Bayesian parametric estimation of stop-signal reaction time distributions

EZ-CDM: Fast, simple, robust, and accurate estimation of circular diffusion model parameters

The investigation of cognitive processes that form the basis of decision-making in paradigms involving continuous outcomes has gained the interest of modeling researchers who aim to develop a dynamic decision theory that accounts for both speed and accuracy. One of the most important of these continuous models is the circular diffusion model (CDM, Smith. Psychological Review, 123(4), 425. 2016), which posits a noisy accumulation process mathematically described as a stochastic two-dimensional Wiener process inside a disk. Despite the considerable benefits of this model, its mathematical intricacy has limited its utilization among scholars. Here, we propose a straightforward and user-friendly method for estimating the CDM parameters and fitting the model to continuous-scale data using simple formulas that can be readily computed and do not require theoretical knowledge of model fitting or extensive programming. Notwithstanding its simplicity, we demonstrate that the aforementioned method performs with a level of accuracy that is comparable to that of the maximum likelihood estimation method. Furthermore, a robust version of the method is presented, which maintains its simplicity while exhibiting a high degree of resistance to contaminant responses. Additionally, we show that the approach is capable of reliably measuring the key parameters of the CDM, even when these values are subject to across-trial variability. Finally, we demonstrate the practical application of the method on experimental data. Specifically, an illustrative example is presented wherein the method is employed along with estimating the probability of guessing. It is hoped that the straightforward methodology presented here will, on the one hand, help narrow the divide between theoretical constructs and empirical observations on continuous response tasks and, on the other hand, inspire cognitive psychology researchers to shift their laboratory investigations towards continuous response paradigms.

Are there jumps in evidence accumulation, and what, if anything, do they reflect psychologically? An analysis of Lévy Flights models of decision-making

According to existing theories of simple decision-making, decisions are initiated by continuously sampling and accumulating perceptual evidence until a threshold value has been reached. Many models, such as the diffusion decision model, assume a noisy accumulation process, described mathematically as a stochastic Wiener process with Gaussian distributed noise. Recently, an alternative account of decision-making has been proposed in the Lévy Flights (LF) model, in which accumulation noise is characterized by a heavy-tailed power-law distribution, controlled by a parameter, [Formula: see text]. The LF model produces sudden large "jumps" in evidence accumulation that are not produced by the standard Wiener diffusion model, which some have argued provide better fits to data. It remains unclear, however, whether jumps in evidence accumulation have any real psychological meaning. Here, we investigate the conjecture by Voss et al. (Psychonomic Bulletin & Review, 26(3), 813-832, 2019) that jumps might reflect sudden shifts in the source of evidence people rely on to make decisions. We reason that if jumps are psychologically real, we should observe systematic reductions in jumps as people become more practiced with a task (i.e., as people converge on a stable decision strategy with experience). We fitted five versions of the LF model to behavioral data from a study by Evans and Brown (Psychonomic Bulletin & Review, 24(2), 597-606, 2017), using a five-layer deep inference neural network for parameter estimation. The analysis revealed systematic reductions in jumps as a function of practice, such that the LF model more closely approximated the standard Wiener model over time. This trend could not be attributed to other sources of parameter variability, speaking against the possibility of trade-offs with other model parameters. Our analysis suggests that jumps in the LF model might be capturing strategy instability exhibited by relatively inexperienced observers early on in task performance. We conclude that further investigation of a potential psychological interpretation of jumps in evidence accumulation is warranted.

Effects of transcranial magnetic stimulation on reactive response inhibition

Reactive response inhibition cancels impending actions to enable adaptive behavior in ever-changing environments and has wide neuropsychiatric implications. A canonical paradigm to measure the covert inhibition latency is the stop-signal task (SST). To probe the cortico-subcortical network underlying motor inhibition, transcranial magnetic stimulation (TMS) has been applied over central nodes to modulate SST performance, especially to the right inferior frontal cortex and the presupplementary motor area. Since the vast parameter spaces of SST and TMS enabled diverse implementations, the insights delivered by emerging TMS-SST studies remain inconclusive. Therefore, a systematic review was conducted to account for variability and synthesize converging evidence. Results indicate certain protocol specificity through the consistent perturbations induced by online TMS, whereas offline protocols show paradoxical effects on different target regions besides numerous null effects. Ancillary neuroimaging findings have verified and dissociated the underpinning network dynamics. Sources of heterogeneity in designs and risk of bias are highlighted. Finally, we outline best-practice recommendations to bridge methodological gaps and subserve the validity as well as replicability of future work.

A hybrid approach to dynamic cognitive psychometrics : 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.

