Inhibitory control in mind and brain 2.0: blocked-input models of saccadic countermanding

Attentional spatial cueing of the stop-signal affects the ability to suppress behavioural responses

The ability to adapt to the environment is linked to the possibility of inhibiting inappropriate behaviours, and this ability can be enhanced by attention. Despite this premise, the scientific literature that assesses how attention can influence inhibition is still limited. This study contributes to this topic by evaluating whether spatial and moving attentional cueing can influence inhibitory control. We employed a task in which subjects viewed a vertical bar on the screen that, from a central position, moved either left or right where two circles were positioned. Subjects were asked to respond by pressing a key when the motion of the bar was interrupted close to the circle (go signal). In about 40% of the trials, following the go signal and after a variable delay, a visual target appeared in either one of the circles, requiring response inhibition (stop signal). In most of the trials the stop signal appeared on the same side as the go signal (valid condition), while in the others, it appeared on the opposite side (invalid condition). We found that spatial and moving cueing facilitates inhibitory control in the valid condition. This facilitation was observed especially for stop signals that appeared within 250ms of the presentation of the go signal, thus suggesting an involvement of exogenous attentional orienting. This work demonstrates that spatial and moving cueing can influence inhibitory control, providing a contribution to the investigation of the relationship between spatial attention and inhibitory control.

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.

Terms of debate: Consensus definitions to guide the scientific discourse on visual distraction

Hypothesis-driven research rests on clearly articulated scientific theories. The building blocks for communicating these theories are scientific terms. Obviously, communication - and thus, scientific progress - is hampered if the meaning of these terms varies idiosyncratically across (sub)fields and even across individual researchers within the same subfield. We have formed an international group of experts representing various theoretical stances with the goal to homogenize the use of the terms that are most relevant to fundamental research on visual distraction in visual search. Our discussions revealed striking heterogeneity and we had to invest much time and effort to increase our mutual understanding of each other's use of central terms, which turned out to be strongly related to our respective theoretical positions. We present the outcomes of these discussions in a glossary and provide some context in several essays. Specifically, we explicate how central terms are used in the distraction literature and consensually sharpen their definitions in order to enable communication across theoretical standpoints. Where applicable, we also explain how the respective constructs can be measured. We believe that this novel type of adversarial collaboration can serve as a model for other fields of psychological research that strive to build a solid groundwork for theorizing and communicating by establishing a common language. For the field of visual distraction, the present paper should facilitate communication across theoretical standpoints and may serve as an introduction and reference text for newcomers.

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.

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.

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

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

Functional architecture of executive control and associated event-related potentials in macaques

The medial frontal cortex (MFC) enables executive control by monitoring relevant information and using it to adapt behavior. In macaques performing a saccade countermanding (stop-signal) task, we simultaneously recorded electrical potentials over MFC and neural spiking across all layers of the supplementary eye field (SEF). We report the laminar organization of neurons enabling executive control by monitoring the conflict between incompatible responses, the timing of events, and sustaining goal maintenance. These neurons were a mix of narrow-spiking and broad-spiking found in all layers, but those predicting the duration of control and sustaining the task goal until the release of operant control were more commonly narrow-spiking neurons confined to layers 2 and 3 (L2/3). We complement these results with evidence for a monkey homolog of the N2/P3 event-related potential (ERP) complex associated with response inhibition. N2 polarization varied with error-likelihood and P3 polarization varied with the duration of expected control. The amplitude of the N2 and P3 were predicted by the spike rate of different classes of neurons located in L2/3 but not L5/6. These findings reveal features of the cortical microcircuitry supporting executive control and producing associated ERPs.

Transient neuronal suppression for exploitation of new sensory evidence

In noisy but stationary environments, decisions should be based on the temporal integration of sequentially sampled evidence. This strategy has been supported by many behavioral studies and is qualitatively consistent with neural activity in multiple brain areas. By contrast, decision-making in the face of non-stationary sensory evidence remains poorly understood. Here, we trained monkeys to identify and respond via saccade to the dominant color of a dynamically refreshed bicolor patch that becomes informative after a variable delay. Animals’ behavioral responses were briefly suppressed after evidence changes, and many neurons in the frontal eye field displayed a corresponding dip in activity at this time, similar to that frequently observed after stimulus onset but sensitive to stimulus strength. Generalized drift-diffusion models revealed consistency of behavior and neural activity with brief suppression of motor output, but not with pausing or resetting of evidence accumulation. These results suggest that momentary arrest of motor preparation is important for dynamic perceptual decision making.

While evidence is constantly changing during real-world decisions, little is known about how the brain deals with such changes. Here, the authors show that the brain strategically suppresses motor output via the frontal eye fields in response to stimulus changes.

