Response inhibition is a hallmark of executive control. The concept refers to the suppression of nolonger required or inappropriate actions, which supports flexible and goal-directed behavior in everchanging environments. The stop-signal paradigm is most suitable for the study of response inhibition in a laboratory setting. The paradigm has become increasingly popular in cognitive psychology, cognitive neuroscience and psychopathology. We review recent findings in the stop-signal literature with the specific aim of demonstrating how each of these different fields contributes to better understanding of the processes involved in inhibiting a response and monitoring stopping performance, and more generally, discovering how behavior is controlled.People can readily stop talking, walking, typing, etc., in response to changes in internal states or changes in the environment. This ability to inhibit inappropriate or irrelevant responses is a hallmark of executive control. The role of inhibition in many experimental paradigms is debated, but most researchers agree that some kind of inhibition is involved in deliberately stopping a motor response. In this article, we focus on the stop-signal paradigm [1], which has proven to be a useful tool for the study of response inhibition in cognitive psychology, cognitive neuroscience and psychopathology. We review recent developments in the stop-signal paradigm in these different fields. The focus is primarily on the inhibition of manual responses. Studies of oculomotor inhibition are discussed in Box 1. Successful stopping: Inhibition and performance monitoringIn the stop-signal paradigm, subjects perform a go task, such as reporting the identity of a stimulus. Occasionally, the go stimulus is followed by a stop signal, which instructs subjects to withhold the response (see Figure 1). Stopping a response requires a fast control mechanism that prevents the execution of the motor response [1]. This process interacts with slower control mechanisms that monitor and adjust performance [2]. The race between going and stoppingPerformance in the stop-signal paradigm is modeled as a race between a go process, which is triggered by the presentation of the go stimulus, and a stop process, which is triggered by the presentation of the stop signal. When the stop process finishes before the go process, the response is inhibited; when the go processes finishes before the stop process, the response is emitted. The latency of the stop process (stop-signal reaction time; SSRT) is covert and must

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.

The stop-signal paradigm is very useful for the study of response inhibition. Stop-signal performance is typically described as a race between a go process, triggered by a go stimulus, and a stop process, triggered by the stop signal. Response inhibition depends on the relative finishing time of these two processes. Numerous studies have shown that the independent horse-race model of Logan and Cowan (1984) accounts for the data very well. In the present article, we review the independent horse-race model and related models, such as the interactive horse-race model (Boucher, Palmeri, Logan & Schall, 2007). We present evidence that favors the independent horse-race model but also some evidence that challenges the model. We end with a discussion of recent models that elaborate the role of a stop process in inhibiting a response.

The task-switching paradigm is being increasingly used as a tool for studying cognitive control and task coordination. Different procedural variations have been developed. They have in common that a comparison is made between transitions in which the previous task is repeated and transitions that involve a change toward another task. In general, a performance switch cost is observed such that switching to a new task results in a slower and more error-prone execution of the task. The present article reviews the theoretical explanations of the switch cost and the findings collected in support of those explanations. Resolution and protection from interference by previous events explain part of the switching cost, but processes related to task setting and task preparation also play a prominent role, as testified by faster execution and lower switch costs when the preparation time is longer. The authors discuss the evidence in favor of each of these sets of accounts and raise a number of questions that situate task switching in a broader context of cognitive control processes. The role of several aspects of the task set, including task variations, task-set overlap, and task-set structure, is addressed, as is the role of knowledge about probability of task changes and about the structure of task sequences.

In five experiments, the authors examined the development of automatic response inhibition in the go/no-go paradigm and a modified version of the stop-signal paradigm. They hypothesized that automatic response inhibition may develop over practice when stimuli are consistently associated with stopping. All five experiments consisted of a training phase and a test phase in which the stimulus mapping was reversed for a subset of the stimuli. Consistent with the automatic-inhibition hypothesis, the authors found that responding in the test phase was slowed when the stimulus had been consistently associated with stopping in the training phase. In addition, they found that response inhibition benefited from consistent stimulus-stop associations. These findings suggest that response inhibition may rely on the retrieval of stimulus-stop associations after practice with consistent stimulus-stop mappings. Stimulus-stop mapping is typically consistent in the go/no-go paradigm, so automatic inhibition is likely to occur. However, stimulus-stop mapping is typically inconsistent in the stop-signal paradigm, so automatic inhibition is unlikely to occur. Thus, the results suggest that the two paradigms are not equivalent because they allow different kinds of response inhibition.