Transcranial focused ultrasound to human rIFG improves response inhibition through modulation of the P300 onset latency

Response inhibition in humans is important to avoid undesirable behavioral action consequences. Neuroimaging and lesion studies point to a locus of inhibitory control in the right inferior frontal gyrus (rIFG). Electrophysiology studies have implicated a downstream event-related potential from rIFG, the fronto-central P300, as a putative neural marker of the success and timing of inhibition over behavioral responses. However, it remains to be established whether rIFG effectively drives inhibition and which aspect of P300 activity uniquely indexes inhibitory control-ERP timing or amplitude. Here, we dissect the connection between rIFG and P300 for inhibition by using transcranial-focused ultrasound (tFUS) to target rIFG of human subjects while they performed a Stop-Signal task. By applying tFUS simultaneously with different task events, we found behavioral inhibition was improved, but only when applied to rIFG simultaneously with a 'stop' signal. Improved inhibition through tFUS to rIFG was indexed by faster stopping times that aligned with significantly shorter N200/P300 onset latencies. In contrast, P300 amplitude was modulated during tFUS across all groups without a paired change in behavior. Using tFUS, we provide evidence for a causal connection between anatomy, behavior, and electrophysiology underlying response inhibition.

Intra-individual variability adaptively increases following inhibition training during middle childhood

There is ongoing debate on the relationship between intra-individual variability (IIV) of cognitive processes and task performance. While psychological research has traditionally assumed that lower intra-individual variability (IIV) aids consistent task performance, some studies suggest that greater IIV can also be adaptive, especially when flexible responding is required. Here we selectively manipulate inhibitory control (Stopping) and response speed (Going) by means of a training paradigm to 1) assess how this manipulation impacts Stopping IIV and its relationship to task performance, and 2) replicate previous findings showing that reductions in Going IIV are adaptive. A group of 208 6-13-year-old children were randomly allocated to an 8-week training targeting Stopping (experimental group) or Going (control group). The stop signal task was administered before and after training. Training Stopping led to adaptive increases in Stopping IIV, where greater flexibility in cognitive processing may be required to meet higher task demands. In line with previous studies, training Going led to adaptive reductions in Going IIV, which allows more consistent and efficient Going performance. These findings provide systematic and causal evidence of the process-dependent relationship of IIV and task performance in the context of Stopping and Going, suggesting a more nuanced perspective on IIV with implications for developmental, ageing and intervention studies.

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.

The role of inhibitory control in sport performance: Systematic review and meta-analysis in stop-signal paradigm

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.

A cognitive process modeling framework for 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 study's 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 cognitive process modeling framework, the RDEX-ABCD model, that provides a parsimonious explanation for the impact of this design feature on "go" stimulus processing and successfully accounts for key behavioral trends in the ABCD data. Simulation studies using this model suggest that failing to account for the context independence violations in the ABCD design can lead to erroneous inferences in several realistic scenarios. However, we demonstrate that RDEX-ABCD effectively addresses these violations and can be used to accurately measure SSRT along with an array of additional mechanistic parameters of interest (e.g., attention to the stop signal, cognitive efficiency), advancing investigators' ability to draw valid and nuanced inferences from ABCD data. AVAILABILITY OF DATA AND MATERIALS: Data from the ABCD Study are available through the NIH Data Archive (NDA): nda.nih.gov/abcd. Code for all analyses featured in this study is openly available on the Open Science Framework (OSF): osf.io/2h8a7/.

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.

Towards Conceptual Clarification of Proactive Inhibitory Control: A Review

The aim of this selective review paper is to clarify potential confusion when referring to the term proactive inhibitory control. Illustrated by a concise overview of the literature, we propose defining reactive inhibition as the mechanism underlying stopping an action. On a stop trial, the stop signal initiates the stopping process that races against the ongoing action-related process that is triggered by the go signal. Whichever processes finishes first determines the behavioral outcome of the race. That is, stopping is either successful or unsuccessful in that trial. Conversely, we propose using the term proactive inhibition to explicitly indicate preparatory processes engaged to bias the outcome of the race between stopping and going. More specifically, these proactive processes include either pre-amping the reactive inhibition system (biasing the efficiency of the stopping process) or presetting the action system (biasing the efficiency of the go process). We believe that this distinction helps meaningful comparisons between various outcome measures of proactive inhibitory control that are reported in the literature and extends to experimental research paradigms other than the stop task.

Longitudinal developmental trajectories of inhibition and white-matter maturation of the fronto-basal-ganglia circuits

Response inhibition refers to the cancelling of planned (or restraining of ongoing) actions and is required in much of our everyday life. Response inhibition appears to improve dramatically in early development and plateau in adolescence. The fronto-basal-ganglia network has long been shown to predict individual differences in the ability to enact response inhibition. In the current study, we examined whether developmental trajectories of fiber-specific white matter properties of the fronto-basal-ganglia network was predictive of parallel developmental trajectories of response inhibition. 138 children aged 9-14 completed the stop-signal task (SST). A subsample of 73 children underwent high-angular resolution diffusion MRI data for up to three time points. Performance on the SST was assessed using a parametric race modelling approach. White matter organization of the fronto-basal-ganglia circuit was estimated using fixel-based analysis. Contrary to predictions, we did not find any significant associations between maturational trajectories of fronto-basal-ganglia white matter and developmental improvements in SST performance. Findings suggest that the development of white matter organization of the fronto-basal-ganglia and development of stopping performance follow distinct maturational trajectories.

