Brianna L. Yamasaki

Bilingual brain training: A neurobiological framework of how bilingual experience improves executive function

Individuals who develop bilingually typically outperform monolinguals on tests of executive functions. This advantage likely reflects enhanced prefrontal function, but the mechanisms that underlie this improvement are still poorly understood. This article describes a theory on the nature of the neural underpinnings of improved executive function in bilinguals. Specifically, we propose that growing up in a bilingual environment trains a gating system in the striatum that flexibly routes information to the prefrontal cortex. This article is divided into three sections. Firstly, literature establishing a three-way connection between bilingualism, executive function, and fronto-striatal loops is summarized. Secondly, a computational model of information processing in the basal ganglia is described, illustrating how the striatal nuclei function to transfer information between cortical regions under prerequisite conditions. Finally, this model is extended to describe how bilingualism may “train the brain,” enabling improved performance under conditions of competitive information selection during information transfer. Theoretical implications and predictions of this theory are discussed.

Resting-state qEEG predicts rate of second language learning in adults

Understanding the neurobiological basis of individual differences in second language acquisition (SLA) is important for research on bilingualism, learning, and neural plasticity. The current study used quantitative electroencephalography (qEEG) to predict SLA in college-aged individuals. Baseline, eyes-closed resting-state qEEG was used to predict language learning rate during eight weeks of French exposure using an immersive, virtual scenario software. Individual qEEG indices predicted up to 60% of the variability in SLA, whereas behavioral indices of fluid intelligence, executive functioning, and working-memory capacity were not correlated with learning rate. Specifically, power in beta and low-gamma frequency ranges over right temporoparietal regions were strongly positively correlated with SLA. These results highlight the utility of resting-state EEG for studying the neurobiological basis of SLA in a relatively construct-free, paradigm-independent manner.

Individual differences in the Simon effect are underpinned by differences in the competitive dynamics in the basal ganglia: An experimental verification and a computational model

Cognitive control is thought to be made possible by the activity of the prefrontal cortex, which selectively uses task-specific representations to bias the selection of task-appropriate responses over more automated, but inappropriate, ones. Recent models have suggested, however, that prefrontal representations are in turn controlled by the basal ganglia. In particular, neurophysiological considerations suggest that the basal ganglias indirect pathway plays a pivotal role in preventing irrelevant information from being incorporated into a task, thus reducing response interference due to the processing of inappropriate stimuli dimensions. Here, we test this hypothesis by showing that individual differences in a non-verbal cognitive control task (the Simon task) are correlated with performance on a decision-making task (the Probabilistic Stimulus Selection task) that tracks the contribution of the indirect pathway. Specifically, the higher the effect of the indirect pathway, the smaller was the behavioral costs associated with suppressing interference in incongruent trials. Additionally, it was found that this correlation was driven by individual differences in incongruent trials only (with little effect on congruent ones) and specific to the indirect pathway (with almost no correlation with the effect of the direct pathways). Finally, it is shown that this pattern of results is precisely what is predicted when competitive dynamics of the basal ganglia are added to the selective attention component of a simple model of the Simon task, thus showing that our experimental results can be fully explained by our initial hypothesis.

The Importance of Managing Interference for Second Language Reading Ability: An Individual Differences Investigation

Research on individual differences in second language (L2) reading ability has primarily focused on factors known to contribute to first language (L1) reading ability, with little consideration of factors mediating interference between languages. In an exploratory analysis, we compared the degree to which the linguistic interference that readers experience predicted reading ability in L1 and L2. Based on current psycholinguistic models, we also investigated whether the relation is mediated by working memory capacity. A series of regression analyses were performed to investigate these relations in 83 monolinguals, 50 L1 English-speaking bilinguals, and 127 L2 English-speaking bilinguals. Results revealed that the amount of linguistic interference experienced significantly predicted reading ability in L2 but not in L1 or for monolinguals. Further, the relation was not mediated by individual differences in working memory capacity. These results illustrate the need for consideration of cross-linguistic factors for models of L2 reading ability in particular.

