6.2 B. Implications and Future Directions
One never notices what has been done; one can only see what remains to be done. . . . Marie Curi (1894)

Space does not allow a thorough discussion of all potential implications of contemporary CHC theory.  As a result, only three major points will be offered for consideration.
First, the structural research of the past decade demonstrates the dynamic and unfolding nature of the CHC taxonomy.  Additional research is needed to better elucidate the structure of abilities in the broad domains of Gkn, Gk, Gh, and Go. In addition, Carroll’s primary focus on identifying an overall structural hierarchy necessitated a deliberate ignoring of datasets with small number of variables within a single broad domain (Carroll, 1994).   The current author believes that more focused “mining” within each broad stratum II domain is rich with possible new discoveries, and will be forthcoming soon.  The parochial Ga, Gv, Gs factor research reviewed in this chapter illustrates how studies with a molar focus on a small number of variables within a single broad CHC domain can provide valuable insights into the structure and relations of narrow abilities within a broad domain.  With the foundational CHC structure serving as a working map, researchers can return to previously ignored or recently published datasets, armed with both exploratory and confirmatory factor analytic tools, to seek a better understanding of the narrow stratum I abilities. In turn, test developers and users of tests of intelligence need to continue to develop and embrace tools and procedures grounded in the best contemporary psychometric theory (viz., CHC theory; see recommendations by McGrew, 1997, McGrew & Flanagan, 1998; Flanagan et al., 2000).
Second, CHC theory needs to move beyond the mere description and cataloguing of human abilities to provide multi-lens explanatory models that will produce more prescriptive hypothesis (e.g., aptitude-treatment-interactions).  A particularly important area of research is CHC- grounded investigations of the causal relations between basic information processing abilities (e.g., processing speed and working memory--“behind g”) and higher-order cognitive abilities (e.g., Gf, g, language, reading, etc.).  The recent research in this area by a cadre of prominent researchers (Ackerman, Beier & Boyle, 2002; Ardila, 2003; Baddeley, 2003; Bayliss, Jarrold, Gunn, & Baddeley, 2003; Cocchini,Logie, Sala, Macpherson, Baddeley, 2002;  Conway, Cowan, Bunting, Therriault, & Minkoff, 2002; Daneman, & Merikle, 1996; Fry & Hale, 2000; Lohman, 2001; Kyllonen, 1996; Miyake, Friedman, Rettinger, Shah & Hegarty,2001; Oberauer, Süß, Wilhelm & Wittman, 2003; Paas, Renkl & Sweller, 2003; Paas, Tuovinen, Tabbers, Van Gerven, 2003) has produced promising models for understanding the dynamic interplay of cognitive abilities during cognitive and academic performance. 
Additionally, a better understanding of human abilities most likely will require an equal emphasis on investigations of both the content and processes underlying performance on diverse cognitive tasks.  The content “faceted” hierarchical Berlin Intelligence Structure Model (BIS) (Beauducel, Brocke, Liepmann, 2001; Süß, Oberauer, Wittman, Wilhelm, & Schulze, 2002) is a promising lens from which to view CHC theory.  “Older” and lesser used multivariate statistical procedures, such as multidimensional scaling (MDS), need to be pulled from psychometricians’ closets to allow for the simultaneous examination of the content (facets), processes, and processing complexity.  [Note.  For example, in an unpublished MDS analyses of 50 different cognitive and achievement tests from the WJ III battery, the current author identified, in addition to the primary broad CHC abilities (e.g., Gv, Gf, Gc, etc.), three other dimensions (possibly reflecting intermediate stratum abilities?) by which to organize and view the diverse array of CHC  measures: (1) Visual- spatial/figuralßàAuditory Linguistic; (2) Process dominantßàProduct dominant; (3) Automatic ProcessesßàControlled Process.  ]  In addition, the promising “beyond g” (g+specific abilities) research should continue and be extended to additional domains of human performance.  The evidence is convincing that a number of lower-stratum CHC abilities make important contributions to understanding academic and cognitive performance, above and beyond the effect of g
Finally, it is time for the CHC taxonomy to go “back to the future” and revisit the original conceptualization of “aptitude,” as updated most recently by Richard Snow and colleagues (Corono et al., 2002).  Contrary to many current erroneous assumptions, “aptitude” is not the same as with “ability” or “intelligence.”  According to Snow and colleagues, aptitude is more aligned with the concepts of readiness, suitability, susceptibility, and proneness, all which suggest a “predisposition to respond in a way that fits, or does not fit, a particular situation or class of situations. The common thread is potentiality—a latent quality that enables the development or production, given specified conditions, of some more advanced performance” (Corno et al., 2002, p. 3). Aptitudes represent the multivariate repertoire of a learner’s degree of readiness (propensities) to learn and to perform well in general and domain- specific learning settings.  As such, a person’s aptitudes must include, along with cognitive and achievement abilities, affective and conative characteristics.  Intelligence scholars and applied assessment personal are urged to investigate the contemporary theoretical and empirical research that has married cognitive constructs (CHC and cognitive information processing) with affective and conative traits in the form of aptitude trait complexes. Snow et al.’s aptitude model (Corno, Cronbach, Kupermintz, Lohman, Mandinach, Porteus, & Talbert, 2002; Snow, Corno & Jackson 1996; Snow, 2001) and Ackerman and colleagues Intelligence-as-Process, Personality, Interests and Intelligence-as- Knowledge (PPIK) model should be required reading for all involved in understanding and measuring human performance (Ackerman, 1996; Ackerman & Beier, 2003; Ackerman, Bowen, Beier & Kanfer, 2001).  The CHC taxonomy is the obvious cognitive cornerstone of a model of human aptitude. 
Yes.  These are indeed exciting times in the ongoing quest to describe, understand, predict, explain and measure human intelligence and performance.
[Note.  In the area of school learning, McGrew, Johnson, Cosio and Evans (2004) recently presented a research synthesis based comprehensive taxonomy (Essential Student Academic Facilitators--ESAF) for organizing and understanding the conative and affective components of academic aptitude.  The model includes the broad domains of Motivational Orientation (e.g., intrinsic motivation, academic goal orientation, etc.), Interests and Attitudes (e.g., academic interests, attitudes, values), Self-Beliefs (e.g., academic self-efficacy, self- concept, and ability conception; etc.), Social/Interpersonal Abilities (e.g., prosocial and problem behaviors; social goal setting, etc.), and Self-Regulation (e.g., planning, activation, monitoring, contral and regulation, and reaction/reflection strategies).]