Growing evidence shows that executive functioning benefits from bilingual experience. However, the nature of the mechanisms underlying this advantage remains to be clarified. Whereas some have put forward single process accounts to explain the superior performance of bilinguals relative to monolinguals in executive control tasks, recent findings have been interpreted by considering the dynamic combination of monitoring and inhibitory processes to overcome interference from distractor information. In the present study we explored this idea by comparing monolinguals and highly proficient bilinguals in the AX-CPT. This task requires individuals to adjust proactive (monitoring) and reactive (inhibition) control to achieve efficient performance. We also examined the extent to which a well-known index of inhibitory capacity, the stop-signal reaction time, predicts accuracy in the AX-CPT. Results showed that bilinguals outperformed monolinguals in the experimental condition where higher requirement of proactive-reactive control adjustment was required. Interestingly, the inhibition index predicted errors in this condition only in the sample of bilinguals. These findings suggest that a better understanding of the cognitive benefits of bilingualism may require consideration of how bilinguals adjust different executive control mechanisms to cope with interference.

Different types of interventions have focused on trying to improve Executive Functions (EF) due to their essential role in human cognition and behavior regulation. Although EF are thought to be diverse, most training studies have targeted cognitive processes related to working memory (WM), and fewer have focused on training other control mechanisms, such as inhibitory control (IC). In the present study, we aimed to investigate the differential impact of training WM and IC as compared with control conditions performing non-executive control activities. Young adults were divided into two training (WM/IC) and two (active/passive) control conditions. Over six sessions, the training groups engaged in three different computer-based adaptive activities (WM or IC), whereas the active control group completed a program with low control-demanding activities that mainly involved processing speed. In addition, motivation and engagement were monitored through the training. The WM-training activities required maintenance, updating and memory search processes, while those from the IC group engaged response inhibition, and interference control. All participants were pre- and post-tested in criterion tasks (n-back and Stroop), near transfer measures of WM (Operation Span) and IC (Stop-Signal). Non-trained far transfer outcome measures included an abstract reasoning test (Raven’s Advanced Progressive Matrices) and a well-validated experimental task (AX-CPT) that provides indices of cognitive flexibility considering proactive/reactive control. Training results revealed that strongly motivated participants reached higher levels of training improvements. Regarding transfer effects, results showed specific patterns of near transfer effects depending on the type of training. Interestingly, it was only the IC training group that showed far transfer to reasoning. Finally, all trained participants showed a shift towards a more proactive mode of cognitive control, highlighting a general effect of training on cognitive flexibility. The present results reveal specific and general modulations of executive control mechanisms after brief training intervention targeting either WM or IC.

Introduction: Patients with schizophrenia show cognitive deficits that are evident both behaviourally and with EEG recordings. Recent studies have suggested that non-linear analyses of EEG might more adequately reflect the complex, irregular, non-stationary behavior of neural processes than more traditional ERP measures. Non-linear analyses have been mainly applied to EEGs from patients at rest, whereas differences in complexity might be more evident during task performance. Objective: We aimed to investigate changes in non-linear brain dynamics of patients with schizophrenia during cognitive processing. Method: 18 patients and 17 matched healthy controls were asked to name pictures. EEG data were collected at rest and while they were performing a naming task. EEGs were analyzed with the classical Lempel-Ziv Complexity (LZC) and with the Multiscale LZC. Electrodes were grouped in seven regions of interest (ROI). Results: As expected, controls had fewer naming errors than patients. Regarding EEG complexity, the interaction between Group, Task and ROI indicated that patients showed higher complexity values in right frontal regions only at rest, where no differences in complexity between patients and controls were found during the naming task. EEG complexity increased from rest to task in controls in left temporal-parietal regions, while no changes from rest to task were observed in patients. Finally, differences in complexity between patients and controls depended on the frequency bands: higher values of complexity in patients at rest were only observed in fast bands, indicating greater heterogeneity in patients in local dynamics of neuronal assemblies. Conclusion: Consistent with previous studies, schizophrenic patients showed higher complexity than controls in frontal regions at rest. Interestingly, we found different modulations of brain complexity during a simple cognitive task between patients and controls. These data can be interpreted as indicating schizophrenia-related failures to adapt brain functioning to the task, which is reflected in poorer behavioral performance. Highlights: - We measured classical and multiscale Lempel-Ziv Complexity (LZC_N and MLZC) of the EEG signal of patients with schizophrenia and controls at rest and while performing a cognitive task. - We found that patients and controls showed a different pattern of brain complexity depending on their cognitive state (at rest or under cognitive challenge). - Our results illustrate the value of the MLZC in the characterization of the pattern of brain complexity in schizophrenia on function of frequency bands. - Nonlinear methodologies of EEG analysis can help to characterize brain dysfunction in schizophrenia.