive Monitoring and Motoric Stopping to Children's Improvements With Practice

Children struggle to stop inappropriate behaviors. What interventions improve inhibitory control, for whom, and why? Prior work suggested that practice proactively monitoring for relevant signals improved children's inhibitory control more than practice with motoric stopping. However, these processes were not clearly dissociated. This study tested 162 seven- to nine-year-old children (89 female, 72 male, 1 unreported; 82% White) on the stop-signal task, following monitoring or stopping-focused practice. Both methods improved inhibitory control, supported generalization, and interacted ( η p 2  = .20-.73). Practice approaches differentially impacted variability ( η p 2  = .01-.09). Only monitoring benefits showed signs of depending upon proactive control ( η p 2  = .02). These findings highlight unique contributions of attentional and stopping processes to inhibitory control, suggesting possibilities for tailored interventions.

Early parenting intervention accelerates inhibitory control development among CPS-involved children in middle childhood: A randomized clinical trial

Children at risk for neglect or abuse are vulnerable to delays in inhibitory control development. Prior findings suggest that early parenting interventions that target parental sensitivity and responsiveness during infancy can improve executive function outcomes of high-risk children during preschool years; however, little is known about how persistent these gains are through middle childhood. Participants included 76 CPS-involved children who were randomly assigned to either the ABC intervention (N = 32) or the Developmental Education for Families (DEF) control intervention (N = 44), and 53 low-risk children. Children completed the Stop Signal Reaction Time (SSRT) paradigm at ages 8 and 10. Intervention group predicted performance on the SSRT at age 8 such that children who received the ABC intervention and children in the low-risk group performed significantly better than children who received the DEF intervention (ABC vs. DEF: Cohen's d = 0.92; low-risk group vs. DEF: d = 0.56). The performances of the ABC and the low-risk groups were not statistically different. There were no significant group differences in SSRT performance at age 10. These findings demonstrate that the ABC intervention has long-term beneficial effects on inhibitory control development in children with a history of early caregiving adversity. A video abstract of this article can be viewed at https://youtu.be/P9oLyfo7pYA.

Unexpected Sounds Nonselectively Inhibit Active Visual Stimulus Representations

The brain's capacity to process unexpected events is key to cognitive flexibility. The most well-known effect of unexpected events is the interruption of attentional engagement (distraction). We tested whether unexpected events interrupt attentional representations by activating a neural mechanism for inhibitory control. This mechanism is most well characterized within the motor system. However, recent work showed that it is automatically activated by unexpected events and can explain some of their nonmotor effects (e.g., on working memory representations). Here, human participants attended to lateralized flickering visual stimuli, producing steady-state visual evoked potentials (SSVEPs) in the scalp electroencephalogram. After unexpected sounds, the SSVEP was rapidly suppressed. Using a functional localizer (stop-signal) task and independent component analysis, we then identified a fronto-central EEG source whose activity indexes inhibitory motor control. Unexpected sounds in the SSVEP task also activated this source. Using single-trial analyses, we found that subcomponents of this source differentially relate to sound-induced SSVEP changes: While its N2 component predicted the subsequent suppression of the attended-stimulus SSVEP, the P3 component predicted the suppression of the SSVEP to the unattended stimulus. These results shed new light on the processes underlying fronto-central control signals and have implications for phenomena such as distraction and the attentional blink.

Changes in BOLD variability are linked to the development of variable response inhibition

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This is the first study to investigate the development of response inhibition focussing on variability.

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We examined intraindividual variability both in stopping latencies and the underlying neural circuitry.

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There were no developmental differences in mean response inhibition, yet clear differences in performance variability.

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This, in turn, was associated with developmental differences in brain signal variability.

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Behavioral and neural variability indices might be a more sensitive measure of developmental differences in inhibition.

Research on the development of response inhibition in humans has focused almost exclusively on average stopping performance. The development of intra-individual variability in stopping performance and its underlying neural circuitry has remained largely unstudied, even though understanding variability is of core importance for understanding development. In a total sample of 45 participants (19 children aged 10–12 years and 26 adults aged 18–26 years) of either sex we aimed to identify age-related changes in intra-individual response inhibition performance and its underlying brain signal variability. While there was no difference in average stopping performance between children and adults, stop signal latencies for the children were more variable. Further, brain signal variability during successful stopping was significantly higher in adults compared to children, especially in bilateral thalamus, but also across regions of the inhibition network. Finally, brain signal variability was significantly associated with stopping performance behavioral variability in adults. Together these results indicate that variability in stopping performance decreases, whereas neural variability in the inhibition network increases, from childhood to adulthood. Future work will need to assess whether developmental changes in neural variability drive those in behavioral variability. In sum, both, neural and behavioral variability indices might be a more sensitive measure of developmental differences in response inhibition compared to the standard average-based measurements.

