Caroline Di Bernardi Luft

Heart rate variability and cognitive function: Effects of physical effort

This study investigated alterations in heart rate variability (HRV) and cognitive performance before and after physical effort, for 30 high-level track and field athletes (23 males and 7 females). Interbeat intervals were assessed at the baseline and during each task of a CogState cognitive battery (simple reaction time, choice reaction time, working memory, short-term memory and sustained attention). Time and frequency domain measures of HRV were compared between conditions and between tasks. The results indicated differences in HRV between executive and non-executive tasks. There was a significant increase in sympathetic-modulation-related indices after physical effort. The differences between executive and non-executive tasks were the same in post-test. Correlations were found between HRV and cognitive performance, which differed by speed and accuracy. We conclude that HRV is related to cognitive demand and that the correlation between HRV and cognitive performance seems to be stronger after physical exercise. The results raise questions about the psychophysiological meaning of different HRV signals and this has implications for future research about the relationship between HRV and cognition.

Learning from feedback: The neural mechanisms of feedback processing facilitating better performance

Different levels of feedback, from sensory signals to verbal advice, are needed not only for learning new skills, but also for monitoring performance. A great deal of research has focused on the electrophysiological correlates of feedback processing and how they relate to good learning. In this paper, studies on the EEG correlates of learning from feedback are reviewed. The main objective is to discuss these findings whilst also considering some key theoretical aspects of learning. The learning processes, its operational definition and the feedback characteristics are discussed and used as reference for integrating the findings in the literature. The EEG correlates of feedback processing for learning using various analytical approaches are discussed, including ERPs, oscillations and inter-site synchronization. How these EEG responses to feedback are related to learning is discussed, highlighting the gaps in the literature and suggesting future directions for understanding the neural underpinnings of learning from feedback.

Best of both worlds: promise of combining brain stimulation and brain connectome

Transcranial current brain stimulation (tCS) is becoming increasingly popular as a non-pharmacological non-invasive neuromodulatory method that alters cortical excitability by applying weak electrical currents to the scalp via a pair of electrodes. Most applications of this technique have focused on enhancing motor and learning skills, as well as a therapeutic agent in neurological and psychiatric disorders. In these applications, similarly to lesion studies, tCS was used to provide a causal link between a function or behavior and a specific brain region (e.g., primary motor cortex). Nonetheless, complex cognitive functions are known to rely on functionally connected multitude of brain regions with dynamically changing patterns of information flow rather than on isolated areas, which are most commonly targeted in typical tCS experiments. In this review article, we argue in favor of combining tCS method with other neuroimaging techniques (e.g., fMRI, EEG) and by employing state-of-the-art connectivity data analysis techniques (e.g., graph theory) to obtain a deeper understanding of the underlying spatiotemporal dynamics of functional connectivity patterns and cognitive performance. Finally, we discuss the possibilities of using these combined techniques to investigate the neural correlates of human creativity and to enhance creativity.

High-Learners Present Larger Mid-Frontal Theta Power and Connectivity in Response to Incorrect Performance Feedback

A crucial aspect of cognitive control and learning is the ability to integrate feedback, that is, to evaluate action outcomes and their deviations from the intended goals and to adjust behavior accordingly. However, how high-learners differ from low-learners in relation to feedback processing has not been characterized. Further, little is known about the underlying brain connectivity patterns during feedback processing. This study aimed to fill these gaps by analyzing electrical brain responses from healthy adult human participants while they performed a time estimation task with correct and incorrect feedback. As compared with low-learners, high-learners presented larger mid-frontal theta (4–8 Hz) oscillations and lower sensorimotor beta (17–24 Hz) oscillations in response to incorrect feedback. Further, high-learners showed larger theta connectivity from left central, associated with motor activity, to mid-frontal, associated with performance monitoring, immediately after feedback (0–0.3 s), followed by (from 0.3 to 0.6 s after feedback) a flux from mid-frontal to prefrontal, associated with executive functioning. We suggest that these results reflect two cognitive processes related to successful feedback processing: first, the obtained feedback is compared with the expected one, and second, the feedback history is updated based on this information. Our results also indicate that high- and low-learners differ not only on how they react to incorrect feedback, but also in relation to how their distant brain areas interact while processing both correct and incorrect feedback. This study demonstrates the neural underpinnings of individual differences in goal-directed adaptive behavior.

Processing graded feedback: Electrophysiological correlates of learning from small and large errors

Feedback processing is important for learning and therefore may affect the consolidation of skills. Considerable research demonstrates electrophysiological differences between correct and incorrect feedback, but how we learn from small versus large errors is usually overlooked. This study investigated electrophysiological differences when processing small or large error feedback during a time estimation task. Data from high-learners and low-learners were analyzed separately. In both high- and low-learners, large error feedback was associated with higher feedback-related negativity (FRN) and small error feedback was associated with a larger P300 and increased amplitude over the motor related areas of the left hemisphere. In addition, small error feedback induced larger desynchronization in the alpha and beta bands with distinctly different topographies between the two learning groups: The high-learners showed a more localized decrease in beta power over the left frontocentral areas, and the low-learners showed a widespread reduction in the alpha power following small error feedback. Furthermore, only the high-learners showed an increase in phase synchronization between the midfrontal and left central areas. Importantly, this synchronization was correlated to how well the participants consolidated the estimation of the time interval. Thus, although large errors were associated with higher FRN, small errors were associated with larger oscillatory responses, which was more evident in the high-learners. Altogether, our results suggest an important role of the motor areas in the processing of error feedback for skill consolidation.