The role of inhibitory control in shoot/don't-shoot decisions

Previous work has demonstrated a link between cognitive abilities, specifically inhibitory control and lethal force decision-making performance. However, many previously used approaches to simulating shoot/don't shoot scenarios have lacked ecological validity. There is a need to investigate how inhibitory control impacts shoot/don't decisions using realistic simulations to better translate the findings to military and law enforcement settings. This study used multiple cognitive control tasks incorporating discrete judgements in go/no-go and stop signal tasks as well as subjective judgements in go/no-go tasks with both colour stimuli and emotional faces. These combined tasks provided a comprehensive evaluation of inhibitory control abilities. To ensure ecological validity in shooting performance, existing military training scenarios incorporated realistic weaponry and aiming behaviours across different shoot/don't-shoot simulations. The inhibitory control battery identified five principal components from the various tasks, including: stopping ability, response speed, emotion detection, colour detection, and emotional biases. These principal inhibitory control components were entered into hierarchical linear regressions with the dependent variables of unintended casualties inflicted and lethal rounds fired, respectively. Stopping ability better predicted the likelihood of inflicting an unintended casualty, whereas response speed better predicted the number of lethal rounds fired. These regression models included baseline metrics of marksmanship and shots fired, which supports a role for inhibitory control above and beyond basic shooting abilities or strategy. These collective findings provide mechanistic support for the relationship between inhibitory control and errors in shoot/don't-shoot decision-making while using realistic military training scenarios.

Volume of β-Bursts, But Not Their Rate, Predicts Successful Response Inhibition

In humans, impaired response inhibition is characteristic of a wide range of psychiatric diseases and of normal aging. It is hypothesized that the right inferior frontal cortex (rIFC) plays a key role by inhibiting the motor cortex via the basal ganglia. The electroencephalography (EEG)-derived β-rhythm (15-29 Hz) is thought to reflect communication within this network, with increased right frontal β-power often observed before successful response inhibition. Recent literature suggests that averaging spectral power obscures the transient, burst-like nature of β-activity. There is evidence that the rate of β-bursts following a Stop signal is higher when a motor response is successfully inhibited. However, other characteristics of β-burst events, and their topographical properties, have not yet been examined. Here, we used a large human (male and female) EEG Stop Signal task (SST) dataset (n = 218) to examine averaged normalized β-power, β-burst rate, and β-burst "volume" (which we defined as burst duration × frequency span × amplitude). We first sought to optimize the β-burst detection method. In order to find predictors across the whole scalp, and with high temporal precision, we then used machine learning to (1) classify successful versus failed stopping and to (2) predict individual stop signal reaction time (SSRT). β-burst volume was significantly more predictive of successful and fast stopping than β-burst rate and normalized β-power. The classification model generalized to an external dataset (n = 201). We suggest β-burst volume is a sensitive and reliable measure for investigation of human response inhibition.SIGNIFICANCE STATEMENT The electroencephalography (EEG)-derived β-rhythm (15-29 Hz) is associated with the ability to inhibit ongoing actions. In this study, we sought to identify the specific characteristics of β-activity that contribute to successful and fast inhibition. In order to search for the most relevant features of β-activity, across the whole scalp and with high temporal precision, we employed machine learning on two large datasets. Spatial and temporal features of β-burst "volume" (duration × frequency span × amplitude) predicted response inhibition outcomes in our data significantly better than β-burst rate and normalized β-power. These findings suggest that multidimensional measures of β-bursts, such as burst volume, can add to our understanding of human response inhibition.

The unknown but knowable relationship between Presaccadic Accumulation of activity and Saccade initiation

The goal of this short review is to call attention to a yawning gap of knowledge that separates two processes essential for saccade production. On the one hand, knowledge about the saccade generation circuitry within the brainstem is detailed and precise - push-pull interactions between gaze-shifting and gaze-holding processes control the time of saccade initiation, which begins when omnipause neurons are inhibited and brainstem burst neurons are excited. On the other hand, knowledge about the cortical and subcortical premotor circuitry accomplishing saccade initiation has crystalized around the concept of stochastic accumulation - the accumulating activity of saccade neurons reaching a fixed value triggers a saccade. Here is the gap: we do not know how the reaching of a threshold by premotor neurons causes the critical pause and burst of brainstem neurons that initiates saccades. Why this problem matters and how it can be addressed will be discussed. Closing the gap would unify two rich but curiously disconnected empirical and theoretical domains.

Design issues and solutions for stop-signal data from the Adolescent Brain Cognitive Development (ABCD) study

The Adolescent Brain Cognitive Development (ABCD) study is an unprecedented longitudinal neuroimaging sample that tracks the brain development of over 9-10 year olds through adolescence. At the core of this study are the three tasks that are completed repeatedly within the MRI scanner, one of which is the stop-signal task. In analyzing the available stopping experimental code and data, we identified a set of design issues that we believe significantly compromise its value. These issues include but are not limited to variable stimulus durations that violate basic assumptions of dominant stopping models, trials in which stimuli are incorrectly not presented, and faulty stop-signal delays. We present eight issues, show their effect on the existing ABCD data, suggest prospective solutions including task changes for future data collection and preliminary computational models, and suggest retrospective solutions for data users who wish to make the most of the existing data.

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.

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.

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.

