Jeff Miller

Recent research on voluntary task switching indicates that people use different strategies to manage multitasking constraints. In this study, we conducted two experiments to investigate individual task selection behavior and provide empirical evidence for two theoretically derived strategies-local and global-aimed at balancing time-related costs in a self-organized task switching paradigm. We implemented a delay for the stimulus required for task repetition (i.e., stimulus-onset asynchrony [SOA]), which increased with each consecutive repetition until a task switch reset the SOA. Thus, repeating a task required waiting, while switching incurred performance costs. We examined individual waiting times at which participants chose to switch tasks (switch SOA) and their task performance (switch costs) across conditions. Results revealed that some participants had switch SOAs similar to their switch costs, consistent with a local strategy. Others showed considerably smaller switch SOAs compared to their switch costs, aligning with the global strategy. These individuals likely accounted for the fact that task switching in this paradigm reduces waiting times in subsequent trials. Importantly, our visual observations of behavioral patterns were confirmed using the expectation-maximization method, a technique sometimes applied in machine learning, providing statistical support for the existence of these two strategies. Overall, the findings suggest that individuals differ in their preferred task selection strategies, with preferences remaining relatively stable across varying experimental conditions. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

A common finding across numerous response time (RT) paradigms is that the mean RT in one trial depends strongly on the characteristics of the immediately preceding trial. Although such sequential effects have usually only been considered within each single paradigm in isolation from the others, there are important similarities across paradigms between the theoretical accounts of these effects. However, so far there has been no systematic comparison of sequential effects across paradigms. To investigate the possible relationships between sequential effects in different paradigms, we conducted an experiment examining sequential effects in visual search, two-choice RT, interference, and task-switching paradigms, using methods designed to maximize the similarity of stimuli and responses across paradigms. Detailed analyses of the observed RT distributions were carried out using both descriptive (e.g., ex-Gaussian) and process-oriented (e.g., diffusion models) methods. The results reveal significant empirical similarities and differences between the sequential effects observed across different paradigms, and in some cases even across different conditions within a single paradigm. Furthermore, the sequential effects are more similar to one another for some pairs of paradigms than for others. These results imply that some cognitive processes eliciting sequential effects are shared across paradigms while others seem to be paradigm-specific.

A methodological problem in most reaction time (RT) studies is that some measured RTs may be outliers—that is, they may be very fast or very slow for reasons unconnected to the task-related processing of interest. Numerous ad hoc methods have been suggested to discriminate between such outliers and the valid RTs of interest, but it is extremely difficult to determine how well these methods work in practice because virtually nothing is known about the actual characteristics of outliers in real RT datasets. This article proposes a new method of pooling cumulative distribution function values for examining empirical RT distributions to assess both the proportions of outliers and their latencies relative to those of the valid RTs. As the method is developed, its strengths and weaknesses are examined using simulations based on previously suggested ad hoc models for RT outliers with particular assumed proportions and distributions of valid RTs and outliers. The method is then applied to several large RT datasets from lexical decision tasks, and the results provide the first empirically based description of outlier RTs. For these datasets, fewer than 1% of the RTs seem to be outliers, and the median outlier latency appears to be approximately 4–6 standard deviations of RT above the mean of the valid RT distribution.

A methodological problem in most reaction time (RT) tasks is that some measured RTs may be outliers, being either too fast or too slow to reflect the task-related processing of interest. Numerous ad hoc procedures have been used to identify these outliers for exclusion from further analyses, but the accuracies of these methods have not been systematically compared. The present study compared the performance of 58 different outlier exclusion procedures (OEPs) using four huge datasets of real RTs. The results suggest that these OEPs are likely to do more harm than good, because they incorrectly identify outliers, increase noise, introduce bias, and generally reduce statistical power. The results suggest that RT researchers should not automatically apply any of these OEPs to clean their RT data prior to the main analyses. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Contrary to the warning of Miller (1988), Rousselet and Wilcox (2020) argued that it is better to summarize each participant's single-trial reaction times (RTs) in a given condition with the median than with the mean when comparing the central tendencies of RT distributions across experimental conditions. They acknowledged that median RTs can produce inflated Type~I error rates when conditions differ in the number of trials tested, consistent with Miller's warning, but they showed that the bias responsible for this error rate inflation could be eliminated with a bootstrap bias correction technique. The present simulations extend their analysis by examining the power of bias-corrected medians to detect true experimental effects and by comparing this power with the power of analyses using means and regular medians. Unfortunately, although bias corrected medians solve the problem of inflated Type~I error rates, their power is lower than that of means or regular medians in many realistic situations. In addition, even when conditions do not differ in the number of trials tested, the power of tests (e.g., t-tests) is generally lower using medians rather than means as the summary measures. Thus, the present simulations demonstrate that summary means will often provide the most powerful test for differences between conditions, and they show what aspects of the RT distributions determine the size of the power advantage for means.

