Performing online behavioral research is gaining increased popularity among researchers in psychological and cognitive science. However, the currently available methods for conducting online reaction time experiments are often complicated and typically require advanced technical skills. In this article, we introduce the Qualtrics Reaction Time Engine (QRTEngine), an open-source JavaScript engine that can be embedded in the online survey development environment Qualtrics. The QRTEngine can be used to easily develop browser-based online reaction time experiments with accurate timing within current browser capabilities, and it requires only minimal programming skills. After introducing the QRTEngine, we briefly discuss how to create and distribute a Stroop task. Next, we describe a study in which we investigated the timing accuracy of the engine under different processor loads using external chronometry. Finally, we show that the QRTEngine can be used to reproduce classic behavioral effects in three reaction time paradigms: a Stroop task, an attentional blink task, and a masked-priming task. These findings demonstrate that QRTEngine can be used as a tool for conducting online behavioral research even when this requires accurate stimulus presentation times.Electronic supplementary materialThe online version of this article (doi:10.3758/s13428-014-0530-7) contains supplementary material, which is available to authorized users.

Using an observational sample of players of a simple online game (n > 1.2 million), we are able to trace the development of skill in that game. Information on playing time, and player location, allows us to estimate time of day during which practice took place. We compare those whose breaks in practice probably contained a night's sleep and those whose breaks in practice probably did not contain a night's sleep. Our analysis confirms experimental evidence showing a benefit of spacing for skill learning, but fails to find any additional benefit of sleeping during a break from practice. We discuss reasons why the well established phenomenon of sleep consolidation might not manifest in an observational study of skill development. We put the spacing effect into the context of the other known influences on skill learning: improvement with practice, and individual differences in initial performance. Analysis of performance data from games allows experimental results to be demonstrated outside of the lab, and for experimental phenomenon to be put in the context of the performance of the whole task.

The role of mid-cingulate cortex (MCC), also referred to as dorsal anterior cingulate cortex, in regulating cognitive control is a topic of primary importance in cognitive neuroscience. Although many studies have shown that MCC responds to cognitive demands, lesion studies in humans are inconclusive concerning the causal role of the MCC in the adaptation to these demands. By elegantly combining single-cell recordings with behavioural methods, Sheth et al. [Sheth, S. et al. Human dorsal anterior cingulate cortex neurons mediate ongoing behavioural adaptation. Nature 488, 218–22 (2012).] recently were able to show that neurons in MCC encode cognitive demand. Importantly, this study also claimed that focal lesions of the MCC abolished behavioural adaptation to cognitive demands. Here we show that the absence of post-cingulotomy behavioural adaptation reported in this study may have been due to practice effects. We run a control condition where we tested subjects before and after a dummy treatment, which substituted cingulotomy with a filler task (presentation of a documentary). The results revealed abolished behavioural adaptation following the dummy treatment. Our findings suggest that future work using proper experimental designs is needed to advance the understanding of the causal role of the MCC in behavioural adaptation.

Behavioural biometrics looks at discriminative features of a person's measurable behaviour, which is known to show high variance over long stretches of time. In psychology, a significant portion of this behavioural variance is explained by an individual improving their skill at performing behaviours, mostly through practice. Understanding what the effects of practice are on biometric recognition performance should allow us to account for much of this variance, as well as make individual behavioural biometric studies easier to compare [15]. We hypothesize that more accumulated practice will lead to both more stable and increased recognition performance. We argue that these are significant effects and show that practice in general is under-investigated. We introduce a novel method of analysis, the Start-to-Train Interval (STI)/Train-to-Test Interval (TTI) contour plot, which allows for systematic investigation of how recognition performance develops under increased practice. We applied this method to three data sets of a Discrete Sequence Production (DSP) task, a task that consists of repeatedly (500+ times) typing in a simple password, and found that more practice both significantly increases recognition performance and makes it more stable. These findings call for further investigation into the effects of practice on recognition performance for more standard behavioural biometric paradigms.