Express saccades during a countermanding task

Express saccades are unusually short latency, visually guided saccadic eye movements. They are most commonly observed when the fixation spot disappears at a consistent, short interval before a target spot appears at a repeated location. The saccade countermanding task includes no fixation-target gap, variable target presentation times, and the requirement to withhold saccades on some trials. These testing conditions should discourage production of express saccades. However, two macaque monkeys performing the saccade countermanding task produced consistent, multimodal distributions of saccadic latencies. These distributions consisted of a longer mode extending from 200 ms to as much as 600 ms after target presentation and another consistently less than 100 ms after target presentation. Simulations revealed that, by varying express saccade production, monkeys could earn more reward. If express saccades were not rewarded, they were rarely produced. The distinct mechanisms producing express and longer saccade latencies were revealed further by the influence of regularities in the duration of the fixation interval preceding target presentation on saccade latency. Temporal expectancy systematically affected the latencies of regular but not of express saccades. This study highlights that cognitive control can integrate information across trials and strategically elicit intermittent very short latency saccades to acquire more reward.NEW & NOTEWORTHY A serendipitous discovery that macaque monkeys produce express saccades under conditions that should discourage them reveals how cognitive control can adapt behavior to maximize reward.

Cognitive control and automatic interference in mind and brain: A unified model of saccadic inhibition and countermanding

Countermanding behavior has long been seen as a cornerstone of executive control-the human ability to selectively inhibit undesirable responses and change plans. However, scattered evidence implies that stopping behavior is entangled with simpler automatic stimulus-response mechanisms. Here we operationalize this idea by merging the latest conceptualization of saccadic countermanding with a neural network model of visuo-oculomotor behavior that integrates bottom-up and top-down drives. This model accounts for all fundamental qualitative and quantitative features of saccadic countermanding, including neuronal activity. Importantly, it does so by using the same architecture and parameters as basic visually guided behavior and automatic stimulus-driven interference. Using simulations and new data, we compare the temporal dynamics of saccade countermanding with that of saccadic inhibition (SI), a hallmark effect thought to reflect automatic competition within saccade planning areas. We demonstrate how SI accounts for a large proportion of the saccade countermanding process when using visual signals. We conclude that top-down inhibition acts later, piggy-backing on the quicker automatic inhibition. This conceptualization fully accounts for the known effects of signal features and response modalities traditionally used across the countermanding literature. Moreover, it casts different light on the concept of top-down inhibition, its timing and neural underpinning, as well as the interpretation of stop-signal reaction time (RT), the main behavioral measure in the countermanding literature. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

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.

Activity in orbitofrontal neuronal ensembles reflects inhibitory control

Stopping, or inhibition, is a form of self-control that is a core element of flexible and adaptive behavior. Its neural origins remain unclear. Some views hold that inhibition decisions reflect the aggregation of widespread and diverse pieces of information, including information arising in ostensible core reward regions (i.e., outside the canonical executive system). We recorded activity of single neurons in the orbitofrontal cortex (OFC) of macaques, a region associated with economic decisions, and whose role in inhibition is debated. Subjects performed a classic inhibition task known as the stop signal task. Ensemble decoding analyses reveal a clear firing rate pattern that distinguishes successful from failed inhibition and that begins after the stop signal and before the stop signal reaction time (SSRT). We also found a different and orthogonal ensemble pattern that distinguishes successful from failed stopping before the beginning of the trial. These signals were distinct from, and orthogonal to, value encoding, which was also observed in these neurons. The timing of the early and late signals was, respectively, consistent with the idea that neuronal activity in OFC encodes inhibition both proactively and reactively.

Countermanding Perceptual Decision-Making

We investigated whether a task requiring concurrent perceptual decision-making and response control can be performed concurrently, whether evidence accumulation and response control are accomplished by the same neurons, and whether perceptual decision-making and countermanding can be unified computationally. Based on neural recordings in a prefrontal area of macaque monkeys, we present behavioral, neural, and computational results demonstrating that perceptual decision-making of varying difficulty can be countermanded efficiently, that single prefrontal neurons instantiate both evidence accumulation and response control, and that an interactive race between stochastic GO evidence accumulators for each alternative and a distinct STOP accumulator fits countermanding choice behavior and replicates neural trajectories. Thus, perceptual decision-making and response control, previously regarded as distinct mechanisms, are actually aspects of a common neuro-computational mechanism supporting flexible behavior.

Accumulators, Neurons, and Response Time

The marriage of cognitive neurophysiology and mathematical psychology to understand decision-making has been exceptionally productive. This interdisciplinary area is based on the proposition that particular neurons or circuits instantiate the accumulation of evidence specified by mathematical models of sequential sampling and stochastic accumulation. This linking proposition has earned widespread endorsement. Here, a brief survey of the history of the proposition precedes a review of multiple conundrums and paradoxes concerning the accuracy, precision, and transparency of that linking proposition. Correctly establishing how abstract models of decision-making are instantiated by particular neural circuits would represent a remarkable accomplishment in mapping mind to brain. Failing would reveal challenging limits for cognitive neuroscience. This is such a vigorous area of research because so much is at stake.