Dissociation of Medial Frontal β-Bursts and Executive Control

The neural mechanisms of executive and motor control concern both basic researchers and clinicians. In human studies, preparation and cancellation of movements are accompanied by changes in the β-frequency band (15-29 Hz) of electroencephalogram (EEG). Previous studies with human participants performing stop signal (countermanding) tasks have described reduced frequency of transient β-bursts over sensorimotor cortical areas before movement initiation and increased β-bursting over medial frontal areas with movement cancellation. This modulation has been interpreted as contributing to the trial-by-trial control of behavior. We performed identical analyses of EEG recorded over the frontal lobe of macaque monkeys (one male, one female) performing a saccade countermanding task. While we replicate the occurrence and modulation of β-bursts associated with initiation and cancellation of saccades, we found that β-bursts occur too infrequently to account for the observed stopping behavior. We also found β-bursts were more common after errors, but their incidence was unrelated to response time (RT) adaptation. These results demonstrate the homology of this EEG signature between humans and macaques but raise questions about the current interpretation of β band functional significance.SIGNIFICANCE STATEMENT The finding of increased β-bursting over medial frontal cortex with movement cancellation in humans is difficult to reconcile with the finding of modulation too late to contribute to movement cancellation in medial frontal cortex of macaque monkeys. To obtain comparable measurement scales, we recorded electroencephalogram (EEG) over medial frontal cortex of macaques performing a stop signal (countermanding) task. We replicated the occurrence and modulation of β-bursts associated with the cancellation of movements, but we found that β-bursts occur too infrequently to account for observed stopping behavior. Unfortunately, this finding raises doubts whether β-bursts can be a causal mechanism of response inhibition, which impacts future applications in devices such as brain-machine interfaces.

The Specificity of Inhibitory Control Deficits in Post-Traumatic Stress Disorder: A Dissociation Between the Speed and Reliability of Stopping

Inhibitory control over thoughts, emotions, and actions is challenging for people with Post-Traumatic Stress Disorder (PTSD). Whether specific aspects of inhibitory control are differentially affected in PTSD remains an open question. Here we examined performance on two popular response inhibition tasks in 28 combat Veterans with PTSD and 27 control Veterans. We used a Hybrid variant that intermixed 75% Go trials, 12.5% NoGo trials, and 12.5% Stop trials. Parameters from an ex-Gaussian race model (Matzke et al., 2017) provided estimates of stopping speed (μ Stop) and stopping variability (τ Stop). Participants with PTSD had higher error rates on NoGo trials, replicating previous results. The estimated probability of "trigger failures" (failures to launch inhibitory control) on Stop trials was also higher in PTSD patients, suggesting that sustained attention was a common deficit in the two tasks. Stopping variability was also increased in participants with PTSD, which supports a difficulty with maintaining task goals. Conversely, stopping speed did not differ between patients and controls, suggesting that core inhibitory processes were intact. These results demonstrate a dissociation between the speed and reliability of motor response inhibition in PTSD, and suggest that top-down inhibitory control was deployed less consistently in participants with PTSD.

Efficacy of inhibitory control depends on procrastination and deceleration in saccade planning

A goal-directed flexible behavior warrants our ability to timely inhibit impending movements deemed inappropriate due to an abrupt change in the context. Race model of countermanding rapid saccadic eye movement posits a competition between a preparatory GO process and an inhibitory STOP process rising to reach a fixed threshold. Stop-signal response time (SSRT), which is the average time STOP takes to rise to the threshold, is widely used as a metric to assess the ability to revoke a movement. A reliable estimation of SSRT critically depends on the assumption of independence between GO and STOP process, which has been violated in many studies. In addition, the physiological correlate of stochastic rise of STOP process to a threshold remains unsubstantiated thus far. Here, we introduce a method to estimate the efficacy of inhibitory control on the premise of an alternative model that assumes deceleration of GO process following the stop-signal onset. The average reaction time increased exponentially with the increase in the maximum duration available to attenuate GO process by the stop-signal. Our method estimates saccade procrastination in anticipation of the stop-signal, and the rate of increase in attenuation on GO process. Unlike SSRT, these new metrics are independent of how the stopping performance varies with the delay between go- and stop-signal onsets. We reckon that these metrics together qualify to be considered as an efficient alternative to SSRT for the estimation of individuals' ability to countermand saccades, especially in cases when the assumptions of race model are no longer valid.

