In this issue of Developmental Cognitive Neuroscience, two articles revisit a pair of seminal models that have permeated developmental neuroscience research focused on adolescence. Shulman and colleagues (this issue) “review, reappraise, and reaffirm” research relevant to dual-systems models of adolescent development, while Nelson and colleagues (this issue) “expand and update” their proposal regarding the social reorientation model of adolescence and its underlying neural circuitry. The present commentary aims to complement these efforts with a constructive critique that leads to concrete steps we believe can, and should, be taken to improve our models and maximize cumulative scientific progress in the field. We propose here that for adolescent developmental neuroscience to be truly meaningful – and by this we mean precise enough to not only make accurate and testable research predictions, but also be translatable into prevention, intervention, and policy programs that will significantly improve developmental outcomes for adolescents – we need to refocus our priorities and enable our scientific models to evolve. Nearly two decades ago, Hans Eysenck (1997) described the range of scientific methodologies appropriate to different stages of psychological research: “Science begins with a hunch, acquired through observation and induction, which is clearly a preparadigmatic position. If the hunch seems to work, psychologists construct small-scale hypotheses, for which they seek verification. If such verification is forthcoming in sufficient quantity, the level of theory is reached, and one may then consider the demands of falsification… The point between hypothesis and theory would seem to mark the advent of a paradigm… when the ordinary business of science takes over, that is, the large-scale testing of deductions from the theory, and the attempt to explain anomalies in terms of the theory’s apparent failure” (pp. 1225–1226). We believe that many of our models in adolescent developmental neuroscience, and the resultant research, are persisting in a verification stage, where we primarily focus on supportive evidence that is consistent with the model in question. Indeed, the task is so complex that this is no small achievement, and it is not surprising that the field registers some satisfaction at having models that explain a wide range of phenomena. However, greater progress will be achieved if we progress to a more falsification oriented approach, where we i) rigorously examine and account for inconsistent evidence, and ii) put our models at strong risk of falsification based on more precise predictions. A precise prediction that is supported by data provides much stronger evidence for a model than does a less precise prediction. In other words, the degree of logical support for a model is greater given the rarity of the observation absent the theory – what Salmon (1984) has called a “damn strange coincidence” and Meehl (1978) has referred to as a “risky prediction.” The importance of this kind of precision goes beyond scientific progress and model building (although that is reason enough). One of the great challenges that our field, along with many others, struggles with is finding strong translational applications of our work – ones that can really have an impact at both the population and individual level (Allen & Dahl, 2015). However, for this admittedly lofty goal to ever be achieved we have to have models that make predictions that are sufficiently precise and robust that we can prescribe public policy and clinical innovations that have real impact. We are well aware that is much easier to sit on the sidelines and encourage others to do better than it is develop models and put them to the test. We have had our own attempts at theorizing and building models, with varying degrees of success, and we know that it is hard and exacting work (e.g., Allen & Badcock, 2003; Davey, Yucel & Allen, 2008; Pfeifer & Peake, 2012). In this respect we would like to make it unambiguous that the work represented in the target articles is a brave and necessary part of the scientific process. The authors and their ideas have our respect and admiration. Also, it is fair to note that we are not proposing an alternative model here, but we do believe that the approach we describe herein is important in addition to, and support of, the process of model building and refinement.
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Mar 19, 2016
John Dunlosky + 1
Open Access