Mouse movement measures enhance the stop-signal task in adult ADHD assessment

The accurate detection of attention-deficit/hyperactivity disorder (ADHD) symptoms, such as inattentiveness and behavioral disinhibition, is crucial for delivering timely assistance and treatment. ADHD is commonly diagnosed and studied with specialized questionnaires and behavioral tests such as the stop-signal task. However, in cases of late-onset or mild forms of ADHD, behavioral measures often fail to gauge the deficiencies well-highlighted by questionnaires. To improve the sensitivity of behavioral tests, we propose a novel version of the stop-signal task (SST), which integrates mouse cursor tracking. In two studies, we investigated whether introducing mouse movement measures to the stop-signal task improves associations with questionnaire-based measures, as compared to the traditional (keypress-based) version of SST. We also scrutinized the influence of different parameters of stop-signal tasks, such as the method of stop-signal delay setting or definition of response inhibition failure, on these associations. Our results show that a) SSRT has weak association with impulsivity, while mouse movement measures have strong and significant association with impulsivity; b) machine learning models trained on the mouse movement data from "known" participants using nested cross-validation procedure can accurately predict impulsivity ratings of "unknown" participants; c) mouse movement features such as maximum acceleration and maximum velocity are among the most important predictors for impulsivity; d) using preset stop-signal delays prompts behavior that is more indicative of impulsivity.

Slow and steady? Strategic adjustments in response caution are moderately reliable and correlate across tasks

Speed-accuracy trade-offs are often considered a confound in speeded choice tasks, but individual differences in strategy have been linked to personality and brain structure. We ask whether strategic adjustments in response caution are reliable, and whether they correlate across tasks and with impulsivity traits. In Study 1, participants performed Eriksen flanker and Stroop tasks in two sessions four weeks apart. We manipulated response caution by emphasising speed or accuracy. We fit the diffusion model for conflict tasks and correlated the change in boundary (accuracy - speed) across session and task. We observed moderate test-retest reliability, and medium to large correlations across tasks. We replicated this between-task correlation in Study 2 using flanker and perceptual decision tasks. We found no consistent correlations with impulsivity. Though moderate reliability poses a challenge for researchers interested in stable traits, consistent correlation between tasks indicates there are meaningful individual differences in the speed-accuracy trade-off.

Selective Influence and Sequential Operations: A Research Strategy for Visual Search

We discuss the problem of elucidating mechanisms of visual search. We begin by considering the history, logic, and methods of relating behavioral or cognitive processes with neural processes. We then survey briefly the cognitive neurophysiology of visual search and essential aspects of the neural circuitry supporting this capacity. We introduce conceptually and empirically a powerful but underutilized experimental approach to dissect the cognitive processes supporting performance of a visual search task with factorial manipulations of singleton-distractor identifiability and stimulus-response cue discriminability. We show that systems factorial technology can distinguish processing architectures from the performance of macaque monkeys. This demonstration offers new opportunities to distinguish neural mechanisms through selective manipulation of visual encoding, search selection, rule encoding, and stimulus-response mapping.

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.

A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task

Response inhibition is essential for navigating everyday life. Its derailment is considered integral to numerous neurological and psychiatric disorders, and more generally, to a wide range of behavioral and health problems. Response-inhibition efficiency furthermore correlates with treatment outcome in some of these conditions. The stop-signal task is an essential tool to determine how quickly response inhibition is implemented. Despite its apparent simplicity, there are many features (ranging from task design to data analysis) that vary across studies in ways that can easily compromise the validity of the obtained results. Our goal is to facilitate a more accurate use of the stop-signal task. To this end, we provide 12 easy-to-implement consensus recommendations and point out the problems that can arise when they are not followed. Furthermore, we provide user-friendly open-source resources intended to inform statistical-power considerations, facilitate the correct implementation of the task, and assist in proper data analysis.

Neurally constrained modeling of speed-accuracy tradeoff during visual search: gated accumulation of modulated evidence

Stochastic accumulator models account for response times and errors in perceptual decision making by assuming a noisy accumulation of perceptual evidence to a threshold. Previously, we explained saccade visual search decision making by macaque monkeys with a stochastic multiaccumulator model in which accumulation was driven by a gated feed-forward integration to threshold of spike trains from visually responsive neurons in frontal eye field that signal stimulus salience. This neurally constrained model quantitatively accounted for response times and errors in visual search for a target among varying numbers of distractors and replicated the dynamics of presaccadic movement neurons hypothesized to instantiate evidence accumulation. This modeling framework suggested strategic control over gate or over threshold as two potential mechanisms to accomplish speed-accuracy tradeoff (SAT). Here, we show that our gated accumulator model framework can account for visual search performance under SAT instructions observed in a milestone neurophysiological study of frontal eye field. This framework captured key elements of saccade search performance, through observed modulations of neural input, as well as flexible combinations of gate and threshold parameters necessary to explain differences in SAT strategy across monkeys. However, the trajectories of the model accumulators deviated from the dynamics of most presaccadic movement neurons. These findings demonstrate that traditional theoretical accounts of SAT are incomplete descriptions of the underlying neural adjustments that accomplish SAT, offer a novel mechanistic account of decision-making mechanisms during speed-accuracy tradeoff, and highlight questions regarding the identity of model and neural accumulators. NEW & NOTEWORTHY A gated accumulator model is used to elucidate neurocomputational mechanisms of speed-accuracy tradeoff. Whereas canonical stochastic accumulators adjust strategy only through variation of an accumulation threshold, we demonstrate that strategic adjustments are accomplished by flexible combinations of both modulation of the evidence representation and adaptation of accumulator gate and threshold. The results indicate how model-based cognitive neuroscience can translate between abstract cognitive models of performance and neural mechanisms of speed-accuracy tradeoff.