Relating individual differences in bilingual language experiences to executive attention

ABSTRACT While bilinguals differ from monolinguals in brain structure and function, the extent to which these differences impact non-linguistic cognitive abilities remains debated. The current set of experiments was motivated by the view that all language experiences don’t impact executive attention equally. Driven by contemporary hypotheses on the neurocognitive basis of bilingual language control, four characteristics of bilingualism (patterns of language use, similarity, proficiency, and age of acquisition) were related to performance on the Attentional Blink (AB) task. While not replicated in Experiment 2, results from Experiment 1 and a combined group analysis demonstrated that more balanced first- and second-language use and more distantly related languages were predictive of smaller ABs. In follow-up analyses, smaller ABs were associated with better Simon task performance across both experiments. These findings highlight the utility of an individual differences approach.

Detecting random responders with infrequency scales using an error-balancing threshold

Infrequency scales are becoming a popular mode of data screening, due to their availability and ease of implementation. Recent research has indicated that the interpretation and functioning of infrequency items may not be as straightforward as had previously been thought (Curran & Hauser, 2015), yet there are no empirically based guidelines for implementing cutoffs using these items. In the present study, we compared two methods of detecting random responding with infrequency items: a zero-tolerance threshold versus a threshold that balances classification error rates. The results showed that a traditional zero-tolerance approach, on average, screens data that are less indicative of careless responding than those screened by the error-balancing approach. Thus, the de facto standard of applying a “zero-tolerance” approach when screening participants with infrequency scales may be too stringent, so that meaningful responses may also be removed from analyses. Recommendations and future directions are discussed.

Individual Differences in Resting-state Brain Rhythms Uniquely Predict Second Language Learning Rate and Willingness to Communicate in Adults

The current study used quantitative electroencephalography (qEEG) to characterize individual differences in neural rhythms at rest and to relate them to fluid reasoning ability, to first language proficiency, and to subsequent second language (L2) learning ability, with the goal of obtaining a better understanding of the neurocognitive bases of L2 aptitude. Mean spectral power, laterality, and coherence metrics were extracted across theta, alpha, beta, and gamma frequency bands obtained from eyes-closed resting-state qEEG data from 41 adults aged 18–34 years. Participants then completed 8 weeks of French training using a virtual language and cultural immersion software. Results replicate and extend previous studies showing that faster learners have higher beta power recorded over right hemisphere (RH) electrode sites, greater laterality (RH − LH/RH + LH) of alpha and beta bands, and greater coherence between RH frontotemporal sites across all frequencies, although only coherence measures survived multiple comparisons. Increased coherence within and between RH networks was also associated with greater posttest declarative memory scores and with more accurate speech during learning. Total speech attempts, in contrast, correlated with bilaterally distributed small-world network configurations, as indexed by lower power and coherence over high-frequency (beta and gamma) bands recorded over frontotemporal networks in both hemispheres. Results from partial correlations and regression analyses suggest that the neural predictors of L2 learning rate, posttest proficiency, and total speech attempts varied in their degree of overlap with qEEG correlates of first language proficiency and fluid reasoning abilities, but that neural predictors alone explained 26–60% of the variance in L2 outcomes.

Human performance across decision making, selective attention, and working memory tasks: Experimental data and computer simulations

This article describes the data analyzed in the paper “Individual differences in the Simon effect are underpinned by differences in the competitive dynamics in the basal ganglia: An experimental verification and a computational model” (Stocco et al., 2017) [1]. The data includes behavioral results from participants performing three cognitive tasks (Probabilistic Stimulus Selection (Frank et al., 2004) [2], Simon task (Craft and Simon, 1970) [3], and Automated Operation Span (Unsworth et al., 2005) [4]), as well as simulationed traces generated by a computational neurocognitive model that accounts for individual variations in human performance across the tasks. The experimental data encompasses individual data files (in both preprocessed and native output format) as well as group-level summary files. The simulation data includes the entire model code, the results of a full-grid search of the models parameter space, and the code used to partition the model space and parallelize the simulations. Finally, the repository includes the R scripts used to carry out the statistical analyses reported in the original paper.