In the stop-signal paradigm, fast responses are harder to inhibit than slow responses, so subjects must balance speed on the go task with successful stopping in the stop task. In theory, subjects achieve this balance by adjusting response thresholds for the go task, making proactive adjustments in response to instructions that indicate that relevant stop signals are likely to occur. We report five experiments that tested this theoretical claim, presenting cues that indicated whether or not stop signals were relevant for the next few trials. Subjects made proactive response-strategy adjustments in each experiment: diffusion-model fits showed that response threshold increased when subjects expected stop signals to occur, slowing go responses and increasing accuracy. Furthermore, our results show that subjects can make proactive response-strategy adjustments on a trial-by-trial basis, suggesting a flexible cognitive system that can proactively adjust itself in changing environments.Keywords response strategies; proactive control adjustments; stop-signal paradigm; reaction time models Cognitive control processes are required to achieve a balance between competing goals in everchanging environments (Baddeley, 1996;Logan, 1985;Miller & Cohen, 2001). These control processes allow people to adjust response strategies in cognitively demanding situations. Response-strategy adjustments are typically investigated by manipulating task instructions (e.g., Howell & Kreidler, 1963;Rinkenauer, Osman, Ulrich, Muller-Gethmann & Mattes, 2004), by presenting distracting information (e.g., Logan & Zbrodoff, 1979, 1982 or by analyzing trials following an error or response conflict (e.g., Botvinick, Braver, Barch, Carter & Cohen, 2001;Gratton, Coles & Donchin, 1992;Rabbitt, 1966Rabbitt, , 1968. In the present study, we investigated how people adjust response strategies in a multi-tasking situation know as the stop-signal paradigm (Lappin & Eriksen, 1966;Vince, 1948). The stop-signal paradigm involves a trade-off between two tasks with opposing requirements: a go task, which requires subjects to respond as quickly as possible when a stimulus is presented, and a stop task, which requires subjects to stop the response when a stop signal is presented. Success on the go task (fast responding) implies failure on the stop task (not stopping a response); success on the stop task (stopping a response) implies failure on the go task (slow Correspondence Address: Frederick Verbruggen, Department of Psychology, Vanderbilt University, Nashville, TN 37203, Phone: (615) 322-5169, Fax: (615) 343-8449, E-mail: E-mail: frederick.verbruggen@ugent.be. Publisher's Disclaimer: The following manuscript is the final accepted manuscript. It has not been subjected to the final copyediting, fact-checking, and proofreading required for formal publication. It is not the definitive, publisher-authenticated version. The American Psychological Association and its Council of Editors disclaim any responsibility or liabilities for errors or omissions of this man...

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Response inhibition is an important act of control in many domains of psychology and neuroscience. It is often studied in a stop-signal task that requires subjects to inhibit an ongoing action in response to a stop signal. Performance in the stop-signal task is understood as a race between a go process that underlies the action and a stop process that inhibits the action. Responses are inhibited if the stop process finishes before the go process. The finishing time of the stop process is not directly observable; a mathematical model is required to estimate its duration. developed an independent race model that is widely used for this purpose. We present a general race model that extends the independent race model to account for the role of choice in go and stop processes, and a special race model that assumes each runner is a stochastic accumulator governed by a diffusion process. We apply the models to 2 data sets to test assumptions about selective influence of capacity limitations on drift rates and strategies on thresholds, which are largely confirmed. The model provides estimates of distributions of stop-signal response times, which previous models could not estimate. We discuss implications of viewing cognitive control as the result of a repertoire of acts of control tailored to different tasks and situations.