br Conclusion The origin of the

Conclusion The origin of the word pecan traces back to an Algonquin word referring to “nuts that require a stone to crack.” But hammering away at a pecan shell with a blunt, round, heavy stone mainly results in squashing the nut. We need thin, sharp stones – precise tools for extracting the meat of the nut. To further advance our science of adolescent development, which to this point has significantly benefited from the seminal dual-systems and social reorientation models (Shulman et al., 2016; Nelson et al., 2016), we propose that “audacious specificity” as concretized by the PECANS checklist will help us all work together to conduct even more cumulatively meaningful work. Ideally, the PECANS checklist would be consulted prior to initiating a study, allowing researchers to enhance precision from the outset, as well as revisited during data analysis and communication phases. We may be nuts (or just naïve), but it gpr120 agonist is our sincere hope that providing the PECANS mnemonic as a concrete, guiding rubric will facilitate more precise and “risky” research that can refine our models and enable transformative translational work to improve and protect adolescents as they develop.
Acknowledgements
Heuristic models, agendas and analogies
In this issue of Developmental cognitive neuroscience Shulman and colleagues (n.d.) and Nelson and colleagues (n.d.) present two heuristic models of cognitive development. Shulman and colleagues review the current evidence in favor of dual systems (DS) models, which suggests that enhanced risk taking in adolescents is the consequence of an imbalance between an early maturing motivational system involved in reward processing and a later maturing cognitive control system. They conclude with the viewpoint that the current literature seems to reaffirm the usefulness of these models. In a similar fashion, Nelson and colleagues (n.d.) presented an updated version of the social information processing model (SIP), a heuristic framework which links facets of social development (ranging form infant caregiver interactions to intimate relationships during adolescence) with functional changes in the developing brain. Models are one of the central instruments of modern science (e.g. the double helix model of DNA, the billiard ball model of a gas, or the mind as a computer). However, not all models are alike and different types of models serve different functions in the process of scientific discovery (Frigg and Hartmann, 2006). The current models of adolescent brain development, including the ones presented in this issue, are often labeled as heuristic models (Casey, 2014; Crone and Dahl, 2012; Nelson et al., n.d.; Richards et al., 2013; Shulman et al., n.d.). However, it is not always clear what heuristic models are and what role they have. It is currently unclear if, and how, different heuristic models can be meaningfully compared, or to what extend they aid the formulation of testable hypotheses. Moreover, although there are many results that can be interpreted as being consistent with heuristic models, we will argue that such comparisons have only limited value and may even hamper further investigation of the underlying developmental mechanisms. In this comment we provide a critical review of heuristic models, making specific references to the DS and SIP models, focusing on their ability to move the field of developmental cognitive neuroscience forward toward a mechanistic understanding of neural development. We aim to make a contribution to the debate around models by (1) providing further conceptual clarification and a framework to evaluate different types of models, and (2) by highlighting the benefits of a stronger commitment to cognitive models in order to generate testable hypotheses and integrate different levels of analyses (including neuroscience). First, we discuss the role of heuristic models in science as frameworks for inspiration and research agenda setting. Although heuristic models are by nature simplistic, we will suggest several principles that can be used to evaluate them. Next we discuss one direction that could be taken to foster the transition from heuristic models to cognitive neuroscience models, from agenda setting to hypothesis testing.