Understanding the role of conscientiousness in healthy aging: where does the brain come in?

I appreciated having the opportunity to review this impressive series of papers, which effectively showcase advances in our understanding of relations between the personality construct of conscientiousness and health in various forms, and provide valuable perspective on directions along which further advances are likely to be made. The papers cover a broad range of ground, from basic personality concepts to lifespan-developmental and behavioral/molecular genetic work to conceptually-informed intervention strategies, and are uniformly thorough and scholarly. I learned a great deal from reading these articles and strongly recommend them to others interested in individual difference variables important to health and positive aging. Notably, one topic emphasized to a limited degree in these papers consists of conceptual and empirical work on the role of brain systems in processes such as cognitive control, response inhibition, and goal-setting of relevance to conscientious tendencies. Some articles contain reference to biological and brain systems (e.g., Friedman et al.’s life-span model includes “biological base” and “physiology” components; Eisenberg et al.’s developmental review cites work on neural systems presumed to be involved in effortful control). However, references of this kind are quite brief, resulting in limited coverage of neuroscientific concepts, methods, and findings. By contrast, one entire article (South & Krueger) is devoted to genetic approaches to investigation of links between conscientiousness and healthy aging, and genetic findings and approaches are also discussed to a notable degree in the articles by Eisenberg et al., Friedman et al., and Roberts et al. The lack of reference to brain-oriented research in these papers was striking to me because my core interests lie in psychophysiological and neuroscientific approaches to the study of clinical problems, and also because of the major initiative that has been launched by the National Institute of Mental Health (NIMH) to establish new conceptions of psychological disorders based around biological (in particular, neurobiological) methods and findings. Titled the “Research Domain Criteria” (RDoC) framework, the initiative’s goal is to “devise a system for identifying and integrating constructs for disordered cognitive, neural, genetic, or epigenetic processes that hold particular promise to [explain] psychiatric symptoms” (Sanislow et al., 2010). A particular focus of the initiative is on characterizing neural circuits relevant to psychological dysfunction and the role they play in observed symptoms. However, the RDoC framework as a whole is broader, spanning multiple levels of analysis from the genetic and cellular levels to the level of measurable behavior in laboratory and naturalistic settings (see RDoC matrix, accessible online at: http://www.nimh.nih.gov/research-priorities/rdoc/nimh-research-domain-criteria-rdoc.shtml#toc_matrix). It seems likely that differing factors account for the limited coverage of neuroscientific research in the current series of articles. However, I suspect the main one is that neuroscientific research, including human work directed at processes of potential relevance to trait dispositions like conscientiousness, does not interface readily with the types of designs and databases that help make the current series of articles so impressive. Specifically, much of the work reviewed in this series (including the article by South and Krueger on genetic methods/findings) consists of large-N, etiologically-informative studies using sophisticated analytic techniques to delineate replicable predictive relations between individual-difference variables and real-world behavioral outcomes of importance. Particularly inspiring in this respect were the conceptual article by Friedman et al., which describes an approach for combining existing databases to address larger-scale questions that transcend the limits of individual study datasets, and the accompanying empirical paper by Kern et al. that provides an illustration of the power of this approach. By contrast, human neuroscientific research on clinical problems and related dispositions has traditionally employed experimental designs focusing on small samples (e.g., 100 or less) with limited availability of collateral measures for characterizing participants, owing to logistic factors including time demands of physiological testing. Further, work of this type has tended to focus on average effects for groups of individuals pre-selected to differ on clinical or dispositional variables, such that a continuous range of variability on individual difference factors of interest is often not represented. Reliance on small samples and select groups has been the norm for neuroimaging studies in particular, due to the high costs per participant and constraints on equipment usage (i.e., at many sites, a single scanner serves multiple investigative groups). Brain measures of other types (e.g., electrocortical) have been included more often in larger-N studies focusing on clinical problems or dispositional variables (for exceptions, see, e.g.: Anokhin, Golosheykin, & Heath, 2008; Iacono, Carlson, Taylor, Elkins, & McGue, 1999; Posthuma et al., 2001), but studies of this type employing Ns of 1000 or more with inclusion of multiple individual difference measures are not common. For these reasons, comparatively little is known about the psychometric properties of neurophysiological variables as indicators of traits and trait-related processes, relative to variables from other domains of assessment (e.g., self-report, other-rating; cf. Patrick et al., 2013; see also Cronbach, 1957). Given this state of affairs, few neurophysiological datasets exist that can be effectively integrated into the types of large-scale empirical-analytic frameworks featured in the papers of this special issue. Consequently, conceptions of the role of neural systems/processes in causal paths modeled within frameworks of this sort are necessarily based on leaps of inference, rather than quantitative-empirical specification. And in turn, ideas about how to incorporate neurobiological findings into intervention strategies for altering dispositional tendencies to improve health outcomes are largely speculative. This makes behavioral or self-report indicators of psychological constructs with known links to outcomes more attractive and defensible as referents for designing treatments and evaluating their effectiveness. In other words, there are good reasons to be cautious about characterizing the role that brain systems play, across development, in tendencies such as conscientiousness that are predictive of health outcomes. How can this knowledge gap be addressed? NIMH’s RDoC initiative, which focuses on ground-up construction of alternative conceptions for linking clinical outcomes to biological (including neural) systems, may serve as one route. However, an alternative approach that could be pursued in parallel with others such as RDoC—entailing incorporation of neurophysiological indicators into already-existing databases and future data collection efforts—is suggested by the current series of papers. In what follows, I briefly describe this alternative approach with reference to articles in this series and other recent developments in the field, and present some specific examples of its implementation suggested by the current articles.