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.

Manual reaction times to visual, auditory, and tactile stimuli presented simultaneously, or with a delay, were measured to test for multisensory interaction effects in a simple detection task with redundant signals. Responses to trimodal stimulus combinations were faster than those to bimodal combinations, which in turn were faster than reactions to unimodal stimuli. Response enhancement increased with decreasing auditory and tactile stimulus intensity and was a U-shaped function of stimulus onset asynchrony. Distribution inequality tests indicated that the multisensory interaction effects were larger than predicted by separate activation models, including the difference between bimodal and trimodal response facilitation. The results are discussed with respect to previous findings in a focused attention task and are compared with multisensory integration rules observed in bimodal and trimodal superior colliculus neurons in the cat and monkey.

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Two models, a Poisson race model and a diffusion model, are fit to data from a perceptual matching task. In each model, information about the similarity or the difference between two stimuli accumulates toward thresholds for either response. Stimulus variables are assumed to influence the rate at which information accumulates, and response variables are assumed to influence the level of the response thresholds. Three experiments were conducted to assess the performance of each model. In Experiment 1, observers performed under different response deadlines; in Experiment 2, response bias was manipulated by changing the relative frequency of same and different stimuli. In Experiment 3, stimulus pairs were presented at three eccentricities: foveal, parafoveal, and peripheral. We examined whether the race and diffusion models could fit the response time and accuracy data through changes only in response parameters (for Experiments 1 and 2) or stimulus parameters (for Experiment 3). Comparisons between the two models suggest that the race model, which has not been studied extensively, can account for perceptual matching data at least as well as the diffusion model. Furthermore, without the constraints on the parameters provided by the experimental conditions, the diffusion and the race models are indistinguishable. This finding emphasizes the importance of fitting models across several conditions and imposing logical psychological constraints on the parameters of models.For close to 50 years, response time (RT) studies have been a major focus of attention in cognitive psychology (Hick, 1952;Hyman, 1953;Luce, 1986). Over this time, a great deal has been learned about how performance in cognitive tasks changes with such factors as stimulus intensity, response bias, and so forth. The relationship between RT and other behavioral variables, such as accuracy, is of considerable interest. For instance, it is well known that a person can decrease RT at the expense of decreasing accuracy; this is the ubiquitous speed-accuracy tradeoff that appears in most, if not all, cognitive tasks (Pachella, 1974).The most successful models of RT and accuracy and, consequently, of the speed-accuracy tradeoff are sequential sampling models. These models assume that, in Portions of this work were presented at the 23rd Annual Meeting of the Society for Mathematical Psychology, University ofToronto, 1990. The project was made possible with Grants MH-44640 from NIMH and SBR-9702291 from NSF. The authors thank In Jae Myung for advice on model comparison statistics and the reviewers, F. Gregory Ashby, Gordon Logan, and Philip Smith for many helpful comments that greatly improved this paper, as well as Lester Krueger for comments on an earlier draft. Correspondence concerning this article should be addressed to T. Van Zandt, Psychology Department, Ohio State University, 1885 Neil Avenue, Columbus, OH 43210-1222. a choice response task, an observer engages a process of sequentially sampling from the stimulus that results in a gradual accumulat...

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Abstract& Saccadic reaction time to visual targets tends to be faster when stimuli from another modality (in particular, audition and touch) are presented in close temporal or spatial proximity even when subjects are instructed to ignore the accessory input (focused attention task). Multisensory interaction effects measured in neural structures involved in saccade generation (in particular, the superior colliculus) have demonstrated a similar spatio-temporal dependence. Neural network models of multisensory spatial integration have been shown to generate convergence of the visual, auditory, and tactile reference frames and the sensorimotor coordinate transformations necessary for coordinated head and eye movements. However, because these models do not capture the temporal coincidences critical for multisensory integration to occur, they cannot easily predict multisensory effects observed in behavioral data such as saccadic reaction times. This article proposes a quantitative stochastic framework, the time-window-of-integration model, to account for the temporal rules of multisensory integration. Saccadic responses collected from a visual-tactile focused attention task are shown to be consistent with the time-window-of-integration model predictions. &

An inequality by J. O. Miller (1982) has become the standard tool to test the race model for redundant signals reaction times (RTs), as an alternative to a neural summation mechanism. It stipulates that the RT distribution function to redundant stimuli is never larger than the sum of the distribution functions for 2 single stimuli. When many different experimental conditions are to be compared, a numerical index of violation is very desirable. Widespread practice is to take a certain area with contours defined by the distribution functions for single and redundant stimuli. Here this area is shown to equal the difference between 2 mean RT values. This result provides an intuitive interpretation of the index and makes it amenable to simple statistical testing. An extension of this approach to 3 redundant signals is presented.Keywords: redundant signals, race model inequality, negative dependenceIn the redundant signals paradigm for simple reaction time (RT), the observer must initiate a response as quickly as possible following the detection of any stimulus onset. A typical finding is that of redundancy gain: Responses are faster, on average, when two or more signals are presented simultaneously than when a single signal appears. Since the pioneering study by Todd (1912), this redundant signals effect (RSE) has been replicated many times for both manual and saccadic RTs, and under different experimental settings, for example, comparing uni-versus multimodal stimulation (Amlôt, Walker, Driver, & Spence, 2003;Diederich, 1995;Diederich & Colonius, 1987;Diederich, Colonius, Bockhorst, & Tabeling, 2003;Gielen, Schmidt, & Van den Heuvel, 1983;Hughes, Nelson, & Aronchick, 1998;Miller, 1982Miller, , 1986Molholm, Ritter, Javitt, & Foxe, 2004), single versus multiple stimuli within the same modality (e.g., Schwarz & Ischebeck, 1994), or monocular versus binocular stimulation (Blake, Martens, & DiGianfillipo, 1980;Westendorf & Blake, 1988) and also for specific populations (e.g., Corballis, 1998; Marzi et al., 1996, for hemianopics;Miller, 2004, for individuals who have undergone split-brain surgery; Reuter-Lorenz, Nozawa, Gazzaniga, & Hughes, 1995;. Raab (1962) was the first to propose a race model for simple RT such that (a) each individual stimulus elicits a detection process performed in parallel to the others and (b) the winner's time determines the observable RT. This model suggests that RSE is generated by statistical facilitation: If detection latencies are interpreted as (nonnegative) random variables, the time to detect the first of several redundant signals is faster, on average, than the detection time for any single signal. A generalization of Raab's model was recently developed in Miller and Ulrich (2003).Testing the race model amounts to testing whether an observed RT speed-up is too large to be attributed to statistical facilitation (viz., probability summation). The race model inequality (RMI) proposed in Miller (1982) has become the standard testing tool in many RT studies. 1 It stipulates that the RT dist...

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