Aroused with heart: Modulation of heartbeat evoked potential by arousal induction and its oscillatory correlates

Recent studies showed that the visceral information is constantly processed by the brain, thereby potentially influencing cognition. One index of such process is the heartbeat evoked potential (HEP), an ERP component related to the cortical processing of the heartbeat. The HEP is sensitive to a number of factors such as motivation, attention, pain, which are associated with higher levels of arousal. However, the role of arousal and its associated brain oscillations on the HEP has not been characterized, yet it could underlie the previous findings. Here we analysed the effects of high- (HA) and low-arousal (LA) induction on the HEP. Further, we investigated the brain oscillations and their role in the HEP in response to HA and LA inductions. As compared to LA, HA was associated with a higher HEP and lower alpha oscillations. Interestingly, individual differences in the HEP modulation by arousal induction were correlated with alpha oscillations. In particular, participants with higher alpha power during the arousal inductions showed a larger HEP in response to HA compared to LA. In summary, we demonstrated that arousal induction affects the cortical processing of heartbeats; and that the alpha oscillations may modulate this effect.

Using online cognitive tasks to predict mathematics low school achievement

This study aimed to analyze the validity of an online cognitive screening battery to predict mathematic school achievement using artificial neural networks (ANNs). The tasks were designed to measure; selective attention, visuo-spatial working memory, mental rotation, and arithmetic ability in an online, game-like format. In the first study, we investigated the cognitive performance of students with low and typical achievement in mathematics and language. In the second study, we developed an ANN to classify mathematics school achievement. Finally, we tested the adequacy of this network to classify an unknown sample to the ANN. Most of the performance differences in the battery were related to mathematics achievement. The ANN was able to predict mathematics achievement with acceptable accuracy and presented equivalent results in a simulation involving a different sample. We suggest that this assessment model combining ANNs and online cognitive tasks may be a valuable tool to research low school achievement in school settings. Online cognitive tasks can be used to predict mathematics school achievement.An ANN trained based on the performance variables can predict school achievement.This ANN distinguished normal and low-achievement in a different sample.

A importância da organização dos ambientes para a saúde humana

Human interaction with their environments has been investigated by environmental psychology, which studies the mutual influence of environmental and behavioral factors. This article focuses on important relationship between the quality of environments and human health. Its a theoretical study about three themes: development of ownership and attachment in built environments; influence of the phenomena territoriality/privacy in the care of mental health, and psychophysiology aspects of the person-virtual environment interaction. Important indicators related the reduction of the options of ownership of environments to illness. Also, it was identified that health care requires a specific look on the particularities of human-environmental relationships established. Finally, given the increasing exposure to virtual environments, it was realized the need for greater understanding of the psychophysiology of these interactions. Looking for expanding the knowledge of the psychological phenomena in human-environmental interactions, this article provides an overview of theoretical contributions in several recent scientific literature.

Transtorno do estresse pós-traumático em acidentes de trânsito: validação de escala

The study evaluates the validity of the PCL-C scale for screening victims of traffic accidents for post-traumatic stress disorder. One hundred and fourteen victims of traffic accidents participated in this study by completing the scale comprising 17 items, divided into three criteria related to the traumatic event. The Cronbach coefficient was used in order to verify the internal consistency with a value of 0.94. The exploratory factor analysis was carried out to verify the construct validity, including the analysis of the main components and factor loadings with Kaiser normalization and orthogonal rotation by the oblimin method. The results of exploratory factor analysis indicate that de construct is one-dimensional. For the suitability of the cutoff point, sensitivity and specificity analysis was carried out through the Receiver Operating Characteristic (ROC) curve. The cutoff point which obtained the greatest sensitivity (1) was 68 points with specificity of 0.842, this being the most suitable value for the identification of the disorder. The scale for the screening of post-traumatic stress disorder showed reliable psychometric qualities.

Ultra‐high‐field fMRI insights on insight: Neural correlates of the Aha!‐moment

Finding creative solutions to difficult problems is a fundamental aspect of human culture and a skill highly needed. However, the exact neural processes underlying creative problem solving remain unclear. Insightful problem solving tasks were shown to be a valid method for investigating one subcomponent of creativity: the Aha!‐moment. Finding insightful solutions during a remote associates task (RAT) was found to elicit specific cortical activity changes. Considering the strong affective components of Aha!‐moments, as manifested in the subjectively experienced feeling of relief following the sudden emergence of the solution of the problem without any conscious forewarning, we hypothesized the subcortical dopaminergic reward network to be critically engaged during Aha. To investigate those subcortical contributions to insight, we employed ultra‐high‐field 7 T fMRI during a German Version of the RAT. During this task, subjects were exposed to word triplets and instructed to find a solution word being associated with all the three given words. They were supposed to press a button as soon as they felt confident about their solution without further revision, allowing us to capture the exact event of Aha!‐moment. Besides the finding on cortical involvement of the left anterior middle temporal gyrus (aMTG), here we showed for the first time robust subcortical activity changes related to insightful problem solving in the bilateral thalamus, hippocampus, and the dopaminergic midbrain comprising ventral tegmental area (VTA), nucleus accumbens (NAcc), and caudate nucleus. These results shed new light on the affective neural mechanisms underlying insightful problem solving.