Mind Wandering Impedes Response Inhibition by Affecting the Triggering of the Inhibitory Process

Mind wandering is a state in which our mental focus shifts toward task-unrelated thoughts. Although it is known that mind wandering has a detrimental effect on concurrent task performance (e.g., decreased accuracy), its effect on executive functions is poorly studied. Yet the latter question is relevant to many real-world situations, such as rapid stopping during driving. Here, we studied how mind wandering would affect the requirement to subsequently stop an incipient motor response. In healthy adults, we tested whether mind wandering affected stopping and, if so, which component of stopping was affected: the triggering of the inhibitory brake or the implementation of the brake following triggering. We observed that during mind wandering, stopping latency increased, as did the percentage of trials with failed triggering. Indeed, 67% of the variance of the increase in stopping latency was explained by increased trigger failures. Thus, mind wandering primarily affects stopping by affecting the triggering of the brake.

The pre-supplementary motor area achieves inhibitory control by modulating response thresholds

The pre-supplementary motor area (pre-SMA) is central for the initiation and inhibition of voluntary action. For the execution of action, the pre-SMA optimises the decision of which action to choose by adjusting the thresholds for the required evidence for each choice. However, it remains unclear how the pre-SMA contributes to action inhibition. Here, we use computational modelling of a stop/no-go task, performed by an adult with a focal lesion in the pre-SMA, and 52 age-matched controls. We show that the patient required more time to successfully inhibit an action (longer stop-signal reaction time) but was faster in terms of go reaction times. Computational modelling revealed that the patient's failure to stop was explained by a significantly lower response threshold for initiating an action, as compared to controls, suggesting that the patient needed less evidence before committing to an action. A similarly specific impairment was also observed for the decision of which action to choose. Together, our results suggest that dynamic threshold modulation may be a general mechanism by which the pre-SMA exerts its control over voluntary action.

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.

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.

Right inferior frontal gyrus damage is associated with impaired 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 gyrus (rIFG) 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 rIFG lesion patients, non-rIFG lesion patients, and healthy comparisons. Furthermore, we measured scalp-EEG to detect β-bursts, a neurophysiological index of inhibitory control. rIFG lesion patients showed a more than fivefold increase in trigger failure trials and did not exhibit the typical increase of stop-related frontal β-bursts. However, on trials in which such β-bursts did occur, rIFG patients showed the typical subsequent upregulation of β over sensorimotor areas, indicating that their ability to implement inhibitory control, once triggered, remains intact. These findings suggest that the role of rIFG in inhibitory control has to be fundamentally reinterpreted.

Applying hierarchical bayesian modeling to experimental psychopathology data: An introduction and tutorial

Over the past 2 decades Bayesian methods have been gaining popularity in many scientific disciplines. However, to this date, they are rarely part of formal graduate statistical training in clinical science. Although Bayesian methods can be an attractive alternative to classical methods for answering certain research questions, they involve a heavy "overhead" (e.g., advanced mathematical methods, complex computations), which pose significant barriers to researchers interested in adding Bayesian methods to their statistical toolbox. To increase the accessibility of Bayesian methods for psychopathology researchers, this article presents a gentle introduction of the Bayesian inference framework and a tutorial on implementation. We first provide a primer on the key concepts of Bayesian inference and major implementation considerations related to Bayesian estimation. We then demonstrate how to apply hierarchical Bayesian modeling (HBM) to experimental psychopathology data. Using a real dataset collected from two clinical groups (schizophrenia and bipolar disorder) and a healthy comparison sample on a psychophysical gaze perception task, we illustrate how to model individual responses and group differences with probability functions respectful of the presumed underlying data-generating process and the hierarchical nature of the data. We provide the code with explanations and the data used to generate and visualize the results to facilitate learning. Finally, we discuss interpretation of the results in terms of posterior probabilities and compare the results with those obtained using a traditional method. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

The Pause-then-Cancel model of human action-stopping: Theoretical considerations and empirical evidence

The ability to stop already-initiated actions is a key cognitive control ability. Recent work on human action-stopping has been dominated by two controversial debates. First, the contributions (and neural signatures) of attentional orienting and motor inhibition after stop-signals are near-impossible to disentangle. Second, the timing of purportedly inhibitory (neuro)physiological activity after stop-signals has called into question which neural signatures reflect processes that actually contribute to action-stopping. Here, we propose that a two-stage model of action-stopping - proposed by Schmidt and Berke (2017) based on subcortical rodent recordings - may resolve these controversies. Translating this model to humans, we first argue that attentional orienting and motor inhibition are inseparable because orienting to salient events like stop-signals automatically invokes broad motor inhibition, reflecting a fast-acting, ubiquitous Pause process. We then argue that inhibitory signatures after stop-signals differ in latency because they map onto two sequential stages: the salience-related Pause and a slower, stop-specific Cancel process. We formulate the model, discuss recent supporting evidence in humans, and interpret existing data within its context.