Non-selective inhibition of the motor system following unexpected and expected infrequent events

Motor inhibition is a key control mechanism that allows humans to rapidly adapt their actions in response to environmental events. One of the hallmark signatures of rapidly exerted, reactive motor inhibition is the non-selective suppression of cortico-spinal excitability (CSE): unexpected sensory stimuli lead to a suppression of CSE across the entire motor system, even in muscles that are inactive. Theories suggest that this reflects a fast, automatic, and broad engagement of inhibitory control, which facilitates behavioral adaptations to unexpected changes in the sensory environment. However, it is an open question whether such non-selective CSE suppression is truly due to the unexpected nature of the sensory event, or whether it is sufficient for an event to be merely infrequent (but not unexpected). Here, we report data from two experiments in which human subjects experienced both unexpected and expected infrequent events during a two-alternative forced-choice reaction time task while CSE was measured from a task-unrelated muscle. We found that expected infrequent events can indeed produce non-selective CSE suppression-but only when they occur during movement initiation. In contrast, unexpected infrequent events produce non-selective CSE suppression relative to frequent, expected events even in the absence of movement initiation. Moreover, CSE suppression due to unexpected events occurs at shorter latencies compared to expected infrequent events. These findings demonstrate that unexpectedness and stimulus infrequency have qualitatively different suppressive effects on the motor system. They also have key implications for studies that seek to disentangle neural and psychological processes related to motor inhibition and stimulus detection.

β-Bursts Reveal the Trial-to-Trial Dynamics of Movement Initiation and Cancellation

The neurophysiological basis of motor control is of substantial interest to basic researchers and clinicians alike. Motor processes are accompanied by prominent field potential changes in the β-frequency band (15-29 Hz): in trial-averages, movement initiation is accompanied by β-band desynchronization over sensorimotor areas, whereas movement cancellation is accompanied by β-power increases over (pre)frontal areas. However, averaging misrepresents the true nature of the β-signal. Unaveraged β-band activity is characterized by short-lasting, burst-like events, rather than by steady modulations. Therefore, averaging-based quantifications may miss important brain-behavior relationships. To investigate how β-bursts relate to movement in male and female humans (N = 234), we investigated scalp-recorded β-band activity during the stop-signal task, which operationalizes both movement initiation and cancellation. Both processes were indexed by systematic spatiotemporal changes in β-burst rates. Before movement initiation, β-bursting was prominent at bilateral sensorimotor sites. These burst-rates predicted reaction time (a relationship that was absent in trial-average data), suggesting that sensorimotor β-bursting signifies an inhibited motor system, which has to be overcome to initiate movements. Indeed, during movement initiation, sensorimotor burst-rates steadily decreased, lateralizing just before movement execution. In contrast, successful movement cancellation was signified by increased phasic β-bursting over fronto-central sites. Such β-bursts were followed by short-latency increases of bilateral sensorimotor β-burst rates, suggesting that motor inhibition can be rapidly re-instantiated by frontal areas when movements have to be rapidly cancelled. Together, these findings suggest that β-bursting is a fundamental signature of the motor system, used by both sensorimotor and frontal areas involved in the trial-by-trial control of behavior.SIGNIFICANCE STATEMENT Movement-related β-frequency (15-29 Hz) changes are among the most prominent features of neural recordings across species, scales, and methods. However, standard averaging-based methods obscure the true dynamics of β-band activity, which is dominated by short-lived, burst-like events. Here, we demonstrate that both movement-initiation and cancellation in humans are characterized by unique trial-to-trial patterns of β-bursting. Movement initiation is characterized by steady reductions of β-bursting over bilateral sensorimotor sites. In contrast, during rapid movement cancellation, β-bursts first emerge over fronto-central sites typically associated with motor control, after which sensorimotor β-bursting re-initiates. These findings suggest a fundamentally novel, non-invasive measure of the neural interaction underlying movement-initiation and -cancellation, opening new avenues for the study of motor control in health and disease.

