There has been discussion over the extent to which delay discounting -as prototypically shown by a preference for a smaller-sooner sum of money over a larger-later sum -measures the same kind of impulsive preferences that drive non-financial behavior. To address this issue a dataset was analyzed, containing 42,863 participants' responses to a single delaydiscounting choice, along with self-report behaviors that can be considered as impulsive.Choice of a smaller-sooner sum was associated with several demographics: younger age, lower income, lower education; and impulsive behaviors: earlier age of first sexual activity and recent relationship infidelity, smoking, and higher body mass index. These findings suggest that at least an aspect of delay discounting preference is associated with a general trait influencing other forms of impulsivity, and therefore that high delay discounting is another form of impulsive behavior.

The authors investigated age-related changes in executive control using an Internet-based task-switching experiment with 5,271 participants between the ages of 10 and 66 years. Speeded face categorization was required on the basis of gender (G) or emotion (E) in single task blocks (GGG... and EEE...) or switching blocks (GGEEGGEE...). General switch costs, the difference between switching block and single task block performance, decreased during development and then increased approximately linearly from age 18. In contrast, specific switch costs, the difference between switch trial and nonswitch trial performance in the switching block, were more stable across the same age range. These results demonstrate differential age effects in task-switching performance and provide a fine-grained analysis of switch costs from puberty to retirement.

Web-based research is becoming ubiquitous in the behavioral sciences, facilitated by convenient, readily available participant pools and relatively straightforward ways of running experiments: most recently, through the development of the HTML5 standard. Although in most studies participants give untimed responses, there is a growing interest in being able to record response times online. Existing data on the accuracy and cross-machine variability of online timing measures are limited, and generally they have compared behavioral data gathered on the Web with similar data gathered in the lab. For this article, we took a more direct approach, examining two ways of running experiments online—Adobe Flash and HTML5 with CSS3 and JavaScript—across 19 different computer systems. We used specialist hardware to measure stimulus display durations and to generate precise response times to visual stimuli in order to assess measurement accuracy, examining effects of duration, browser, and system-to-system variability (such as across different Windows versions), as well as effects of processing power and graphics capability. We found that (a) Flash and JavaScript’s presentation and response time measurement accuracy are similar; (b) within-system variability is generally small, even in low-powered machines under high load; (c) the variability of measured response times across systems is somewhat larger; and (d) browser type and system hardware appear to have relatively small effects on measured response times. Modeling of the effects of this technical variability suggests that for most within- and between-subjects experiments, Flash and JavaScript can both be used to accurately detect differences in response times across conditions. Concerns are, however, noted about using some correlational or longitudinal designs online.Electronic supplementary materialThe online version of this article (doi:10.3758/s13428-014-0471-1) contains supplementary material, which is available to authorized users.

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