Locus coeruleus integrity and the effect of atomoxetine on response inhibition in Parkinson's disease

Cognitive decline is a common feature of Parkinson's disease, and many of these cognitive deficits fail to respond to dopaminergic therapy. Therefore, targeting other neuromodulatory systems represents an important therapeutic strategy. Among these, the locus coeruleus-noradrenaline system has been extensively implicated in response inhibition deficits. Restoring noradrenaline levels using the noradrenergic reuptake inhibitor atomoxetine can improve response inhibition in some patients with Parkinson's disease, but there is considerable heterogeneity in treatment response. Accurately predicting the patients who would benefit from therapies targeting this neurotransmitter system remains a critical goal, in order to design the necessary clinical trials with stratified patient selection to establish the therapeutic potential of atomoxetine. Here, we test the hypothesis that integrity of the noradrenergic locus coeruleus explains the variation in improvement of response inhibition following atomoxetine. In a double-blind placebo-controlled randomized crossover design, 19 patients with Parkinson's disease completed an acute psychopharmacological challenge with 40 mg of oral atomoxetine or placebo. A stop-signal task was used to measure response inhibition, with stop-signal reaction times obtained through hierarchical Bayesian estimation of an ex-Gaussian race model. Twenty-six control subjects completed the same task without undergoing the drug manipulation. In a separate session, patients and controls underwent ultra-high field 7 T imaging of the locus coeruleus using a neuromelanin-sensitive magnetization transfer sequence. The principal result was that atomoxetine improved stop-signal reaction times in those patients with lower locus coeruleus integrity. This was in the context of a general impairment in response inhibition, as patients on placebo had longer stop-signal reaction times compared to controls. We also found that the caudal portion of the locus coeruleus showed the largest neuromelanin signal decrease in the patients compared to controls. Our results highlight a link between the integrity of the noradrenergic locus coeruleus and response inhibition in patients with Parkinson's disease. Furthermore, they demonstrate the importance of baseline noradrenergic state in determining the response to atomoxetine. We suggest that locus coeruleus neuromelanin imaging offers a marker of noradrenergic capacity that could be used to stratify patients in trials of noradrenergic therapy and to ultimately inform personalized treatment approaches.

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.

Face the (trigger) failure: Trigger failures strongly drive the effect of reward on response inhibition

Response inhibition is typically understood as the ability to stop inappropriate actions and is often investigated using the stop-signal task, in which a go response, triggered by a go signal, has to be inhibited upon the onset of a stop signal. In this task, response inhibition has been formalized as a race between a go and a stop process, which allows the latency of the stop process (stop-signal reaction time; SSRT) to be estimated. Yet, non-parametric SSRT estimations assume that the stop process is initiated without fail, which appears problematic as it is known that participants fail to do so on a subset of trials ("trigger failures"). Importantly, non-parametric methods systematically overestimate SSRT when trigger failures are present, and a growing literature is demonstrating that reported SSRT differences between groups and individuals are also (or rather) driven by differential trigger-failure rates. In the present study, we extend this line of research to a within-individual manipulation, namely the influence of reward on stop performance. We first reanalyzed four data sets of studies that had reported a facilitating effect of stimulus-based reward on SSRTs. Reanalyzing this data, we found that reward decreased the rates of trigger failures. When accounting for these differential trigger-failure rates, the effect of reward on SSRTs (i.e., stop latency) appeared to be virtually abolished. We then conducted a preregistered online follow-up study, implementing a typical block-based reward manipulation. The results of this study indicated simultaneous reward effects on trigger-failure rates and on SSRT. In sum, the present results indicate that trigger failures are an important source of variance in response inhibition, dovetailing with an evolving multicomponential view of response inhibition.

Severe violations of independence in response inhibition tasks

The stop-signal paradigm, a primary experimental paradigm for understanding cognitive control and response inhibition, rests upon the theoretical foundation of race models, which assume that a go process races independently against a stop process that occurs after a stop-signal delay (SSD). We show that severe violations of this independence assumption at short SSDs occur systematically across a wide range of conditions, including fast and slow reaction times, auditory and visual stop signals, manual and saccadic responses, and especially in selective stopping. We also reanalyze existing data and show that conclusions can change when short SSDs are excluded. Last, we suggest experimental and analysis techniques to address this violation, and propose adjustments to extant models to accommodate this finding.

Testing the factor structure underlying behavior using joint cognitive models: Impulsivity in delay discounting and Cambridge gambling tasks

Neurocognitive tasks are frequently used to assess disordered decision making, and cognitive models of these tasks can quantify performance in terms related to decision makers' underlying cognitive processes. In many cases, multiple cognitive models purport to describe similar processes, but it is difficult to evaluate whether they measure the same latent traits or processes. In this article, we develop methods for modeling behavior across multiple tasks by connecting cognitive model parameters to common latent constructs. This approach can be used to assess whether 2 tasks measure the same dimensions of cognition, or actually improve the estimates of cognitive models when there are overlapping cognitive processes between 2 related tasks. The approach is then applied to connecting decision data on 2 behavioral tasks that evaluate clinically relevant deficits, the delay discounting task and Cambridge gambling task, to determine whether they both measure the same dimension of impulsivity. We find that the discounting rate parameters in the models of each task are not closely related, although substance users exhibit more impulsive behavior on both tasks. Instead, temporal discounting on the delay discounting task as quantified by the model is more closely related to externalizing psychopathology like aggression, while temporal discounting on the Cambridge gambling task is related more to response inhibition failures. The methods we develop thus provide a new way to connect behavior across tasks and grant new insights onto the different dimensions of impulsivity and their relation to substance use. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

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.

