Differential effects of theta/beta and SMR neurofeedback in ADHD on sleep onset latency

Recent studies suggest a role for sleep and sleep problems in the etiology of attention deficit hyperactivity disorder (ADHD) and a recent model about the working mechanism of sensori-motor rhythm (SMR) neurofeedback, proposed that this intervention normalizes sleep and thus improves ADHD symptoms such as inattention and hyperactivity/impulsivity. In this study we compared adult ADHD patients (N = 19) to a control group (N = 28) and investigated if differences existed in sleep parameters such as Sleep Onset Latency (SOL), Sleep Duration (DUR) and overall reported sleep problems (PSQI) and if there is an association between sleep-parameters and ADHD symptoms. Secondly, in 37 ADHD patients we investigated the effects of SMR and Theta/Beta (TBR) neurofeedback on ADHD symptoms and sleep parameters and if these sleep parameters may mediate treatment outcome to SMR and TBR neurofeedback. In this study we found a clear continuous relationship between self-reported sleep problems (PSQI) and inattention in adults with- and without-ADHD. TBR neurofeedback resulted in a small reduction of SOL, this change in SOL did not correlate with the change in ADHD symptoms and the reduction in SOL only happened in the last half of treatment, suggesting this is an effect of symptom improvement not specifically related to TBR neurofeedback. SMR neurofeedback specifically reduced the SOL and PSQI score, and the change in SOL and change in PSQI correlated strongly with the change in inattention, and the reduction in SOL was achieved in the first half of treatment, suggesting the reduction in SOL mediated treatment response to SMR neurofeedback. Clinically, TBR and SMR neurofeedback had similar effects on symptom reduction in ADHD (inattention and hyperactivity/impulsivity). These results suggest differential effects and different working mechanisms for TBR and SMR neurofeedback in the treatment of ADHD

Drift-diffusion modeling reveals that masked faces are preconceived as unfriendly

During the COVID-19 pandemic, the use of face masks has become a daily routine. Studies have shown that face masks increase the ambiguity of facial expressions which not only affects (the development of) emotion recognition, but also interferes with social interaction and judgement. To disambiguate facial expressions, we rely on perceptual (stimulus-driven) as well as preconceptual (top-down) processes. However, it is unknown which of these two mechanisms accounts for the misinterpretation of masked expressions. To investigate this, we asked participants (N = 136) to decide whether ambiguous (morphed) facial expressions, with or without a mask, were perceived as friendly or unfriendly. To test for the independent effects of perceptual and preconceptual biases we fitted a drift-diffusion model (DDM) to the behavioral data of each participant. Results show that face masks induce a clear loss of information leading to a slight perceptual bias towards friendly choices, but also a clear preconceptual bias towards unfriendly choices for masked faces. These results suggest that, although face masks can increase the perceptual friendliness of faces, people have the prior preconception to interpret masked faces as unfriendly

Стилі керівництва як моделі вербальної поведінки у корпоративному дискурсі

Статья посвящена анализу лингвистических особенностей директивного и демократического стилей руководства как распространенных моделей вербального поведения в корпоративном дискурсе. Внимание уделяется также гендерному фактору, который учитывается при авторитарном коммуникативном поведении.Стаття присвячена аналізові лінгвістичних особливостей директивного та демократичного стилів керівництва як поширених моделей вербальної поведінки у корпоративному дискурсі. Увага приділяється також гендерному фактору, який враховується в авторитарній комунікативній поведінці.The article is dedicated to the analysis of linguistic peculiarities of the directive and democratic management styles as models of the verbal behaviour in the corporate discourse. Attention is also paid to the gender factor, which is considered in the authoritarian communicative behaviour

A matter of availability: sharper tuning for memorized than for perceived stimulus features

Our visual environment is relatively stable over time. An optimized visual system could capitalize on this by devoting less representational resources to objects that are physically present. The vividness of subjective experience, however, suggests that externally available (perceived) information is more strongly represented in neural signals than memorized information. To distinguish between these opposing predictions, we use EEG multivariate pattern analysis to quantify the representational strength of task-relevant features in anticipation of a change-detection task. Perceptual availability was manipulated between experimental blocks by either keeping the stimulus available on the screen during a 2-s delay period (perception) or removing it shortly after its initial presentation (memory). We find that task-relevant (attended) memorized features are more strongly represented than irrelevant (unattended) features. More importantly, we find that task-relevant features evoke significantly weaker representations when they are perceptually available compared with when they are unavailable. These findings demonstrate that, contrary to what subjective experience suggests, vividly perceived stimuli elicit weaker neural representations (in terms of detectable multivariate information) than the same stimuli maintained in visual working memory. We hypothesize that an efficient visual system spends little of its limited resources on the internal representation of information that is externally available anyway

A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task

© Verbruggen et al. Response inhibition is essential for navigating everyday life. Its derailment is considered integral to numerous neurological and psychiatric disorders, and more generally, to a wide range of behavioral and health problems. Response-inhibition efficiency furthermore correlates with treatment outcome in some of these conditions. The stop-signal task is an essential tool to determine how quickly response inhibition is implemented. Despite its apparent simplicity, there are many features (ranging from task design to data analysis) that vary across studies in ways that can easily compromise the validity of the obtained results. Our goal is to facilitate a more accurate use of the stop-signal task. To this end, we provide 12 easy-to-implement consensus recommendations and point out the problems that can arise when they are not followed. Furthermore, we provide user-friendly open-source resources intended to inform statistical-power considerations, facilitate the correct implementation of the task, and assist in proper data analysis

A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task.

Response inhibition is essential for navigating everyday life. Its derailment is considered integral to numerous neurological and psychiatric disorders, and more generally, to a wide range of behavioral and health problems. Response-inhibition efficiency furthermore correlates with treatment outcome in some of these conditions. The stop-signal task is an essential tool to determine how quickly response inhibition is implemented. Despite its apparent simplicity, there are many features (ranging from task design to data analysis) that vary across studies in ways that can easily compromise the validity of the obtained results. Our goal is to facilitate a more accurate use of the stop-signal task. To this end, we provide 12 easy-to-implement consensus recommendations and point out the problems that can arise when they are not followed. Furthermore, we provide user-friendly open-source resources intended to inform statistical-power considerations, facilitate the correct implementation of the task, and assist in proper data analysis