Performance is generally worse when performing multiple tasks than when performing a single task, but there is debate about whether this multitasking interference arises due to a structural bottleneck that requires serial central processing or due to resource limitations that slow processing of 2 tasks when they are carried out in parallel. The present study used a novel approach of comparing first- and second-task reaction times (RTs) within the psychological refractory period (PRP) paradigm to contrast these 2 possibilities. Counterbalancing task order across participants to control for differences in task difficulty, we found that second-task responses were faster than first-task responses at long stimulus onset asynchronies (SOAs). This second-task advantage is difficult to explain within bottleneck models, which allow the first task to be processed at full speed while the second task waits for access to the bottleneck process. Instead, the effect suggests that processing of the first task is slowed because some cognitive resources are held back in case they are needed for second-task processing. At long SOAs, all resources can be allocated to second-task processing because the first task is already completed. Thus, we propose that cognitive control processes flexibly coordinating the sharing of limited central resources may better explain dual-task performance in the PRP paradigm than bottleneck-based waiting due to structural limitations. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Recent evidence suggests that research practices in psychology and many other disciplines are far less effective than previously assumed, which has led to what has been called a “crisis of confidence” in psychological research (e.g., Pashler & Wagenmakers 2012 ). In response to the perceived crisis, standard research practices have come under intense scrutiny, and various changes have been suggested to improve them. The burgeoning field of metascience seeks to use standard quantitative data-gathering and modeling techniques to understand the reasons for inefficiency, to assess the likely effects of suggested changes, and ultimately to tell psychologists how to do better science. We review the pros and cons of suggested changes, highlighting the many complex research trade-offs that must be addressed to identify better methods.

We combined behavioral measures with electrophysiological measures of motor activation (i.e., lateralized readiness potentials, LRPs) to disentangle the relative contribution of premotor and motor processes to multitasking interference in the prioritized processing paradigm. Specifically, we presented stimuli of two tasks (primary and background task) in each trial, but participants were instructed to perform the background task only if the primary task required no response. As expected, task performance was substantially influenced by a task probability manipulation: Background task responses were faster, psychological refractory period effects were smaller, and interference from the second task (i.e., backward compatibility effects) was larger when there was a larger probability that this task required a response. Critically, stimulus‐locked and response‐locked LRP analyses indicate that these behavioral effects of parallel processing were not driven by background task motor processing (e.g., motoric response activation) taking place during primary task processing. Instead, the LRP results suggest that these effects were exclusively localized during premotor stages of processing (e.g., response selection). Thus, the present results generally provide evidence for multitasking accounts allowing parallel task processing during response selection, whereas the task‐specific motor responses are activated in a serial manner. One plausible account is that multiple task information sources can be processed in parallel, with sharing of limited cognitive resources depending on task relevance, but a primary and still active task goal prevents motor activation related to the goals of other tasks in order to avoid outcome conflict.

We recently developed a simple and general sequential sampling method for testing null hypotheses, the independent segments procedure (ISP; Miller & Ulrich, 2021). In this reply, we discuss the comments of Erdfelder and Schnuerch (2021) and Lakens (2021), who consider alternative methods such as the sequential probability ratio test (SPRT) and the group sequential design (GSD), respectively. We evaluate the pros and cons of these alternatives and conclude that the ISP does have several advantages over these other methods, especially for psychological research. All of these sequential methods can save research resources because smaller sample sizes are required compared to standard nonsequential methods, so it seems appropriate for researchers to choose froma variety of sequentialmethods based on the practical requirements of their research. Translational Abstract We recently developed a novel sequential sampling method, the independent segments procedure (ISP), to test statistical null hypotheses. This article compares ISP with two previous alternative methods: the sequential probability ratio test (SPRT) and the group sequential design (GSD). Each of the three methods is suitable for saving research resources because all require smaller sample sizes than standard inferential statistical methods. This article compares the advantages and disadvantages of these three methods, highlighting situations in which each would be more appropriate than the others. Therefore, this article may help researchers determine the most appropriate sequential sampling procedure for their particular application.