GABA and glutamate deficits from frontotemporal lobar degeneration are associated with disinhibition

Behavioural disinhibition is a common feature of the syndromes associated with frontotemporal lobar degeneration (FTLD). It is associated with high morbidity and lacks proven symptomatic treatments. A potential therapeutic strategy is to correct the neurotransmitter deficits associated with FTLD, thereby improving behaviour. Reductions in the neurotransmitters glutamate and GABA correlate with impulsive behaviour in several neuropsychiatric diseases and there is post-mortem evidence of their deficit in FTLD. Here, we tested the hypothesis that prefrontal glutamate and GABA levels are reduced by FTLD in vivo, and that their deficit is associated with impaired response inhibition. Thirty-three participants with a syndrome associated with FTLD (15 patients with behavioural variant frontotemporal dementia and 18 with progressive supranuclear palsy, including both Richardson's syndrome and progressive supranuclear palsy-frontal subtypes) and 20 healthy control subjects were included. Participants undertook ultra-high field (7 T) magnetic resonance spectroscopy and a stop-signal task of response inhibition. We measured glutamate and GABA levels using semi-LASER magnetic resonance spectroscopy in the right inferior frontal gyrus, because of its strong association with response inhibition, and in the primary visual cortex, as a control region. The stop-signal reaction time was calculated using an ex-Gaussian Bayesian model. Participants with frontotemporal dementia and progressive supranuclear palsy had impaired response inhibition, with longer stop-signal reaction times compared with controls. GABA concentration was reduced in patients versus controls in the right inferior frontal gyrus, but not the occipital lobe. There was no group-wise difference in partial volume corrected glutamate concentration between patients and controls. Both GABA and glutamate concentrations in the inferior frontal gyrus correlated inversely with stop-signal reaction time, indicating greater impulsivity in proportion to the loss of each neurotransmitter. We conclude that the glutamatergic and GABAergic deficits in the frontal lobe are potential targets for symptomatic drug treatment of frontotemporal dementia and progressive supranuclear palsy.

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.

Toward a Science of Effective Cognitive Training

A long-standing question in the behavioral sciences is whether cognitive functions can be improved through dedicated training. It is uncontested that training programs can lead to near transfer, meaning increased performance on untrained tasks involving similar cognitive functions. However, whether training also leads to far transfer, meaning increased performance on loosely related untrained tasks or even activities of daily living, is still hotly debated. Here, we review the extant literature and, in particular, the most recent meta-analytic evidence and argue that the ongoing crisis in the field of cognitive-training research may benefit from taking a more mechanistic approach to studying the effectiveness of training. We propose that (a) adopting a more rigorous theoretical framework that builds on a process-based account of training and transfer, (b) considering the role of individual differences in the responsiveness to training, and (c) drawing on Bayesian models of development may help to solve controversial issues in the field and lead the way to designing and implementing more effective training protocols.

A Time Series-Based Point Estimation of Stop Signal Reaction Times: More Evidence on the Role of Reactive Inhibition-Proactive Inhibition Interplay on the SSRT Estimations

The Stop Signal Reaction Time (SSRT) is a latency measurement for the unobservable human brain stopping process, and was formulated by Logan (1994) without consideration of the nature (go/stop) of trials that precede the stop trials. Two asymptotically equivalent and larger indices of mixture SSRT and weighted SSRT were proposed in 2017 to address this issue from time in task longitudinal perspective, but estimation based on the time series perspective has still been missing in the literature. A time series-based state space estimation of SSRT was presented and it was compared with Logan 1994 SSRT over two samples of real Stop Signal Task (SST) data and the simulated SST data. The results showed that time series-based SSRT is significantly larger than Logan’s 1994 SSRT consistent with former Longitudinal-based findings. As a conclusion, SSRT indices considering the after effects of inhibition in their estimation process are larger yielding to hypothesize a larger estimates of SSRT using information on the reactive inhibition, proactive inhibition and their interplay in the SST data.

Leveling the Field for a Fairer Race between Going and Stopping: Neural Evidence for the Race Model of Motor Inhibition from a New Version of the Stop Signal Task

