Moving from Single Traits to Composite Factors
Some of the most striking results in decision neuroscience link specific brain regions to complex cognitive traits. Most such examples come from across-subjects correlations between trait scores – whether derived based on observed behavior or self-reported on a questionnaire – and fMRI activation associated with a relevant task. In recent years, researchers have identified potential neural correlates for behaviors and traits as diverse as reward sensitivity (Beaver et al., 2006), Machiavellian personality (Spitzer et al., 2007), loss aversion (Tom et al., 2007), and altruism (Tankersley et al., 2007; Hare et al., 2010).
Trait-to-brain correlations, in themselves, provide only limited information about the specific processes supported by the associated brain regions. Due to the small sample sizes of fMRI research, relatively few studies adopt the methods of social and personality psychology. Even if a single trait is desired, incorporating related measures can improve specificity of claims; e.g., identifying the effects of altruism, controlling for empathy and theory-of-mind. In other areas of cognitive psychology, for example, measures of processing speed, memory, and other basic abilities can predict individual differences in more complex cognitive functions (Salthouse, 1996). And, factor and cluster analyses can take a set of related measures and derive composite traits – or can segment a sample into groups with shared characteristics, as frequently done in clinical settings. Improved trait measures will also facilitate genomic analyses; single genes will rarely match to traditional trait measures, making identification of robust traits crucial for multi-gene analyses.