![]() While these studies include a fixed sample with a mainly clinical focus, there are also those that use open “recruitment” of participants through a software application that can be downloaded and consequently reach a larger audience (crowd sourcing) than what is normally achieved with common sampling methods (e.g., Killingsworth and Gilbert, 2010 Dufau et al., 2011). Some recent studies have harnessed this advantage by acquiring participants’ self-reports on their current mood ( Courvoisier et al., 2010) as well as their cognitive performance at controlled time points during the day ( Tiplady et al., 2009 Kennedy et al., 2011). One clear advantage of using MDs over internet-based testing that relies mostly on stationary computers is the possibility to access participants over the whole day, anywhere that they happen to be at that particular time, allowing for unique opportunities for experimental intervention. While the popularity of internet-based psychological testing has grown rapidly over the last decade (see, Barak and Buchanan, 2004), the use of MDs for data collection is still in its infancy. This approach is not entirely new however, until recently, it has been promoted mainly within a clinical context where it is referred to as ambulatory assessment involving the acquisition of psychophysiological data and self-reports in natural settings (e.g., Fahrenberg, 1996). Thus, the natural environment is the authentic arena where psychological theories can be proven to transcend laboratory walls and stand the test of real-life situations. In real-life, cognitive processes are executed in noisy environments. ![]() However, the advent of handheld mobile devices (MDs e.g., smartphones) with processing power comparable to stationary systems has opened the door to transferring experiments from the laboratory to real-life settings while maintaining control over stimulus presentation. In experimental psychological research the control of confounding variables is weighed against the degree of ecological validity usually aiming to maximize control at the expense of ecological validity ( Brunswik, 1947). Although this has obvious advantages, it is often too resource demanding to reach a larger audience and obtain a broad sample. Traditionally, the laboratory functions as center stage for psychological experiments in general, and laterality research in particular. Furthermore, the present findings support the feasibility of using smartphones in conducting large-scale field experiments. Together, these results establish the current MD version as a valid and reliable method for administering the DL paradigm both in experimentally controlled as well as uncontrolled settings. Importantly, the size of the REA was not dependent on whether the participants chose to listen to the syllables in their native language or not. When tested in real-life settings (Experiment 2), participants ( N = 167) also showed a significant REA. ![]() The results of Experiment 1 indicated high reliability ( r ICC = 0.78) and validity ( r ICC = 0.76–0.82) of the MD version, which consistently showed the expected right ear advantage (REA). Second, we explored its ecological validity by collecting data from the general population by means of a free release of the MD version ( iDichotic) to the iTunes App Store (Experiment 2). First, we evaluated the retest reliability and concurrent validity of the DL paradigm in its MD version in two samples tested in controlled, laboratory settings (Experiment 1). In order to examine the feasibility of mobile devices (MD) in psychological research in general, and laterality research in particular, we developed a MD version of the widely used speech laterality test, the consonant-vowel dichotic listening (DL) paradigm, for use with iPhones/iPods. The recent advent of smartphone technology provides an ideal medium for such testing. Since cognitive processes usually take place in noisy environments, they should also be tested in these contexts. Most psychological experimentation takes place in laboratories aiming to maximize experimental control however, this creates artificial environments that are not representative of real-life situations.
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