Dynamic models of choice

Parameter estimation in evidence-accumulation models of choice response times is demanding of both the data and the user. We outline how to fit evidence-accumulation models using the flexible, open-source, R-based Dynamic Models of Choice (DMC) software. DMC provides a hands-on introduction to the Bayesian implementation of two popular evidence-accumulation models: the diffusion decision model (DDM) and the linear ballistic accumulator (LBA). It enables individual and hierarchical estimation, as well as assessment of the quality of a model's parameter estimates and descriptive accuracy. First, we introduce the basic concepts of Bayesian parameter estimation, guiding the reader through a simple DDM analysis. We then illustrate the challenges of fitting evidence-accumulation models using a set of LBA analyses. We emphasize best practices in modeling and discuss the importance of parameter- and model-recovery simulations, exploring the strengths and weaknesses of models in different experimental designs and parameter regions. We also demonstrate how DMC can be used to model complex cognitive processes, using as an example a race model of the stop-signal paradigm, which is used to measure inhibitory ability. We illustrate the flexibility of DMC by extending this model to account for mixtures of cognitive processes resulting from attention failures. We then guide the reader through the practical details of a Bayesian hierarchical analysis, from specifying priors to obtaining posterior distributions that encapsulate what has been learned from the data. Finally, we illustrate how the Bayesian approach leads to a quantitatively cumulative science, showing how to use posterior distributions to specify priors that can be used to inform the analysis of future experiments.

Alpha Phase Synchronization of Parietal Areas Reflects Switch-Specific Activity During Mental Rotation: An EEG Study

Action selection is typically influenced by the history of previously selected actions (the immediate motor history), which is apparent when a selected action is switched from a previously selected one to a new one. This history dependency of the action selection is even observable during a mental hand rotation task. Thus, we hypothesized that the history-dependent interaction of actions might share the same neural mechanisms among different types of action switching tasks. An alternative hypothesis is that the history dependency of the mental hand rotation task might involve a distinctive neural mechanism from the general action selection tasks so that the reported observation with the mental hand rotation task in the previously published literature might lack generality. To refute this possibility, we compared neural activity during action switching in the mental hand rotation with the general action switching task which is triggered by a simple visual stimulus. In the experiment, to focus on temporal changes in whole brain oscillatory activity, we recorded electroencephalographic (EEG) signals while 25 healthy subjects performed the two tasks. For analysis, we examined functional connectivity reflected in EEG phase synchronization and analyzed temporal changes in brain activity when subjects switched from a previously selected action to a new action. Using a clustering-based method to identify functional connectivity reflected in time-varying phase synchronization, we identified alpha-power inter-parietal synchronization that appears only during switching of the selected action, regardless of the hand laterality in the presented image. Moreover, the current study revealed that for both tasks the extent of this alpha-power inter-parietal synchronization was altered by the history of the selected actions. These findings suggest that alpha-power inter-parietal synchronization is engaged as a form of switching-specific functional connectivity, and that switching-related activity is independent of the task paradigm.

Revealing Neurocomputational Mechanisms of Reinforcement Learning and Decision-Making With the hBayesDM Package

Reinforcement learning and decision-making (RLDM) provide a quantitative framework and computational theories with which we can disentangle psychiatric conditions into the basic dimensions of neurocognitive functioning. RLDM offer a novel approach to assessing and potentially diagnosing psychiatric patients, and there is growing enthusiasm for both RLDM and computational psychiatry among clinical researchers. Such a framework can also provide insights into the brain substrates of particular RLDM processes, as exemplified by model-based analysis of data from functional magnetic resonance imaging (fMRI) or electroencephalography (EEG). However, researchers often find the approach too technical and have difficulty adopting it for their research. Thus, a critical need remains to develop a user-friendly tool for the wide dissemination of computational psychiatric methods. We introduce an R package called hBayesDM (hierarchical Bayesian modeling of Decision-Making tasks), which offers computational modeling of an array of RLDM tasks and social exchange games. The hBayesDM package offers state-of-the-art hierarchical Bayesian modeling, in which both individual and group parameters (i.e., posterior distributions) are estimated simultaneously in a mutually constraining fashion. At the same time, the package is extremely user-friendly: users can perform computational modeling, output visualization, and Bayesian model comparisons, each with a single line of coding. Users can also extract the trial-by-trial latent variables (e.g., prediction errors) required for model-based fMRI/EEG. With the hBayesDM package, we anticipate that anyone with minimal knowledge of programming can take advantage of cutting-edge computational-modeling approaches to investigate the underlying processes of and interactions between multiple decision-making (e.g., goal-directed, habitual, and Pavlovian) systems. In this way, we expect that the hBayesDM package will contribute to the dissemination of advanced modeling approaches and enable a wide range of researchers to easily perform computational psychiatric research within different populations.

