Moving Developmental Research Online: Comparing In-Lab and Web-Based Studies of Model-Based Reinforcement Learning release_t6o3yggdwfcrvaq2mkuzkfli5u

by Kate Nussenbaum, Maximilian Scheuplein, Camille V. Phaneuf, Michael D. Evans, Catherine A. Hartley

Published in Collabra: Psychology by University of California Press.

(2020)

Abstract

For years, adult psychological research has benefitted from web-based data collection. There is growing interest in harnessing this approach to facilitate data collection from children and adolescents to address foundational questions about cognitive development. 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 value-based learning and decision-making. To address this question, 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 used the same paradigm 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) and replicated the main finding that the use of a "model-based" learning strategy increases with age. In addition, we adapted a new index of abstract reasoning (MaRs-IB; Chierchia et al. 2019) for use online, and replicated a key result from Potter et al. (2017), which found that abstract reasoning ability mediated the relation between age and model-based learning. Our re-analyses of two previous in-lab datasets alongside our analysis of our online dataset revealed few qualitative differences across task administrations. These findings suggest that with appropriate precautions, researchers can effectively examine developmental differences in learning computations through unmoderated, online experiments.
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