The stop signal task (SST) is the gold standard experimental model of inhibitory control. However, neither SST condition-contrast (stop vs. go, successful vs. failed stop) purely operationalizes inhibition. Because stop trials include a second, infrequent signal, the stop versus go contrast confounds inhibition with attentional and stimulus processing demands. While this confound is controlled for in the successful versus failed stop contrast, the go process is systematically faster on failed stop trials, contaminating the contrast with a different noninhibitory confound. Here, we present an SST variant to address both confounds and evaluate putative neural indices of inhibition with these influences removed. In our variant, stop signals occurred on every trial, equating the noninhibitory demands of the stop versus go contrast. To entice participants to respond despite the impending stop signals, responses produced before stop signals were rewarded. This also reversed the go process bias that typically affects the successful versus failed stop contrast. We recorded scalp electroencephalography in this new version of the task (as well as a standard version of the SST with infrequent stop signal) and found that, even under these conditions, the properties of the frontocentral stop signal P3 ERP remained consistent with the race model. Specifically, in both tasks, the amplitude of the P3 was increased on stop versus go trials. Moreover, the onset of this P3 occurred earlier for successful compared with failed stop trials in both tasks, consistent with the proposal of the race model that an earlier start of the inhibition process will increase stopping success. Therefore, the frontocentral stop signal P3 represents a neural process whose properties are in line with the predictions of the race model of motor inhibition, even when the SST's confounds are controlled.

Computing Bayes factors for evidence-accumulation models using Warp-III bridge sampling

Over the last decade, the Bayesian estimation of evidence-accumulation models has gained popularity, largely due to the advantages afforded by the Bayesian hierarchical framework. Despite recent advances in the Bayesian estimation of evidence-accumulation models, model comparison continues to rely on suboptimal procedures, such as posterior parameter inference and model selection criteria known to favor overly complex models. In this paper, we advocate model comparison for evidence-accumulation models based on the Bayes factor obtained via Warp-III bridge sampling. We demonstrate, using the linear ballistic accumulator (LBA), that Warp-III sampling provides a powerful and flexible approach that can be applied to both nested and non-nested model comparisons, even in complex and high-dimensional hierarchical instantiations of the LBA. We provide an easy-to-use software implementation of the Warp-III sampler and outline a series of recommendations aimed at facilitating the use of Warp-III sampling in practical applications.

Temporal cascade of frontal, motor and muscle processes underlying human action-stopping

Action-stopping is a canonical executive function thought to involve top-down control over the motor system. Here we aimed to validate this stopping system using high temporal resolution methods in humans. We show that, following the requirement to stop, there was an increase of right frontal beta (~13 to 30 Hz) at ~120 ms, likely a proxy of right inferior frontal gyrus; then, at 140 ms, there was a broad skeletomotor suppression, likely reflecting the impact of the subthalamic nucleus on basal ganglia output; then, at ~160 ms, suppression was detected in the muscle, and, finally, the behavioral time of stopping was ~220 ms. This temporal cascade supports a physiological model of action-stopping, and partitions it into subprocesses that are isolable to different nodes and are more precise than the behavioral latency of stopping. Variation in these subprocesses, including at the single-trial level, could better explain individual differences in impulse control.

A method, framework, and tutorial for efficiently simulating models of decision-making

Evidence accumulation models (EAMs) have become the dominant models of rapid decision-making. Several variants of these models have been proposed, ranging from the simple linear ballistic accumulator (LBA) to the more complex leaky-competing accumulator (LCA), and further extensions that include time-varying rates of evidence accumulation or decision thresholds. Although applications of the simpler variants have been widespread, applications of the more complex models have been fewer, largely due to their intractable likelihood function and the computational cost of mass simulation. Here, I present a framework for efficiently fitting complex EAMs, which uses a new, efficient method of simulating these models. I find that the majority of simulation time is taken up by random number generation (RNG) from the normal distribution, needed for the stochastic noise of the differential equation. To reduce this inefficiency, I propose using the well-known concept within computer science of “look-up tables” (LUTs) as an approximation to the inverse cumulative density function (iCDF) method of RNG, which I call “LUT-iCDF”. I show that when using an appropriately sized LUT, simulations using LUT-iCDF closely match those from the standard RNG method in R. My framework, which I provide a detailed tutorial on how to implement, includes C code for 12 different variants of EAMs using the LUT-iCDF method, and should make the implementation of complex EAMs easier and faster.

Individual Differences in the Neural Dynamics of Response Inhibition

Response inhibition is a widely studied aspect of cognitive control that is particularly interesting because of its applications to clinical populations. Although individual differences are integral to cognitive control, so too is our ability to aggregate information across a group of individuals, so that we can powerfully generalize and characterize the group's behavior. Hence, an examination of response inhibition would ideally involve an accurate estimation of both group- and individual-level effects. Hierarchical Bayesian analyses account for individual differences by simultaneously estimating group and individual factors and compensate for sparse data by pooling information across participants. Hierarchical Bayesian models are thus an ideal tool for studying response inhibition, especially when analyzing neural data. We construct hierarchical Bayesian models of the fMRI neural time series, models assuming hierarchies across conditions, participants, and ROIs. Here, we demonstrate the advantages of our models over a conventional generalized linear model in accurately separating signal from noise. We then apply our models to go/no-go and stop signal data from 11 participants. We find strong evidence for individual differences in neural responses to going, not going, and stopping and in functional connectivity across the two tasks and demonstrate how hierarchical Bayesian models can effectively compensate for these individual differences while providing group-level summarizations. Finally, we validated the reliability of our findings using a larger go/no-go data set consisting of 179 participants. In conclusion, hierarchical Bayesian models not only account for individual differences but allow us to better understand the cognitive dynamics of response inhibition.

