A neurocognitive psychometrics account of individual differences in attentional control.

Abstract

Attention control processes play an important role in many substantial psychological theories, but are hard to reliably and validly measure on the subject-level. Therefore, associations between individual differences in attentional control and other variables are often inconsistent. Here we propose a novel neurocognitive psychometrics account of attentional control that integrates model parameters from the dual-stage two-phase model (Hübner et al., 2010), a mathematical model of selective attention, with neural correlates of conflict processing in a multi-layer structural equation model framework. We analyzed data from 150 participants who completed the Eriksen Flanker task while their EEG was recorded and used the neurocognitive psychometric approach to distinguish between two sequential stages of information-processingtarget selection and response selection. Model parameters and neural correlates showed convergent validity and could be meaningfully related to each other.Together, these neurocognitive process parameters jointly predicted 37 % of the variance in individual differences in higher-order cognitive abilities. Individuals with greater cognitive abilities were not only better at focusing their attention on the target stimulus, but also at subsequent response-selection. All in all, our results support the idea that individual differences in attentional control processes contribute to individual differences in cognitive abilities. Moreover, they provide hope that the measurement crisis of individual differences in attentional control can be overcome by integrating measurement approaches from related disciplines such as mathematical psychology and cognitive neuroscience.

How robust is the relationship between neural processing speed and cognitive abilities?

“…Moreover, before averaging EEG activity across trials, we suggest conducting an odd‐even split of trial data, calculating ERPs separately for odd and even trials, and subsequently estimating ERP latencies separately for odd and even trials. This does not only allow to estimate the reliabilities of ERP latencies based on odd‐even correlations, but also to fit latent variable models where a latent ERP latency factor loads onto latency estimates from odd and even trial data, respectively (see Schubert et al, 2022, for an example). Finally, researchers should think carefully about whether they want to study latencies of a specific component (e.g., the P3) or of several components associated with higher‐order processing (e.g., the P2, N2, and P3), because being able to average or model latent variables across components will further improve their analyses psychometrically.…”

“…At the second measurement session, they completed the CRT and Posner letter matching (PLM) tasks while their EEG was recorded. At each of those measurement sessions, participants also completed a number of other cognitive tasks not reported here (see Schubert et al, 2022). During EEG recordings, participants were seated in a dimly lit, sound-attenuated, and electrically shielded EEG cabin.…”

“…At the second measurement session, they completed the CRT and Posner letter matching (PLM) tasks while their EEG was recorded. At each of those measurement sessions, participants also completed a number of other cognitive tasks not reported here (see Schubert et al, 2022). During EEG recordings, participants were seated in a dimly lit, sound‐attenuated, and electrically shielded EEG cabin.…”

A spurious correlation between difference scores in evidence-accumulation model parameters

“…As an alternative (or complement) to formal model selection, researchers could use a structural equation modelling approach for estimating the parameters of evidence-accumulation models (e.g., Schubert et al, 2022) 6 . In such a framework, the evidence-accumulation model parameters can be conceptualised as latent variables (which are estimated repeatedly on the basis of different subsets of the raw data; for example, separate estimations on the basis of oddnumbered and even-numbered trials).…”

Intelligence and Visual Mismatch Negativity: Is Pre-Attentive Visual Discrimination Related to General Cognitive Ability?

“…The study of event-related brain potentials (ERP) with electroencephalographic recordings during specific tasks allows to more directly study the relation between variability in intelligence and its association with process parameters. Significant associations between intelligence and neural correlates of higher-order cognitive processes have been reported frequently, such as working memory (e.g., Stipacek et al, 2003) or attentional control (e.g., Schubert et al, 2017Schubert et al, , 2022 for review see Hilger et al, 2022). However, the relevance of lower-order processes for intelligence is unclear.…”