Delta plots do not reveal response inhibition in lying

The role of response inhibition in lying is debated. By using the delta-plot method applied to the Sheffield Lie Test, Debey, Ridderinkhof, De Houwer, De Schryver, and Verschuere (2015) provided evidence supporting the role of inhibition in lying. In the study of Debey et al., inhibitory skill was measured in terms of the size of the lie effect. However, to provide convincing evidence that delta plots highlight the role of response inhibition in lying, inhibitory ability must be evaluated independently from the size of the lie effect. After replicating original findings, this article shows that a delta plot analysis does not differentiate individuals with different inhibitory abilities, when inhibitory skill is measured by means of the Stop Signal Task, instead of the size of the lie effect. This suggests that researchers should be cautious when making conclusions about cognitive mechanisms based on the sole analysis of delta plots.

A Bayesian approach for estimating the probability of trigger failures in the stop-signal paradigm

Response inhibition is frequently investigated using the stop-signal paradigm, where participants perform a two-choice response time task that is occasionally interrupted by a stop signal instructing them to withhold their response. Stop-signal performance is formalized as a race between a go and a stop process. If the go process wins, the response is executed; if the stop process wins, the response is inhibited. Successful inhibition requires fast stop responses and a high probability of triggering the stop process. Existing methods allow for the estimation of the latency of the stop response, but are unable to identify deficiencies in triggering the stop process. We introduce a Bayesian model that addresses this limitation and enables researchers to simultaneously estimate the probability of trigger failures and the entire distribution of stopping latencies. We demonstrate that trigger failures are clearly present in two previous studies, and that ignoring them distorts estimates of stopping latencies. The parameter estimation routine is implemented in the BEESTS software (Matzke et al., Front. Quantitative Psych. Measurement, 4, 918; 2013a) and is available at http://dora.erbe-matzke.com/software.html.

A test-retest reliability analysis of diffusion measures of white matter tracts relevant for cognitive control

Recent efforts to replicate structural brain-behavior correlations have called into question the replicability of structural brain measures used in cognitive neuroscience. Here, we report an evaluation of test-retest reliability of diffusion tensor imaging (DTI) measures, including fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity, in several white matter tracts previously shown to be involved in cognitive control. In a data set consisting of 34 healthy participants scanned twice on a single day, we observe overall stability of DTI measures. This stability remained in a subset of participants who were also scanned a third time on the same day as well as in a 2-week follow-up session. We conclude that DTI measures in these tracts show relative stability, and that alternative explanations for the recent failures of replication must be considered.

Failures of cognitive control or attention? The case of stop-signal deficits in schizophrenia

We used Bayesian cognitive modelling to identify the underlying causes of apparent inhibitory deficits in the stop-signal paradigm. The analysis was applied to stop-signal data reported by Badcock et al. (Psychological Medicine 32: 87-297, 2002) and Hughes et al. (Biological Psychology 89: 220-231, 2012), where schizophrenia patients and control participants made rapid choice responses, but on some trials were signalled to stop their ongoing response. Previous research has assumed an inhibitory deficit in schizophrenia, because estimates of the mean time taken to react to the stop signal are longer in patients than controls. We showed that these longer estimates are partly due to failing to react to the stop signal ("trigger failures") and partly due to a slower initiation of inhibition, implicating a failure of attention rather than a deficit in the inhibitory process itself. Correlations between the probability of trigger failures and event-related potentials reported by Hughes et al. are interpreted as supporting the attentional account of inhibitory deficits. Our results, and those of Matzke et al. (2016), who report that controls also display a substantial although lower trigger-failure rate, indicate that attentional factors need to be taken into account when interpreting results from the stop-signal paradigm.

More intelligent extraverts are more likely to deceive

The tendency to lie is a part of personality. But are personality traits the only factors that make some people lie more often than others? We propose that cognitive abilities have equal importance. People with higher cognitive abilities are better, and thus more effective liars. This might reinforce using lies to solve problems. Yet, there is no empirical research that shows this relationship in healthy adults. Here we present three studies in which the participants had free choice about their honesty. We related differences in cognitive abilities and personality to the odds of lying. Results show that personality and intelligence are both important. People low on agreeableness and intelligent extraverts are most likely to lie. This suggests that intelligence might mediate the relationship between personality traits and lying frequency. While personality traits set general behavioral tendencies, intelligence and environment set boundaries.