Assessing the practical differences between model selection methods in inferences about choice response time tasks

Evidence accumulations models (EAMs) have become the dominant modeling framework within rapid decision-making, using choice response time distributions to make inferences about the underlying decision process. These models are often applied to empirical data as "measurement tools", with different theoretical accounts being contrasted within the framework of the model. Some method is then needed to decide between these competing theoretical accounts, as only assessing the models on their ability to fit trends in the empirical data ignores model flexibility, and therefore, creates a bias towards more flexible models. However, there is no objectively optimal method to select between models, with methods varying in both their computational tractability and theoretical basis. I provide a systematic comparison between nine different model selection methods using a popular EAM-the linear ballistic accumulator (LBA; Brown & Heathcote, Cognitive Psychology 57(3), 153-178 2008)-in a large-scale simulation study and the empirical data of Dutilh et al. (Psychonomic Bulletin and Review, 1-19 2018). I find that the "predictive accuracy" class of methods (i.e., the Akaike Information Criterion [AIC], the Deviance Information Criterion [DIC], and the Widely Applicable Information Criterion [WAIC]) make different inferences to the "Bayes factor" class of methods (i.e., the Bayesian Information Criterion [BIC], and Bayes factors) in many, but not all, instances, and that the simpler methods (i.e., AIC and BIC) make inferences that are highly consistent with their more complex counterparts. These findings suggest that researchers should be able to use simpler "parameter counting" methods when applying the LBA and be confident in their inferences, but that researchers need to carefully consider and justify the general class of model selection method that they use, as different classes of methods often result in different inferences.

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.

Cognitive Modeling Suggests That Attentional Failures Drive Longer Stop-Signal Reaction Time Estimates in Attention Deficit/Hyperactivity Disorder

Mean stop-signal reaction time (SSRT) is frequently employed as a measure of response inhibition in cognitive neuroscience research on ADHD. However, this measurement model is limited by two factors which may bias SSRT estimation in this population: 1) excessive skew in "go" RT distributions, and 2) trigger failures, or instances in which individuals fail to trigger an inhibition process in response to the "stop" signal. We use a Bayesian parametric approach, which allows unbiased estimation of the shape of entire SSRT distributions and the probability of trigger failures, to clarify mechanisms of stop-signal task deficits in ADHD. Children with ADHD displayed greater positive skew than their peers in both "go" RT and SSRT distributions. However, they also displayed more frequent trigger failures, which appeared to drive ADHD-related stopping difficulties. Results suggest that stop-signal task performance in ADHD reflects impairments in early attentional processes, rather than inefficiency in the stop process.

A parameter recovery assessment of time-variant models of decision-making

Evidence accumulation models have been one of the most dominant modeling frameworks used to study rapid decision-making over the past several decades. These models propose that evidence accumulates from the environment until the evidence for one alternative reaches some threshold, typically associated with caution, triggering a response. However, researchers have recently begun to reconsider the fundamental assumptions of how caution varies with time. In the past, it was typically assumed that levels of caution are independent of time. Recent investigations have however suggested the possibility that levels of caution decrease over time and that this strategy provides more efficient performance under certain conditions. Our study provides the first comprehensive assessment of this newer class of models accounting for time-varying caution to determine how robustly their parameters can be estimated. We assess five overall variants of collapsing threshold/urgency signal models based on the diffusion decision model, linear ballistic accumulator model, and urgency gating model frameworks. We find that estimation of parameters, particularly those associated with caution/urgency modulation are most robust for the linearly collapsing threshold diffusion model followed by an urgency-gating model with a leakage process. All other models considered, particularly those with ballistic accumulation or nonlinear thresholds, are unable to recover their own parameters adequately, making their usage in parameter estimation contexts questionable.

On the importance of avoiding shortcuts in applying cognitive models to hierarchical data

Psychological experiments often yield data that are hierarchically structured. A number of popular shortcut strategies in cognitive modeling do not properly accommodate this structure and can result in biased conclusions. To gauge the severity of these biases, we conducted a simulation study for a two-group experiment. We first considered a modeling strategy that ignores the hierarchical data structure. In line with theoretical results, our simulations showed that Bayesian and frequentist methods that rely on this strategy are biased towards the null hypothesis. Secondly, we considered a modeling strategy that takes a two-step approach by first obtaining participant-level estimates from a hierarchical cognitive model and subsequently using these estimates in a follow-up statistical test. Methods that rely on this strategy are biased towards the alternative hypothesis. Only hierarchical models of the multilevel data lead to correct conclusions. Our results are particularly relevant for the use of hierarchical Bayesian parameter estimates in cognitive modeling.

