Developmental changes in the reliance on

Developmental changes in the reliance on model-based learning might also reflect an increasing capacity to recruit learned cognitive models to guide decisions. Working memory, the ability to maintain mental representations in an active state despite interference, is a key component of model recruitment (D’Esposito and Postle, 2015). Introducing working memory load during decision-making reduces adults’ use of a model-based strategy (Otto et al., 2013a), and high working memory capacity ace inhibitors individuals from stress-induced impairment of model-based learning (Otto et al., 2013b). Another important process potentially underlying successful model recruitment is fluid reasoning, the capacity to flexibly integrate independent goal-relevant associations across domains. Fluid reasoning involves the reorganization, transformation, and extrapolation of learned conceptual relationships in order to solve novel problems (Cattell, 1987; McArdle et al., 2002). Both working memory and fluid reasoning have been shown to increase from early childhood into young adulthood (Ferrer et al., 2009; Fry and Hale, 1996), suggesting that either of these processes, or their integrated function, may foster increased recruitment of model-based choice. Building upon a previous finding that model-based reinforcement learning increased with age from childhood into adulthood (Decker et al., 2016), in this study, we sought to characterize the cognitive underpinnings of this developmental trajectory. Given previous observations of age-related changes in statistical learning, working memory, and fluid reasoning, we examined the contributions of these putative component processes to the development of model-based choice in a sequential reinforcement-learning task. We found that fluid reasoning, but not statistical learning, mediated the relationship between age and model-based choice. Ceiling performance on our working memory assay prevented examination of its contribution to model-based learning. Collectively, these findings suggest that the protracted development of fluid reasoning ability may be a critical process underpinning the gradual emergence of model-based learning.
Methods
Results
Discussion In this study, we sought to elucidate the cognitive components that underlie the developmental emergence of model-based learning. Replicating previous findings in this sequential reinforcement-learning task (Decker et al., 2016), we found that whereas model-free learning was evident across our developmental sample, model-based choice exhibited a protracted maturational trajectory, only becoming evident in adolescence and continuing to strengthen into adulthood. We examined whether developmental changes in statistical learning ability, working memory, and fluid reasoning might contribute to the increased recruitment of model-based choice with age. We found that statistical learning performance was evident in children and improved with age. However, these improvements in statistical learning did not account for age-related increases in model-based choice. In contrast, fluid reasoning increased with age and significantly mediated the relationship between age and model-based learning. Collectively, these results suggest that the ability to integrate distinct learned associations, and not merely the acquisition of those associations, is a critical cognitive component underlying the gradual development of model-based choice. Although children did not show evidence of model-based learning, Pangaea demonstrated knowledge of the task transition structure. Children, like adolescents and adults, could explicitly describe the task structure and were also slower to respond following rare transitions, reflecting sensitivity to these less frequent outcomes. Notably, whereas adults’ response time sensitivity was apparent early in the task, this sensitivity only emerged later in children. Adults are able to rapidly incorporate explicit instruction to inform their actions (Cole et al., 2013), and may have used the task description provided in our tutorial to scaffold a cognitive model of the probabilistic transition structure. In contrast, younger participants, who tend to rely on experiential over instructed knowledge (Decker et al., 2015), may have had greater difficulty recruiting this instruction to inform choices. Thus, providing instruction may have facilitated adults’ recruitment of a model-based strategy. Younger participants may instead have learned the task structure predominantly through the experience they accumulated over many trials. This proposal is consistent with the observed correlation between response time slowing and participants’ performance on the statistical learning task, in which sequential regularities were learned solely through experience. Future studies might test whether children exhibit model-based choice if this learned task structure knowledge grows more “crystallized” through extensive practice.