Bilingualism caught in a net: A new approach to understanding the complexity of bilingual experience

The growing importance of research on bilingualism in psychology and neuroscience motivates the need for a psychometric model that can be used to understand and quantify this phenomenon. This research is the first to meet this need. We reanalyzed two data sets (N = 171 and N = 112) from relatively young adult language-unbalanced bilinguals and asked whether bilingualism is best described by the factor structure or by the network structure. The factor and network models were established on one data set and then validated on the other data set in a fully confirmatory manner. The network model provided the best fit to the data. This implies that bilingualism should be conceptualized as an emergent phenomenon arising from direct and idiosyncratic dependencies among the history of language acquisition, diverse language skills, and language-use practices. These dependencies can be reduced to neither a single universal quotient nor to some more general factors. Additional in-depth network analyses showed that the subjective perception of proficiency along with language entropy and language mixing were the most central indices of bilingualism, thus indicating that these measures can be especially sensitive to variation in the overall bilingual experience. Overall, this work highlights the great potential of psychometric network modeling to gain a more accurate description and understanding of complex (psycho)linguistic and cognitive phenomena

Advancing the understanding of individual differences in attentional control: Theoretical, methodological, and analytical considerations

Attentional control as an ability to regulate information processing during goal-directed behavior is critical to many theories of human cognition and thought to predict a large range of everyday behaviors. However, in recent years, failures to reliably assess individual differences in attentional control have sparked a debate concerning whether attentional control, as currently conceptualized and assessed, can be regarded as a valid psychometric construct. In this consensus paper, we summarize the current debate from theoretical, methodological, and analytical perspectives. First, we propose a consensus-based definition of attentional control and the cognitive mechanisms that potentially contribute to individual differences in attentional control. Next, guided by the findings of an in-depth literature survey, we discuss the psychometric considerations that are critical when assessing attentional control. We then provide suggestions for recent methodological and analytical approaches that can alleviate the most common concerns. We conclude that, to truly advance our understanding of the construct of attentional control, we must develop a theory-driven and empirically supported consensus on how we define, operationalize, and assess attentional control. This consensus paper presents a first step to achieve this goal; a shift toward transparent reporting, sharing of materials and data, and cross-laboratory efforts will further accelerate progress. This repository contains the data and R scripts to process, describe and analyze, and plot the literature survey data

Spatiotemporal complexity patterns of resting‐state bioelectrical activity explain fluid intelligence: Sex matters

Neural complexity is thought to be associated with efficient information processing but the exact nature of this relation remains unclear. Here, the relationship of fluid intelligence (gf) with the resting‐state EEG (rsEEG) complexity over different timescales and different electrodes was investigated. A 6‐min rsEEG blocks of eyes open were analyzed. The results of 119 subjects (57 men, mean age = 22.85 ± 2.84 years) were examined using multivariate multiscale sample entropy (mMSE) that quantifies changes in information richness of rsEEG in multiple data channels at fine and coarse timescales. gf factor was extracted from six intelligence tests. Partial least square regression analysis revealed that mainly predictors of the rsEEG complexity at coarse timescales in the frontoparietal network (FPN) and the temporo‐parietal complexities at fine timescales were relevant to higher gf. Sex differently affected the relationship between fluid intelligence and EEG complexity at rest. In men, gf was mainly positively related to the complexity at coarse timescales in the FPN. Furthermore, at fine and coarse timescales positive relations in the parietal region were revealed. In women, positive relations with gf were mostly observed for the overall and the coarse complexity in the FPN, whereas negative associations with gf were found for the complexity at fine timescales in the parietal and centro‐temporal region. These outcomes indicate that two separate time pathways (corresponding to fine and coarse timescales) used to characterize rsEEG complexity (expressed by mMSE features) are beneficial for effective information processing