Many researchers in the behavioral sciences depend on research software that presents stimuli, and records response times, with sub-millisecond precision. There are a large number of software packages with which to conduct these behavioral experiments and measure response times and performance of participants. Very little information is available, however, on what timing performance they achieve in practice. Here we report a wide-ranging study looking at the precision and accuracy of visual and auditory stimulus timing and response times, measured with a Black Box Toolkit. We compared a range of popular packages: PsychoPy, E-Prime®, NBS Presentation®, Psychophysics Toolbox, OpenSesame, Expyriment, Gorilla, jsPsych, Lab.js and Testable. Where possible, the packages were tested on Windows, macOS, and Ubuntu, and in a range of browsers for the online studies, to try to identify common patterns in performance. Among the lab-based experiments, Psychtoolbox, PsychoPy, Presentation and E-Prime provided the best timing, all with mean precision under 1 millisecond across the visual, audio and response measures. OpenSesame had slightly less precision across the board, but most notably in audio stimuli and Expyriment had rather poor precision. Across operating systems, the pattern was that precision was generally very slightly better under Ubuntu than Windows, and that macOS was the worst, at least for visual stimuli, for all packages. Online studies did not deliver the same level of precision as lab-based systems, with slightly more variability in all measurements. That said, PsychoPy and Gorilla, broadly the best performers, were achieving very close to millisecond precision on several browser/operating system combinations. For response times (measured using a high-performance button box), most of the packages achieved precision at least under 10 ms in all browsers, with PsychoPy achieving a precision under 3.5 ms in all. There was considerable variability between OS/browser combinations, especially in audio-visual synchrony which is the least precise aspect of the browser-based experiments. Nonetheless, the data indicate that online methods can be suitable for a wide range of studies, with due thought about the sources of variability that result. The results, from over 110,000 trials, highlight the wide range of timing qualities that can occur even in these dedicated software packages for the task. We stress the importance of scientists making their own timing validation measurements for their own stimuli and computer configuration.

Many researchers in the behavioral sciences depend on research software that presents stimuli, and records response times, with sub-millisecond precision. There are a large number of software packages with which to conduct these behavioural experiments and measure response times and performance of participants. Very little information is available, however, on what timing performance they achieve in practice. Here we report a wide-ranging study looking at the precision and accuracy of visual and auditory stimulus timing and response times, measured with a Black Box Toolkit. We compared a range of popular packages: PsychoPy, E-Prime®, NBS Presentation®, Psychophysics Toolbox, OpenSesame, Expyriment, Gorilla, jsPsych, Lab.js and Testable. Where possible, the packages were tested on Windows, MacOS, and Ubuntu, and in a range of browsers for the online studies, to try to identify common patterns in performance. Among the lab-based experiments, Psychtoolbox, PsychoPy, Presentation and E-Prime provided the best timing, all with mean precision under 1 millisecond across the visual, audio and response measures. OpenSesame had slightly less precision across the board, but most notably in audio stimuli and Expyriment had rather poor precision. Across operating systems, the pattern was that precision was generally very slightly better under Ubuntu than Windows, and that Mac OS was the worst, at least for visual stimuli, for all packages. Online studies did not deliver the same level of precision as lab-based systems, with slightly more variability in all measurements. That said, PsychoPy and Gorilla, broadly the best performers, were achieving very close to millisecond precision on a number of browser configurations. For response times (using a high-performance button box), most of the packages achieved precision at least under 10 ms in all browsers, with PsychoPy achieving a precision under 3.5 ms in all. There was considerable variability between operating systems and browsers, especially in audio-visual synchrony which is the least precise aspect of the browser-based experiments. Nonetheless, the data indicate that online methods can be suitable for a wide range of studies, with due thought about the sources of variability that result.The results, from over 110,000 trials, highlight the wide range of timing qualities that can occur even in these dedicated software packages for the task. We stress the importance of scientists making their own timing validation measurements for their own stimuli and computer configuration.

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Concealed information tests (CITs) are used to determine whether an individual possesses information about an item of interest. Event-related potential (ERP) measures in CITs have focused almost exclusively on the P3b component, showing that this component is larger when lying about the item of interest (probe) than telling the truth about control items (irrelevants). Recent studies have begun to examine other ERP components, such as the anterior N2, with mixed results. A seminal CIT study found that visual probes elicit a larger anterior N2 than irrelevants (Gamer and Berti, 2010) and suggested that this component indexes cognitive control processes engaged when lying about probes. However, this study did not control for potential intrinsic differences among the stimuli: the same probe and irrelevants were used for all participants, and there was no control condition composed of uninformed participants. Here, first we show that the N2 effect found in the study by Gamer and Berti (2010) was in large part due to stimulus differences, as the effect observed in a concealed information condition was comparable to that found in two matched control conditions without any concealed information (Experiments 1 and 2). Next, we addressed the issue of the generality of the N2 findings by counterbalancing a new set of stimuli across participants and by using a control condition with uninformed participants (Experiment 3). Results show that the probe did not elicit a larger anterior N2 than the irrelevants under these controlled conditions. These findings suggest that caution should be taken in using the N2 as an index of concealed information in CITs. Furthermore, they are a reminder that results of CIT studies (not only with ERPs) performed without stimulus counterbalancing and suitable control conditions may be confounded by differential intrinsic properties of the stimuli employed.

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Motion extrapolation (ME), the ability to estimate the current position of moving objects hidden by an occluder, is critical to interact with a dynamic environment. In a typical paradigm, participants estimate time to contact (TTC) by pressing a button when they estimate the occluded moving target reaches a certain cue. Research using this paradigm has shown that motion adaptation of the occluded area produces a shift in the TTC estimate (Gilden et al., 1995). We examined the effect of motion adaptation on the contingent negative variation (CNV), a frontal electrophysiological component (Tecce, 1972) that could reflect the activity of an accumulator (Buhusi and Meck, 2005) for time processing. We predicted that longer TTC estimates due to previous visual motion adaptation would result in a larger CNV because the accumulator can collect more time units. Results showed that motion adaptation actually modulates the CNV, but the CNV amplitude did not correlate with TTC duration, falsifying the accumulator hypothesis. We suggest that motion adaptation interferes with the remembered speed (stored during the visible part of the trajectory) that may be used as input by higher cognitive function to guide the temporal update of target position, regardless of the TTC estimate.

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