A Roadmap for the Development of Applied Computational Psychiatry

BACKGROUND

Computational psychiatry is a burgeoning field that utilizes mathematical approaches to investigate psychiatric disorders, derive quantitative predictions, and integrate data across multiple levels of description. Computational psychiatry has already led to many new insights into the neurobehavioral mechanisms that underlie several psychiatric disorders, but its usefulness from a clinical standpoint is only now starting to be considered.

METHODS

Examples of computational psychiatry are highlighted, and a phase-based pipeline for the development of clinical computational-psychiatry applications is proposed, similar to the phase-based pipeline used in drug development. It is proposed that each phase has unique endpoints and deliverables, which will be important milestones to move tasks, procedures, computational models, and algorithms from the laboratory to clinical practice.

RESULTS

Application of computational approaches should be tested on healthy volunteers in Phase I, transitioned to target populations in Phase IB and Phase IIA, and thoroughly evaluated using randomized clinical trials in Phase IIB and Phase III. Successful completion of these phases should be the basis of determining whether computational models are useful tools for prognosis, diagnosis, or treatment of psychiatric patients.

CONCLUSIONS

A new type of infrastructure will be necessary to implement the proposed pipeline. This infrastructure should consist of groups of investigators with diverse backgrounds collaborating to make computational psychiatry relevant for the clinic.

Response inhibition and response monitoring in a saccadic double-step task in schizophrenia

BACKGROUND

Cognitive control impairments are linked to functional outcome in schizophrenia. The goal of the current study was to investigate precise abnormalities in two aspects of cognitive control: reactively changing a prepared response, and monitoring performance and adjusting behavior accordingly. We adapted an oculomotor task from neurophysiological studies of the cellular basis of cognitive control in nonhuman primates.

METHODS

16 medicated outpatients with schizophrenia (SZ) and 18 demographically-matched healthy controls performed the modified double-step task. In this task, participants were required to make a saccade to a visual target. Infrequently, the target jumped to a new location and participants were instructed to rapidly inhibit and change their response. A race model provided an estimate of the time needed to cancel a planned movement. Response monitoring was assessed by measuring reaction time (RT) adjustments based on trial history.

RESULTS

SZ patients had normal visually-guided saccadic RTs but required more time to switch the response to the new target location. Additionally, the estimated latency of inhibition was longer in patients and related to employment. Finally, although both groups slowed down on trials that required inhibiting and changing a response, patients showed exaggerated performance-based adjustments in RTs, which was correlated with positive symptom severity.

CONCLUSIONS

SZ patients have impairments in rapidly inhibiting eye movements and show idiosyncratic response monitoring. These results are consistent with functional abnormalities in a network involving cortical oculomotor regions, the superior colliculus, and basal ganglia, as described in neurophysiological studies of non-human primates using an identical paradigm, and provide a translational bridge for understanding cognitive symptoms of SZ.

The practice of going helps children to stop: the importance of context monitoring in inhibitory control

How do we stop ourselves during ongoing action? Recent work implies that stopping per se is easy given sufficient monitoring of contextual cues signaling the need to change action. We test key implications of this idea for improving inhibitory control. Seven- to 9-year-old children practiced stopping an ongoing action or monitoring for cues that signaled the need to go again. Both groups subsequently showed better response inhibition in a Stop-Signal task than active controls, and practice monitoring yielded a dose-response relationship. When monitoring practice was optimized to occur while children engaged in responding, the greatest benefits were observed-even greater than from practicing stopping itself. These findings demonstrate the importance of monitoring processes in developing response inhibition and suggest promising new directions for interventions.

Eating to stop: tyrosine supplementation enhances inhibitory control but not response execution

Animal studies and research in humans have shown that the supplementation of tyrosine, or tyrosine-containing diets, increase the plasma tyrosine and enhance brain dopamine (DA). However, the strategy of administering tyrosine (and the role of DA therein) to enhance cognition is unclear and heavily debated. We studied, in a healthy population, whether tyrosine supplementation improves stopping overt responses, a core cognitive-control function. In a double-blind, placebo-controlled, within-subject design, one hour following the administration of tyrosine (corresponding to the beginning of the 1h-peak of the plasma concentration) or placebo, participants performed a stop-signal task-which taps into response inhibition and response execution speed. Participants in the Tyrosine condition were more efficient in inhibiting unwanted action tendencies but not in reacting to go signals. This is the first demonstration that the supplementation of tyrosine selectively targets, and reliably improves the ability to stop overt responses.