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

Why are we not flooded by involuntary thoughts about the past and future? Testing the cognitive inhibition dependency hypothesis

© The Author(s) 2018In everyday life, involuntary thoughts about future plans and events occur as often as involuntary thoughts about the past. However, compared to involuntary autobiographical memories (IAMs), such episodic involuntary future thoughts (IFTs) have become a focus of study only recently. The aim of the present investigation was to examine why we are not constantly flooded by IFTs and IAMs given that they are often triggered by incidental cues while performing undemanding activities. One possibility is that activated thoughts are suppressed by the inhibitory control mechanism, and therefore depleting inhibitory control should enhance the frequency of both IFTs and IAMs. We report an experiment with a between-subjects design, in which participants in the depleted inhibition condition performed a 60-min high-conflict Stroop task before completing a laboratory vigilance task measuring the frequency of IFTs and IAMs. Participants in the intact inhibition condition performed a version of the Stroop task that did not deplete inhibitory control. To control for physical and mental fatigue resulting from performing the 60-min Stroop tasks in experimental conditions, participants in the control condition completed only the vigilance task. Contrary to predictions, the number of IFTs and IAMs reported during the vigilance task, using the probe-caught method, did not differ across conditions. However, manipulation checks showed that participants’ inhibitory resources were reduced in the depleted inhibition condition, and participants were more tired in the experimental than in the control conditions. These initial findings suggest that neither inhibitory control nor physical and mental fatigue affect the frequency of IFTs and IAMs.Peer reviewedFinal Published versio

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