Bridge Over Troubled Water: Commenting on Kovacs and Conway's Process Overlap Theory

Scientific theories build bridges connecting available evidence in novel ways. They are “the glue that holds scientific observations together, for they summarize the chains of cause and effect (that help) to understand how the world works” (Hunt, 2011, p. 65). Scientific theories consider measurable variables; are objective; account for data; and, crucially, are never confirmed. We admire theories still playing the scientific game because they have not been refuted. But this must be for good reasons, not for bad ones. Let us say from the outset that we applaud Kristof Kovacs and Andrew Conway’s (this issue) effort for introducing a theoretical proposal presented under the rubric process overlap theory (POT). As properly underscored by Johnson (2013), conceptual tools are essential for making sense of both the huge amounts of already available data and the new findings derived from neuroscience and molecular genetics. POT is aimed at connecting evidence derived from psychometrics, cognitive psychology, and neuroscience in an attempt to explain one of the most replicated findings in science, namely, the positive manifold: When individuals picked at random from the general population complete varied mental tests, those with better scores in a given test tend to have better scores in the remaining tests (and vice versa). The statistical analysis of a correlation matrix comprising these mental tests produces a general factor (g). No matter how this information is analyzed, at the end of the day we will distill g if there is a truly positive manifold in the data. The interest about the nature of g dates back to the researcher who discovered this empirical fact, Charles Spearman (1904, 1927). The comprehensive book by Jensen (1998) reviews the major and minor topics related to g and discusses extensively the positive manifold at the psychometric, cognitive, biological, and genetic levels. In this regard, several points can be highlighted after his review: (a) g results from the common source of individual differences observed after resolving a variety of mental tests; (b) some tests are better measures of g than others, but the superficial characteristics of the former do not help to characterize g, (c) individual differences in cognitive abilities are remarkably greater than ability differences within a given individual; (d) psychometric g cannot be interpreted as a cognitive process or a brain feature; and (e) g might be compared with a computer’s CPU. One question of paramount relevance relates to the unitary nature of g. In this regard, Kranzler and Jensen (1991) analyzed a large battery of intelligence tests and elementary cognitive tasks failing to support the proposal that a unitary process underlies the general factor (g). However, Carroll (1991) reanalyzed their data arriving at the conclusion that “it seems parsimonious to assume that g is unitary and represents a single entity . that influences a great variety of behaviors and performances, including speed and efficiency of information processing” (p. 434). If g is not unitary, statistical analyses should reveal several high-order factors, but this is hardly the case. Bridges must be solid enough to resist earthquakes. As we show here, POT is a suggestive and courageous bridge built over troubled water. Let’s see two examples before moving forward. According to the Spearman Law of Diminishing Returns, g explains more variance at lower levels than at higher levels of cognitive ability. Following Kovacs and Conway (this issue), this factor differentiation shows that “g is far from being a constant . The domain-generality of the positive manifold varies across ability level” (p. 155). However, there is no Spearman Law of Diminishing Returns effect for the relationship between fluid intelligence (Gf) and working memory capacity (WMC; Gignac & Weiss, 2015; Kroczek, Ociepka, & Chuderski, 2016). Furthermore, Abad, Colom, Juan-Espinosa, and Garc ıa (2003) observed that strong differentiation effects are found when crystallized batteries are analyzed (Detterman & Daniels, 1989; Lynn, 1992). However, the analysis of fluid batteries reveals meager or null differentiation effects (Deary et al., 1996; Fogarty & Stankov, 1995). Their own study, analyzing 4,253 individuals, compared Gf and crystallized intelligence (Gc) batteries, finding a very weak effect for the former and a remarkable effect for the latter. They suggested that the higher correlations observed in the bottom half of the intelligence distribution might be a by-product of educational differences separating the low and high IQ bands, not a genuine intelligence effect. The second example refers to “goal neglect. ” This cognitive function is thought to reflect a limit in WMC. Individuals must