The National Institute of Mental Health has proposed the Research Domain Criteria (RDoC) framework as a dimensional approach to evaluating and defining components of psychopathology. The current RDoC matrix is comprised of several systems, including the positive valence system, which encompasses reward sensitivity, which can be further separated into temporal phases of reward anticipation and initial response to reward. The current RDoC matrix includes limited measures within reward sensitivity. As such, reward sensitivity is an ideal variable for the psychoneurometric approach, which aims to integrate multiple units of analysis to operationalize latent variables. An exploratory factor analysis (EFA) was used to integrate four electroencephalographic variables (SPN, P300, RewP amplitudes and delta power), a behavioral measure (reaction time), and self-report measures of personality traits and mood symptoms often associated with reward processing like impulsivity, anhedonia, and valuation. Although hypotheses included a two-factor solution of reward anticipation and reward response, results revealed a six-factor solution, explaining 66.031% of the variance. Factors seemed to reflect: the P-factor/Questionnaire Methods Variance, Trait-Level Approach Motivation/Task Disengagement, General Reward Sensitivity, Excitement Seeking, Impulsivity, and State-Dependent Response to Reward. The separation of hypomanic symptoms (motivation, excitement seeking, and impulsivity) from a broader reward sensitivity factor demonstrate that motivation, excitement seeking, and impulsivity may be candidates for separate subconstructs of Reward Responsiveness in the RDoC matrix. Most factors were comprised of either self-report or neurophysiological indices rather than integrating across units of analysis. Results highlight the need for future studies to include fewer variables coming from a variety of methods rather than oversaturating models with questionnaire data, potentially even developing new scales to index latent constructs or calculate composite scores before conducting factor analyses.
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