Bayes factors for the linear ballistic accumulator model of decision-making

Evidence accumulation models of decision-making have led to advances in several different areas of psychology. These models provide a way to integrate response time and accuracy data, and to describe performance in terms of latent cognitive processes. Testing important psychological hypotheses using cognitive models requires a method to make inferences about different versions of the models which assume different parameters to cause observed effects. The task of model-based inference using noisy data is difficult, and has proven especially problematic with current model selection methods based on parameter estimation. We provide a method for computing Bayes factors through Monte-Carlo integration for the linear ballistic accumulator (LBA; Brown and Heathcote, 2008), a widely used evidence accumulation model. Bayes factors are used frequently for inference with simpler statistical models, and they do not require parameter estimation. In order to overcome the computational burden of estimating Bayes factors via brute force integration, we exploit general purpose graphical processing units; we provide free code for this. This approach allows estimation of Bayes factors via Monte-Carlo integration within a practical time frame. We demonstrate the method using both simulated and real data. We investigate the stability of the Monte-Carlo approximation, and the LBA's inferential properties, in simulation studies.

Reliability of triggering inhibitory process is a better predictor of impulsivity than SSRT

The ability to control behaviour is thought to rely at least partly on adequately suppressing impulsive responses to external stimuli. However, the evidence for a relationship between response inhibition ability and impulse control is weak and inconsistent. This study investigates the relationship between response inhibition and both self-report and behavioural measures of impulsivity as well as engagement in risky behaviours in a large community sample (N = 174) of healthy adolescents and young adults (15-35 years). Using a stop-signal paradigm with a number parity go task, we implemented a novel hierarchical Bayesian model of response inhibition that estimates stop-signal reaction time (SSRT) as a distribution and also accounts for failures to react to the stop-signal (i.e., "trigger failure"), and failure to react to the choice stimulus (i.e., "go failure" or omission errors). In line with previous studies, the model reduced estimates of SSRT by approximately 100 ms compared with traditional non-parametric SSRT estimation techniques. We found significant relationships between behavioural and self-report measures of impulsivity and traditionally estimated SSRT, that did not hold for the model-based SSRT estimates. Instead, behavioural impulsivity measures were correlated with rate of trigger failure. The relationship between trigger failure and impulsivity suggests that the former may index a higher order inhibition process, whereas SSRT may index a more automatic inhibition process. We suggest that the existence of distinct response inhibition processes that may be associated with different levels of cognitive control.

Non-Gaussian Distributional Analyses of Reaction Times (RT): Improvements that Increase Efficacy of RT Tasks for Describing Cognitive Processes

This didactic aims of this review are to demonstrate the advantages of examining the entire reaction time (RT) distribution to better realize the efficacy of mental speed assessment in clinical neuropsychology. RT distributions are typically non-normal, requiring consideration of a host of statistical issues. Specifically, the appropriate model of the mental speed task's distribution (e.g., ex-Gaussian, Weibull, Normal-Gaussian, etc.) must be determined to know what parameters can be used to characterize test performance. While RT mean and standard deviation are typically used to characterize clinical performance, these parameters are usually inappropriate because RT performance rarely conforms to a normal-Gaussian distribution. For illustrative purposes, a tutorial for examining the entire RT distribution is provided that demonstrates differences between an Attention Deficit/Hyperactivity and a neurotypical group of college students. While such analyses are descriptive, it is important to characterize test performance in the context of a theoretical model of RT performance. Therefore, the tutorial includes interpretation that uses the Diffusion model (Ratcliff Psychological Review, 85, 59-108, 1978), which assumes an ex-Gaussian distribution. It is concluded that current results conform to a large literature demonstrating a more nuanced understanding of cognition afforded by non-Gaussian analysis of RT. This literature is compelling neuropsychology to enlarge assessment technology beyond the limitations of paper-and-pencil instruments.

Generalised inhibitory impairment to appetitive cues: From alcoholic to non-alcoholic visual stimuli

BACKGROUND

Prior research demonstrates that individuals who consume alcohol show diminished inhibitory control towards alcohol-related cues. However, such research contrasts predominantly alcoholic appetitive cues with non-alcoholic, non-appetitive cues (e.g., stationary items). As such, it is not clear whether it is specifically the alcoholic nature of the cues that influences impairments in inhibitory control or whether more general appetitive processes are at play.

AIMS

The current study examined the hitherto untested assertion that the disinhibiting effects of alcohol-related stimuli might generalise to other appetitive liquid stimuli, but not to non-appetitive liquid stimuli.

METHOD

Fifty-nine participants (Mage=21.63, SD=5.85) completed a modified version of the Stop Signal Task, which exposed them to visual stimuli of three types of liquids: Alcoholic appetitive (e.g., wine), non-alcoholic appetitive (e.g., water) and non-appetitive (e.g., washing-up liquid).

RESULTS

Consistent with predictions, Stop-signal reaction time was significantly longer for appetitive (alcoholic, non-alcoholic) compared to non-appetitive stimuli. Participants were also faster and less error-prone when responding to appetitive relative to non-appetitive stimuli on go-trials. There were no apparent differences in stop signal reaction times between alcoholic and non-alcoholic appetitive products.

CONCLUSIONS

These findings suggest that decreases in inhibitory control in response to alcohol-related cues might generalise to other appetitive liquids, possibly due to evaluative conditioning. Implications for existing research methodologies include the use of appetitive control conditions and the diversification of cues within tests of alcohol-related inhibitory control.

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.