Dissociating the neural substrates for inhibition and shifting in domain-general cognitive control

Inhibition and shifting are two key components of domain-general cognitive control. Numerous studies have investigated the neural substrates of both components, but it is still unclear whether the relevant brain regions are specifically involved in one specific component or commonly engaged in both components. Here, we addressed this question by using functional magnetic resonance imaging and a modified saccade paradigm that was effective to disentangle inhibition and shifting in one experiment. The results showed that both the middle frontal gyrus and left parietal lobe were involved in both components but the middle frontal gyrus was more active for the inhibition while the inferior parietal lobe was more active for the shifting processing. The outcome suggests that, although both regions are engaged in inhibition and shifting, each plays a dominant role in one component. These findings provide a further insight into the neural dissociation in inhibition and shifting, as well as a better explanation on the framework of unity and diversity from a neuropsychological viewpoint.

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.

Detectability of stop-signal determines magnitude of deceleration in saccade planning

An inhibitory control is exerted when the context in which a movement has been planned changes abruptly making the impending movement inappropriate. Neurons in the frontal eye field and superior colliculus steadily increase activity before a saccadic eye movement, but cease the rise below a threshold when an impending saccade is withheld in response to an unexpected stop-signal. This type of neural modulation has been majorly considered as an outcome of a race between preparatory and inhibitory processes ramping up to reach a decision criterion. An alternative model claims that the rate of saccade planning is diminished exclusively when the stop-signal is detected within a stipulated period. However, due to a dearth of empirical evidence in support of the latter model, it remains unclear how the detectability of the stop-signal influences saccade inhibition. In our study, human participants selected a visual target to look at by discriminating a go-cue. Infrequently they cancelled saccade and reported whether they saw the stop-signal. The go-cue and stop-signal both were embedded in a stream of irrelevant stimuli presented in rapid succession. Participants exhibited difficulty in detection of the stop-signal when presented almost immediately after the go-cue. We found a robust relationship between the detectability of the stop-signal and the odds of saccade inhibition. Saccade latency increased exponentially with the maximum time available for processing the stop-signal before gaze shifted. A model in which the stop-signal onset spontaneously decelerated progressive saccade planning with the magnitude proportional to its detectability accounted for the data.

Visual salience of the stop signal affects the neuronal dynamics of controlled inhibition

The voluntary control of movement is often tested by using the countermanding, or stop-signal task that sporadically requires the suppression of a movement in response to an incoming stop-signal. Neurophysiological recordings in monkeys engaged in the countermanding task have shown that dorsal premotor cortex (PMd) is implicated in movement control. An open question is whether and how the perceptual demands inherent the stop-signal affects inhibitory performance and their underlying neuronal correlates. To this aim we recorded multi-unit activity (MUA) from the PMd of two male monkeys performing a countermanding task in which the salience of the stop-signals was modulated. Consistently to what has been observed in humans, we found that less salient stimuli worsened the inhibitory performance. At the neuronal level, these behavioral results were subtended by the following modulations: when the stop-signal was not noticeable compared to the salient condition the preparatory neuronal activity in PMd started to be affected later and with a less sharp dynamic. This neuronal pattern is probably the consequence of a less efficient inhibitory command useful to interrupt the neural dynamic that supports movement generation in PMd.

A model-based quantification of action control deficits in Parkinson's disease

Basal ganglia dysfunction in Parkinson's disease (PD) is thought to generate deficits in action control, but the characterization of these deficits have been qualitative rather than quantitative. Patients with PD typically show prolonged response times on tasks that instantiate a conflict between goal-directed processing and automatic response tendencies. In the Simon task, for example, the irrelevant location of the stimulus automatically activates a corresponding lateralized response, generating a potential conflict with goal-directed choices. We applied a new computational model of conflict processing to two sets of behavioral data from the Simon task to quantify the effects of PD and dopaminergic (DA) medication on action control mechanisms. Compared to healthy controls (HC) matched in age gender and education, patients with PD showed a deficit in goal-directed processing, and the magnitude of this deficit positively correlated with cognitive symptoms. Analyses of the time-course of the location-based automatic activation yielded mixed findings. In both datasets, we found that the peak amplitude of the automatic activation was similar between PD and HC, demonstrating a similar degree of response capture. However, PD patients showed a prolonged automatic activation in only one dataset. This discrepancy was resolved by theoretical analyses of conflict resolution in the Simon task. The reduction of interference generated by the automatic activation appears to be driven by a mixture of passive decay and top-down inhibitory control, the contribution of each component being modulated by task demands. Our results suggest that PD selectively impairs the inhibitory control component, a deficit likely remediated by DA medication. This work advances our understanding of action control deficits in PD, and illustrates the benefit of using computational models to quantitatively measure cognitive processes in clinical populations.

