## 4 Discussion
Furthermore, these physiological and psychological fitness indicators overlap in the expected direction, where positive physiological indicators tend to correlate with positive psychological indicators [82; 83], suggesting the possibility of a single factor underlying both. Note that these \"fitness indicators\" are themselves explained to varying degrees by the overlapping theories of classical evolution [84], sexual selection [85], and developmental instability [86; 87], but here we present them only as examples of mental characteristics (such as those captured in \"c\"). In the present study, we do find some evidence supporting the \"fitness indicator\" explanation for \"c\". Specifically, if \"c\" indicates fitness in the way described above, we would expect cognitive tests with the highest factor loadings on \"c\" to be those typically most associated with \"g\" [65], and that is what we find. All three of the tests with loadings > 0.50 on \"c\" (PMAT, PVRT, and WRAT), measure domains (matrix reasoning and verbal ability, including analogies) consistently reported as central to \"g\" [17; 88; 89]. Practical applications of the \"c\" factor depend on results of future research, but we propose some examples here. If indeed \"c\" contains information not captured by either \"g\" or \"p\", as tentatively suggested by results using the validity criteria above, it offers a single measure helpful to predict or explain outcomes. In studies that do not focus on either cognition or psychopathology, \"c\" could function as a useful covariate. For example, in a study relating blood pressure to dietary habits, it would make sense to control for brain (dys)function without having to include multiple covariates. Second, as alluded to by [PERSON] et al. [23], the \"c\" factor model allows one to examine associations of \"p\" and \"g\" with other criteria after controlling for \"c\". In the same way as the \"p\" factor model allows one to control for \"p\" in examining relationships with the specific factors (e.g., depression, externalizing, etc. ), the \"c\" factor allows one to do the same with \"p\" and \"g\". Further, because the \"c\" factor is modeled in an orthogonal tri-factor configuration, all factors in the model can be related simultaneously to an external criterion without concern for collinearity. Third, \"c\" could be useful in tracking the overall brain function of participants in a clinical trial. For example, in a Phase 1 study where side effects are of central concern, the \"c\" factor could be a way to assess participants' day-to-day or week-to-week functioning and brain health as the protocol proceeds. Finally, the relative sizes of the standardized loadings of the \"c\" factor model itself can be helpful in informing (1) which measures to prioritize if one is planning a study that involves measuring overall cerebral function but is limited in available time to do so, or (2) in a data set that is already collected, which measures would be the best for measuring overall cerebral functioning in a composite score. Based on the present study, if interested in \"c\", one would want to prioritize or focus on measures of IQ (as indicated by high loadings on matrix reasoning and analogies) and clinical assessmentsof externalizing disorders and agorapabobia. However, we discourage using this single study as the basis for either #1 or #2 above; further research on the \"c\" factor is necessary. This study has some notable limitations that warrant careful consideration. First, clinical diagnostics were assessed using a mix of self- and parent-reported information, depending on the age range (e.g., all parent-reported for participants 8-10 years old), and there is evidence of only moderate agreement between parents and children in this sample [90]. Parent-reporting was used only for children under 11 because there were fewer externalizing than internalizing disorders assessed here, and there is evidence that parent-reported data are more valuable for externalizing symptoms [91].