Moving developmental research online: comparing in-lab and web-based studies of model-based reinforcement learning
For years, adult psychological research has benefitted from remote data collection via web-based experiments. There is growing interest in harnessing this approach to facilitate data collection from children and adolescents to address foundational questions about cognitive development in large and diverse participant samples. To date, however, few studies have directly tested whether findings from in-lab developmental psychology tasks can be replicated online, particularly in the domain of
... the domain of value-based learning and decision-making. These tasks, and the computational characterization of learning processes, typically require participants to make many, repeated decisions over a lengthy experimental session (30 - 60 minutes). To address whether we can obtain high-quality data in such tasks, we set up a pipeline for online data collection with children, adolescents, and adults, and conducted a replication of Decker et al. (2016). The original in-lab study employed a sequential decision-making paradigm to examine shifts in value-learning strategies from childhood to adulthood. Here, we employed the same paradigm and, in a sample of 151 children (N = 50; ages 8 - 12 years), adolescents (N = 50; ages 13 - 17 years), and adults (N = 51; ages 18 - 25 years) replicated the main finding that the use of a "model-based" learning strategy increases from childhood to adulthood. In addition, we adapted a new index of abstract reasoning (MaRs-IB; Chierchia, Furhmann et al. (2019)) for use online, and replicated a key result from Potter, Bryce et al. (2017), which found that age-related differences in abstract reasoning mediated the relation between age and model-based learning. Our re-analyses of Decker et al. (2016) and Potter, Bryce et al. (2017) alongside our new analysis of our online dataset revealed few qualitative differences across the in-lab and online task administrations. Taken together, these findings suggest that with appropriate precautions, researchers can effectively examine developmental differences in learning and decision-making computations through unmoderated, online experiments.