Testing the validity of conflict drift-diffusion models for use in estimating cognitive processes: A parameter-recovery study

Researchers and clinicians are interested in estimating individual differences in the ability to process conflicting information. Conflict processing is typically assessed by comparing behavioral measures like RTs or error rates from conflict tasks. However, these measures are hard to interpret because they can be influenced by additional processes like response caution or bias. This limitation can be circumvented by employing cognitive models to decompose behavioral data into components of underlying decision processes, providing better specificity for investigating individual differences. A new class of drift-diffusion models has been developed for conflict tasks, presenting a potential tool to improve analysis of individual differences in conflict processing. However, measures from these models have not been validated for use in experiments with limited data collection. The present study assessed the validity of these models with a parameter-recovery study to determine whether and under what circumstances the models provide valid measures of cognitive processing. Three models were tested: the dual-stage two-phase model (Hübner, Steinhauser, & Lehle, Psychological Review, 117(3), 759-784, 2010), the shrinking spotlight model (White, Ratcliff, & Starns, Cognitive Psychology, 63(4), 210-238, 2011), and the diffusion model for conflict tasks (Ulrich, Schröter, Leuthold, & Birngruber, Cogntive Psychology, 78, 148-174, 2015). The validity of the model parameters was assessed using different methods of fitting the data and different numbers of trials. The results show that each model has limitations in recovering valid parameters, but they can be mitigated by adding constraints to the model. Practical recommendations are provided for when and how each model can be used to analyze data and provide measures of processing in conflict tasks.

A Pause-then-Cancel model of stopping: evidence from basal ganglia neurophysiology

Many studies have implicated the basal ganglia in the suppression of action impulses ('stopping'). Here, we discuss recent neurophysiological evidence that distinct hypothesized processes involved in action preparation and cancellation can be mapped onto distinct basal ganglia cell types and pathways. We examine how movement-related activity in the striatum is related to a 'Go' process and how going may be modulated by brief epochs of beta oscillations. We then describe how, rather than a unitary 'Stop' process, there appear to be separate, complementary 'Pause' and 'Cancel' mechanisms. We discuss the implications of these stopping subprocesses for the interpretation of the stop-signal reaction time-in particular, some activity that seems too slow to causally contribute to stopping when assuming a single Stop processes may actually be fast enough under a Pause-then-Cancel model. Finally, we suggest that combining complementary neural mechanisms that emphasize speed or accuracy respectively may serve more generally to optimize speed-accuracy trade-offs.This article is part of the themed issue 'Movement suppression: brain mechanisms for stopping and stillness'.

Behavioural and computational varieties of response inhibition in eye movements

Response inhibition is the ability to override a planned or an already initiated response. It is the hallmark of executive control as its deficits favour impulsive behaviours, which may be detrimental to an individual's life. This article reviews behavioural and computational guises of response inhibition. It focuses only on inhibition of oculomotor responses. It first reviews behavioural paradigms of response inhibition in eye movement research, namely the countermanding and antisaccade paradigms, both proven to be useful tools for the study of response inhibition in cognitive neuroscience and psychopathology. Then, it briefly reviews the neural mechanisms of response inhibition in these two behavioural paradigms. Computational models that embody a hypothesis and/or a theory of mechanisms underlying performance in both behavioural paradigms as well as provide a critical analysis of strengths and weaknesses of these models are discussed. All models assume the race of decision processes. The decision process in each paradigm that wins the race depends on different mechanisms. It has been shown that response latency is a stochastic process and has been proven to be an important measure of the cognitive control processes involved in response stopping in healthy and patient groups. Then, the inhibitory deficits in different brain diseases are reviewed, including schizophrenia and obsessive-compulsive disorder. Finally, new directions are suggested to improve the performance of models of response inhibition by drawing inspiration from successes of models in other domains.This article is part of the themed issue 'Movement suppression: brain mechanisms for stopping and stillness'.

Large-scale functional neural network correlates of response inhibition: an fMRI meta-analysis

An influential hypothesis from the last decade proposed that regions within the right inferior frontal cortex of the human brain were dedicated to supporting response inhibition. There is growing evidence, however, to support an alternative model, which proposes that neural areas associated with specific inhibitory control tasks co-exist as common network mechanisms, supporting diverse cognitive processes. This meta-analysis of 225 studies comprising 323 experiments examined the common and distinct neural correlates of cognitive processes for response inhibition, namely interference resolution, action withholding, and action cancellation. Activation coordinates for each subcategory were extracted using multilevel kernel density analysis (MKDA). The extracted activity patterns were then mapped onto the brain functional network atlas to derive the common (i.e., process-general) and distinct (i.e., domain-oriented) neural network correlates of these processes. Independent of the task types, activation of the right hemispheric regions (inferior frontal gyrus, insula, median cingulate, and paracingulate gyri) and superior parietal gyrus was common across the cognitive processes studied. Mapping the activation patterns to a brain functional network atlas revealed that the fronto-parietal and ventral attention networks were the core neural systems that were commonly engaged in different processes of response inhibition. Subtraction analyses elucidated the distinct neural substrates of interference resolution, action withholding, and action cancellation, revealing stronger activation in the ventral attention network for interference resolution than action inhibition. On the other hand, action withholding/cancellation primarily engaged the fronto-striatal circuit. Overall, our results suggest that response inhibition is a multidimensional cognitive process involving multiple neural regions and networks for coordinating optimal performance. This finding has significant implications for the understanding and assessment of response inhibition.

