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Theory of Mind (ToM) is the ability understand that other people's mental states may be different from one's own. Psychometric models have shown that individual differences in ToM can largely be attributed to general intelligence ("g") (Coyle et al. 2018). Most psychometric models specify "g" as a reflective latent variable, which is interpreted as a general ability that plays a causal role in a broad range of cognitive tasks, including ToM tasks. However, an alternative approach is to specify "g" as a formative latent variable, that is, an overall index of cognitive ability that does not represent a psychological attribute (Kovacs and Conway 2016). Here we consider a formative "g" approach to the relationship between ToM and intelligence. First, we conducted an SEM with reflective g to test the hypothesis that ToM is largely accounted for by a general ability. Next, we conducted a model with formative "g" to determine whether the relationship between ToM and intelligence is influenced by domain-specific tasks. Finally, we conducted a redundancy analysis to examine the contribution of each "g" variable. Results suggest that the relationship between ToM and intelligence in this study was influenced by language-based tasks, rather than solely a general ability.

Process overlap theory (POT) is a new theoretical framework designed to account for the general factor of intelligence ("g"). According to POT, g does not reflect a general cognitive ability. Instead, "g" is the result of multiple domain-general executive attention processes and multiple domain-specific processes that are sampled in an overlapping manner across a battery of intelligence tests. POT explains several benchmark findings on human intelligence. However, the precise nature of the executive attention processes underlying g remains unclear. In the current paper, we discuss challenges associated with building a theory of individual differences in attention and intelligence. We argue that the conflation of psychological theories and statistical models, as well as problematic inferences based on latent variables, impedes research progress and prevents theory building. Two studies designed to illustrate the unique features of POT relative to previous approaches are presented. In Study 1, a simulation is presented to illustrate precisely how POT accounts for the relationship between executive attention processes and "g." In Study 2, three datasets from previous studies are reanalyzed (N = 243, N = 234, N = 945) and reveal a discrepancy between the POT simulated model and the unity/diversity model of executive function. We suggest that this discrepancy is largely due to methodological problems in previous studies but also reflects different goals of research on individual differences in attention. The unity/diversity model is designed to facilitate research on executive function and dysfunction associated with cognitive and neural development and disease. POT is uniquely suited to guide and facilitate research on individual differences in cognitive ability and the investigation of executive attention processes underlying "g."

In a recent publication in the Journal of Intelligence, Dennis McFarland mischaracterized previous research using latent variable and psychometric network modeling to investigate the structure of intelligence. Misconceptions presented by McFarland are identified and discussed. We reiterate and clarify the goal of our previous research on network models, which is to improve compatibility between psychological theories and statistical models of intelligence. WAIS-IV data provided by McFarland were reanalyzed using latent variable and psychometric network modeling. The results are consistent with our previous study and show that a latent variable model and a network model both provide an adequate fit to the WAIS-IV. We therefore argue that model preference should be determined by theory compatibility. Theories of intelligence that posit a general mental ability (general intelligence) are compatible with latent variable models. More recent approaches, such as mutualism and process overlap theory, reject the notion of general mental ability and are therefore more compatible with network models, which depict the structure of intelligence as an interconnected network of cognitive processes sampled by a battery of tests. We emphasize the importance of compatibility between theories and models in scientific research on intelligence.

The positive manifold--the finding that cognitive ability measures demonstrate positive correlations with one another--has led to models of intelligence that include a general cognitive ability or general intelligence (g). This view has been reinforced using factor analysis and reflective, higher-order latent variable models. However, a new theory of intelligence, Process Overlap Theory (POT), posits that g is not a psychological attribute but an index of cognitive abilities that results from an interconnected network of cognitive processes. These competing theories of intelligence are compared using two different statistical modeling techniques: (a) latent variable modeling and (b) psychometric network analysis. Network models display partial correlations between pairs of observed variables that demonstrate direct relationships among observations. Secondary data analysis was conducted using the Hungarian Wechsler Adult Intelligence Scale Fourth Edition (H-WAIS-IV). The underlying structure of the H-WAIS-IV was first assessed using confirmatory factor analysis assuming a reflective, higher-order model and then reanalyzed using psychometric network analysis. The compatibility (or lack thereof) of these theoretical accounts of intelligence with the data are discussed.

