Do Executive Attentional Processes Uniquely or Commonly Explain Psychometric g and Correlations in the Positive Manifold? A Structural Equation Modeling and Network-Analysis Approach to Investigate the Process Overlap Theory

Abstract

One of the best-established findings in intelligence research is the pattern of positive correlations among various intelligence tests. Although this so-called positive manifold became the conceptual foundation of many theoretical accounts of intelligence, the very nature of it has remained unclear. Only recently, Process Overlap Theory (POT) proposed that the positive manifold originated from overlapping domain-general, executive processes. To test this assumption, the functional relationship between different aspects of executive attention and the positive manifold was investigated by re-analyzing an existing dataset (N = 228). Psychometric reasoning, speed, and memory performance were assessed by a short form of the Berlin Intelligence Structure test. Two aspects of executive attention (sustained and selective attention) and speed of decision making were measured by a continuous performance test, a flanker task, and a Hick task, respectively. Traditional structural equation modeling, representing the positive manifold by a g factor, as well as network analyses, investigating the differential effects of the two aspects of executive attention and speed of decision making on the specific correlations of the positive manifold, suggested that selective attention, sustained attention, and speed of decision making explained the common but not the unique portions of the positive manifold. Thus, we failed to provide evidence for POT’s assumption that the positive manifold is the result of overlapping domain-general processes. This does not mean that domain-general processes other than those investigated here will not be able to show the pattern of results predicted by POT.

A neurocognitive psychometrics account of individual differences in attentional control.

“…Together, these findings indicate that there is a clear link between target-selection processes and cognitive abilities across all levels of measurement, whereas the link between response-selection processes and cognitive abilities is much more inconsistent. What is most compelling about this finding is that it firmly establishes that individual differences in selective attention are related to individual differences in cognitive abilities; this is a hypothesis that is still heavily disputed in the field (see Frischkorn et al, 2019; Frischkorn & von Bastian, 2021; Rey-Mermet et al, 2018, 2019; Schubert & Rey-Mermet, 2019; Troche et al, 2021; von Bastian et al, 2020). Only by combining cognitive model parameters and sLRP peak latencies into a single multilayer structural equation model were we able to make this important distinction between the role of target and response selection processes and contribute to the ongoing discussion in intelligence research.…”

“…The present study was not designed to formally test either of these theoretical accounts, because we did not compare additive and multiplicative relations between cognitive process parameters nor did we distinguish between the contributions of maintenance and disengagement processes to individual differences in cognitive abilities. However, recent discussions about empirical tests of these theories show that theoretical progress is stalled by a lack of reliable and valid measures of attentional control (Frischkorn & von Bastian, 2021; Schubert & Rey-Mermet, 2019; Troche et al, 2021; Wang et al, 2021). In the present study, we demonstrated that the adoption of the neurocognitive psychometrics approach developed here yields reliable and valid measures of attentional control in the Eriksen flanker task.…”

Neural correlates of intelligence: ERP Components of temporary storage predict fluid intelligence over and above those of executive functions

“…Temporary storage reflects one's capacity to maintain a limited amount of information in a highly accessible state for a short time and has been investigated in the frameworks of both short‐term memory and working memory (Atkinson & Shiffrin, 1968; Baddeley, 1992; Cowan, 2008). Behavioral research indicates that both executive functions and temporary storage contribute to individual differences in fluid intelligence (Benedek et al, 2014; Colom et al, 2008; Cowan et al, 2005; Draheim et al, 2021; Friedman et al, 2006; Rey‐Mermet et al, 2019; Stauffer et al, 2014; Troche et al, 2021; Unsworth & Engle, 2007; Unsworth et al, 2014; Wongupparaj et al, 2015). Some studies even suggest that temporary storage predicts fluid intelligence over and beyond executive functions (Chuderski & Nęcka, 2012; Chuderski et al, 2012; Cowan et al, 2006; Wang et al, 2013).…”

Identifying Links Between Latent Memory and Speech Recognition Factors

“…In the psychology literature, individual performance on specific tasks is typically modeled as arising from an underlying set of latent factors (Engle et al 1999; Miyake et al 2000). While the exact nature and relationship of those latent factors to one another is an area of active research (Kovacs & Conway 2016; Shipstead et al 2016; Friedman & Miyake 2017; Rey-Mermet et al 2018; Troche et al 2021), the norm is to explicitly state and test assumptions about how task performance maps onto latent variables. This approach is advantageous in that it can identify latent factors which contribute to performance across multiple tasks, estimate individual differences in those factors, and identify tasks for which performance depends on multiple latent factors.…”