Models of inhibitory control

We survey models of response inhibition having different degrees of mathematical, computational and neurobiological specificity and generality. The independent race model accounts for performance of the stop-signal or countermanding task in terms of a race between GO and STOP processes with stochastic finishing times. This model affords insights into neurophysiological mechanisms that are reviewed by other authors in this volume. The formal link between the abstract GO and STOP processes and instantiating neural processes is articulated through interactive race models consisting of stochastic accumulator GO and STOP units. This class of model provides quantitative accounts of countermanding performance and replicates the dynamics of neural activity producing that performance. The interactive race can be instantiated in a network of biophysically plausible spiking excitatory and inhibitory units. Other models seek to account for interactions between units in frontal cortex, basal ganglia and superior colliculus. The strengths, weaknesses and relationships of the different models will be considered. We will conclude with a brief survey of alternative modelling approaches and a summary of problems to be addressed including accounting for differences across effectors, species, individuals, task conditions and clinical deficits.This article is part of the themed issue 'Movement suppression: brain mechanisms for stopping and stillness'.

Speeded saccadic and manual visuo-motor decisions: Distinct processes but same principles

•

Core architecture of visuo-motor selection model generalises across effectors.

•

Hand and eyes show very different response times, but similar decision times.

•

Longer non-decision time for visuo-manual responses accounts for longer response times.

•

Stronger faster transient visual inputs for saccades account for different selection dynamics.

Action decisions are considered an emergent property of competitive response activations. As such, decision mechanisms are embedded in, and therefore may differ between, different response modalities. Despite this, the saccadic eye movement system is often promoted as a model for all decisions, especially in the fields of electrophysiology and modelling. Other research traditions predominantly use manual button presses, which have different response distribution profiles and are initiated by different brain areas. Here we tested whether core concepts of action selection models (decision and non-decision times, integration of automatic and selective inputs to threshold, interference across response options, noise, etc.) generalise from saccadic to manual domains. Using two diagnostic phenomena, the remote distractor effect (RDE) and ‘saccadic inhibition', we find that manual responses are also sensitive to the interference of visual distractors but to a lesser extent than saccades and during a shorter time window. A biologically-inspired model (DINASAUR, based on non-linear input dynamics) can account for both saccadic and manual response distributions and accuracy by simply adjusting the balance and relative timings of transient and sustained inputs, and increasing the mean and variance of non-decisional delays for manual responses. This is consistent with known neurophysiological and anatomical differences between saccadic and manual networks. Thus core decision principles appear to generalise across effectors, consistent with previous work, but we also conclude that key quantitative differences underlie apparent qualitative differences in the literature, such as effects being robustly reported in one modality and unreliable in another.

Model-based cognitive neuroscience

This special issue explores the growing intersection between mathematical psychology and cognitive neuroscience. Mathematical psychology, and cognitive modeling more generally, has a rich history of formalizing and testing hypotheses about cognitive mechanisms within a mathematical and computational language, making exquisite predictions of how people perceive, learn, remember, and decide. Cognitive neuroscience aims to identify neural mechanisms associated with key aspects of cognition using techniques like neurophysiology, electrophysiology, and structural and functional brain imaging. These two come together in a powerful new approach called model-based cognitive neuroscience, which can both inform cognitive modeling and help to interpret neural measures. Cognitive models decompose complex behavior into representations and processes and these latent model states can be used to explain the modulation of brain states under different experimental conditions. Reciprocally, neural measures provide data that help constrain cognitive models and adjudicate between competing cognitive models that make similar predictions about behavior. As examples, brain measures are related to cognitive model parameters fitted to individual participant data, measures of brain dynamics are related to measures of model dynamics, model parameters are constrained by neural measures, model parameters or model states are used in statistical analyses of neural data, or neural and behavioral data are analyzed jointly within a hierarchical modeling framework. We provide an introduction to the field of model-based cognitive neuroscience and to the articles contained within this special issue.