The underlying cognitive mechanisms explaining why speakers sometimes make communication errors are not well understood. Some scholars have theorized that audience design engages automatic processes when a listener is present; others argue that it relies on effortful resources, even if a listener is present. We hypothesized that working memory is engaged during communicative audience design and that the extent to which working memory is engaged relies on individual differences in cognitive abilities and concurrent amount of resources available. In Experiment 1, participants completed a referential task under high, low, or no cognitive load with a present listener whose perspective differed from the speaker's. Speakers made few referential errors under no and low load, but errors increased when cognitive load was highest. In Experiment 2, the listener was absent. Speakers made few referential errors under no and low load, but errors increased when cognitive load was highest, suggesting that audience design is still effortful under high cognitive load, both when the listener was present and when the listener was absent. Experiment 3 tested whether cognitive abilities predicted communication performance. Participants with higher fluid intelligence and working memory capacity made fewer communication errors. Our findings suggest that communication relies on available cognitive resources, and therefore errors occur as a function of factors like cognitive load and individual differences.

Fluid intelligence (gF) and working memory (WM) span predict success in demanding cognitive situations. Recent studies show that much of the variance in gF and WM span is shared, suggesting common neural mechanisms. This study provides a direct investigation of the degree to which shared variance in gF and WM span can be explained by neural mechanisms of interference control. The authors measured performance and functional magnetic resonance imaging activity in 102 participants during the n-back WM task, focusing on the selective activation effects associated with high-interference lure trials. Brain activity on these trials was correlated with gF, WM span, and task performance in core brain regions linked to WM and executive control, including bilateral dorsolateral prefrontal cortex (middle frontal gyrus; BA9) and parietal cortex (inferior parietal cortex; BA 40/7). Interference-related performance and interference-related activity accounted for a significant proportion of the shared variance in gF and WM span. Path analyses indicate that interference control activity may affect gF through a common set of processes that also influence WM span. These results suggest that individual differences in interference-control mechanisms are important for understanding the relationship between gF and WM span. (Contains 4 tables and 3 figures.)

The present studies directly test the usefulness of two English-language working memory capacity (WMC) assessments with two samples of students whose native language was not English. Participants completed two widely used complex span tasks, Reading Span (RSpan) and Operation Span (OSpan), in English. To determine whether the well-established relationship between WMC and Raven's Advanced Progressive Matrices (RAPM) would be observed when span tasks were not given in the native language of the participant, span scores were regressed against performance on the RAPM. Results indicated that while OSpan was a reliable and valid predictor of RAPM in non-native-English speakers, RSpan administered in English was not. (Contains 3 tables.)

The present study investigates how working memory and fluid intelligence are related in young children and how these links develop over time. The major aim is to determine which aspect of the working memory system--short-term storage or cognitive control--drives the relationship with fluid intelligence. A sample of 119 children was followed from kindergarten to second grade and completed multiple assessments of working memory, short-term memory, and fluid intelligence. The data showed that working memory, short-term memory, and fluid intelligence were highly related but separate constructs in young children. The results further showed that when the common variance between working memory and short-term memory was controlled, the residual working memory factor manifested significant links with fluid intelligence whereas the residual short-term memory factor did not. These findings suggest that in young children cognitive control mechanisms rather than the storage component of working memory span tasks are the source of their link with fluid intelligence. (Contains 6 tables and 3 figures.)

The n-back task requires participants to decide whether each stimulus in a sequence matches the one that appeared n items ago. Although n-back has become a standard "executive" working memory (WM) measure in cognitive neuroscience, it has been subjected to few behavioral tests of construct validity. A combined experimental-correlational study tested the attention-control demands of verbal 2- and 3-back tasks by presenting n = 1 "lure" foils. Lures elicited more false alarms than control foils in both 2- and 3-back tasks, and lures caused more misses to targets that immediately followed them compared with control targets, but only in 3-back tasks. N-back thus challenges control over familiarity-based responding. Participants also completed a verbal WM span task (operation span task) and a marker test of general fluid intelligence (Gf; Ravens Advanced Progressive Matrices Test; J. C. Raven, J. E. Raven, & J. H. Court, 1998). N-back and WM span correlated weakly, suggesting they do not reflect primarily a single construct; moreover, both accounted for independent variance in Gf. N-back has face validity as a WM task, but it does not demonstrate convergent validity with at least 1 established WM measure.

Studied the interrelationships among general fluid intelligence, short-term memory capacity, working memory capacity, and processing speed in 120 young adults and used structural equation modeling to determine the best predictor of general fluid intelligence. Results suggest that working memory capacity, but not short-term memory capacity or processing speed, is a good predictor of general fluid intelligence. (SLD)