Model-based functional neuroimaging using dynamic neural fields: An integrative cognitive neuroscience approach

A fundamental challenge in cognitive neuroscience is to develop theoretical frameworks that effectively span the gap between brain and behavior, between neuroscience and psychology. Here, we attempt to bridge this divide by formalizing an integrative cognitive neuroscience approach using dynamic field theory (DFT). We begin by providing an overview of how DFT seeks to understand the neural population dynamics that underlie cognitive processes through previous applications and comparisons to other modeling approaches. We then use previously published behavioral and neural data from a response selection Go/Nogo task as a case study for model simulations. Results from this study served as the 'standard' for comparisons with a model-based fMRI approach using dynamic neural fields (DNF). The tutorial explains the rationale and hypotheses involved in the process of creating the DNF architecture and fitting model parameters. Two DNF models, with similar structure and parameter sets, are then compared. Both models effectively simulated reaction times from the task as we varied the number of stimulus-response mappings and the proportion of Go trials. Next, we directly simulated hemodynamic predictions from the neural activation patterns from each model. These predictions were tested using general linear models (GLMs). Results showed that the DNF model that was created by tuning parameters to capture simultaneously trends in neural activation and behavioral data quantitatively outperformed a Standard GLM analysis of the same dataset. Further, by using the GLM results to assign functional roles to particular clusters in the brain, we illustrate how DNF models shed new light on the neural populations' dynamics within particular brain regions. Thus, the present study illustrates how an interactive cognitive neuroscience model can be used in practice to bridge the gap between brain and behavior.

RELATING ACCUMULATOR MODEL PARAMETERS AND NEURAL DYNAMICS

Accumulator models explain decision-making as an accumulation of evidence to a response threshold. Specific model parameters are associated with specific model mechanisms, such as the time when accumulation begins, the average rate of evidence accumulation, and the threshold. These mechanisms determine both the within-trial dynamics of evidence accumulation and the predicted behavior. Cognitive modelers usually infer what mechanisms vary during decision-making by seeing what parameters vary when a model is fitted to observed behavior. The recent identification of neural activity with evidence accumulation suggests that it may be possible to directly infer what mechanisms vary from an analysis of how neural dynamics vary. However, evidence accumulation is often noisy, and noise complicates the relationship between accumulator dynamics and the underlying mechanisms leading to those dynamics. To understand what kinds of inferences can be made about decision-making mechanisms based on measures of neural dynamics, we measured simulated accumulator model dynamics while systematically varying model parameters. In some cases, decision- making mechanisms can be directly inferred from dynamics, allowing us to distinguish between models that make identical behavioral predictions. In other cases, however, different parameterized mechanisms produce surprisingly similar dynamics, limiting the inferences that can be made based on measuring dynamics alone. Analyzing neural dynamics can provide a powerful tool to resolve model mimicry at the behavioral level, but we caution against drawing inferences based solely on neural analyses. Instead, simultaneous modeling of behavior and neural dynamics provides the most powerful approach to understand decision-making and likely other aspects of cognition and perception.

Go-activation endures following the presentation of a stop-signal: evidence from startle

It has been proposed that, in a stop-signal task (SST), independent go- and stop-processes "race" to control behavior. If the go-process wins, an overt response is produced, whereas, if the stop-process wins, the response is withheld. One prediction that follows from this proposal is that, if the activation associated with one process is enhanced, it is more likely to win the race. We looked to determine whether these initiation and inhibition processes (and thus response outcomes) could be manipulated by using a startling acoustic stimulus (SAS), which has been shown to provide additional response activation. In the present study, participants were to respond to a visual go-stimulus; however, if a subsequent stop-signal appeared, they were to inhibit the response. The stop-signal was presented at a delay corresponding to a probability of responding of 0.4 (determined from a baseline block of trials). On stop-trials, a SAS was presented either simultaneously with the go-signal or stop-signal or 100, 150, or 200 ms following the stop-signal. Results showed that presenting a SAS during stop-trials led to an increase in probability of responding when presented with or following the stop-signal. The latency of SAS responses at the stop-signal + 150 ms and stop-signal + 200 ms probe times suggests that they would have been voluntarily inhibited but instead were involuntarily initiated by the SAS. Thus results demonstrate that go-activation endures even 200 ms following a stop-signal and remains accessible well after the response has been inhibited, providing evidence against a winner-take-all race between independent go- and stop-processes.

NEW & NOTEWORTHY

In this study, a startling acoustic stimulus (SAS) was used to determine whether response outcome could be manipulated in a stop-signal task. Results revealed that presenting a SAS during stop-signal trials led to an increase in probability of responding even when presented 200 ms following the stop-signal. The latency of SAS responses indicates that go-activation remains accessible and modifiable well after the response is voluntarily inhibited, providing evidence against an irrevocable commitment to inhibition.

How to withhold or replace a prepotent response: An analysis of the underlying control processes and their temporal dynamics

The present study isolated and compared ERP components associated with flexible behavior in two action-control tasks. The 'withhold' groups had to withhold all responses when a signal appeared. The 'change' groups had to replace a prepotent go response with a different response on signal trials. We proposed that the same chain of processes determined the effectiveness of action control in both tasks. Consistent with this idea, lateral (Experiment 1) and central (Experiment 2) signal presentation elicited the same perceptual and response-related components in both tasks with similar latencies. Thus, completely withholding a response and replacing a response required a similar chain of processes. Furthermore, latency analyses revealed intra-individual differences: When the signal occurred in the periphery, differences between fast and slow change trials arose at early perceptual stages; by contrast, differences arose at later processing stages when signal detection was easy but stimulus discrimination and response selection were harder.