Background: Error awareness is essential to maintain an adaptive and goal-directed behavior and is supposed to rely on the activity of the right dorsolateral prefrontal cortex (DLPFC). However, studies employing electrophysiological methods and functional resonance imaging (fMRI) do not allow to establish a causal relationship between error awareness and implicated brain structures.Objective: The study examined the causal relationship between DLPFC activity and error awareness in order to confirm the involvement of the right DLPFC in error awareness and to obtain temporal information about this process, namely when the activity of the right DLPFC is involved in error awareness.Methods: Three experiments with three different samples were conducted employing on-line Transcranial Magnetic Stimulation (TMS). A paired-pulse and a single-pulse on-line TMS paradigm were employed respectively in Experiments 1 and 3, whereas in Experiment 2 a control test was conducted without TMS. In TMS experiments, the right DLPFC was stimulated, considering the left DLPFC and the Vertex as control sites.Results: Experiment 1 showed no effect of paired-pulse TMS over either right or left DLPFC on error awareness. In Experiment 3, independently from the time point during which TMS was delivered, results showed a significant effect of single-pulse TMS over the DLPFC on Stroop Awareness, without evidence for lateralization of the process.Conclusions: Results of the present study partially demonstrate the involvement of the DLPFC in error awareness.

Films, compared with emotional static pictures, represent true-to-life dynamic stimuli that are both ecological and effective in inducing an emotional response given the involvement of multimodal stimulation (i.e., visual and auditory systems). We hypothesized that a direct comparison between the two methods would have shown greater efficacy of movies, compared to standardized slides, in eliciting emotions at both subjective and neurophysiological levels. To this end, we compared these two methods of emotional stimulation in a group of 40 young adults (20 females). Electroencephalographic (EEG) Alpha rhythm (8–12 Hz) was recorded from 64 scalp sites while participants watched (in counterbalanced order across participants) two separate blocks of 45 slides and 45 clips. Each block included three groups of 15 validated stimuli classified as Erotic, Neutral and Fear content. Greater self-perceived arousal was found after the presentation of Fear and Erotic video clips compared with the same slide categories. sLORETA analysis showed a different lateralization pattern: slides induced decreased Alpha power (greater activation) in the left secondary visual area (Brodmann Area, BA, 18) to Erotic and Fear compared with the Neutral stimuli. Instead, video clips elicited reduced Alpha in the homologous right secondary visual area (BA 18) again to both Erotic and Fear contents compared with Neutral ones. Comparison of emotional stimuli showed smaller Alpha power to Erotic than to Fear stimuli in the left precuneus/posterior cingulate cortex (BA 7/31) for the slide condition, and in the left superior parietal lobule (BA 7) for the clip condition. This result matched the parallel analysis of the overlapped Mu rhythm (corresponding to the upper Alpha band) and can be interpreted as Mu/Alpha EEG suppression elicited by greater motor action tendency to Erotic (approach motivation) compared to Fear (withdrawal motivation) stimuli. Correlation analysis found lower Alpha in the left middle temporal gyrus (BA 21) associated with greater pleasantness to Erotic slides (r38 = –0.62, p = 0.009), whereas lower Alpha in the right supramarginal/angular gyrus (BA 40/39) was associated with greater pleasantness to Neutral clips (r38 = –0.69, p = 0.012). Results point to stronger emotion elicitation of movies vs. slides, but also to a specific involvement of the two hemispheres during emotional processing of slides vs. video clips, with a shift from the left to the right associative visual areas.

Machine learning approaches have been fruitfully applied to several neurophysiological signal classification problems. Considering the relevance of emotion in human cognition and behaviour, an important application of machine learning has been found in the field of emotion identification based on neurophysiological activity. Nonetheless, there is high variability in results in the literature depending on the neuronal activity measurement, the signal features and the classifier type. The present work aims to provide new methodological insight into machine learning applied to emotion identification based on electrophysiological brain activity. For this reason, we analysed previously recorded EEG activity measured while emotional stimuli, high and low arousal (auditory and visual) were provided to a group of healthy participants. Our target signal to classify was the pre-stimulus onset brain activity. Classification performance of three different classifiers (linear discriminant analysis, support vector machine and k-nearest neighbour) was compared using both spectral and temporal features. Furthermore, we also contrasted the classifiers’ performance with static and dynamic (time evolving) features. The results show a clear increase in classification accuracy with temporal dynamic features. In particular, the support vector machine classifiers with temporal features showed the best accuracy (63.8 %) in classifying high vs low arousal auditory stimuli.

During central fixation, a moving pattern of nontargets induces repeated temporary blindness to even salient peripheral targets: motion-induced blindness (MIB). Hitherto, behavioral measures of MIB have relied on subjective judgments. Here, we offer an objective alternative that builds on earlier findings regarding the effects of MIB on the detectability of physical target offsets. We propose a small modification of regular MIB displays: Following a variable duration (lead time), one of the targets is physically removed. Subjects are to respond immediately afterward. We hypothesize that illusory target offsets, caused by MIB, are mistaken for physical target offsets and that errors should thus increase with lead time. Indeed, for both nonsalient and salient targets, we found that detection accuracy for physical target offsets dramatically decreased with lead time. We conclude that target offset detection accuracy is a valid objective measure of MIB. With our method, effects of guessing are minimal, and the fitting of psychometric functions is straightforward. In principle, a staircase extensionfor more efficient data collection-is also possible.

Background: In this study, we investigated the neural correlates of the anticipatory activity of randomly presented faces and sounds of both high and low arousal level by recording EEG activity with a high spatial resolution EEG system. Methods: we preregistered the following three hypotheses: - a contingent Negative Variation (CNV) difference in the amplitude voltage between auditory vs faces stimuli; a greater amplitude voltage in the CNV, in high arousal stimuli vs low arousal stimuli, both in auditory and faces stimuli, in the temporal window from 0 to 1000 ms before the stimulus presentation; - in the time window from 0 to 1000 ms a sensory specific activation at the brain source level in the temporal lobe and auditory cortex before the presentation of an auditory stimulus and an activation of occipital area, dedicated to the elaboration of visual stimuli, before the presentation of of faces .Results: By using a preregistered, hypothesis-driven approach, we found no statistically significant differences in the CNV due to an overly conservative correction for multiple comparisons for the control of Type I error. By contrast, using a data-driven approach based on a machine learning algorithm (Support Vector Machine), we found a significantly larger amplitude in the occipital cluster of electrodes before the presentation of faces with respect to sounds, along with a larger amplitude in the right auditory cortex before the presentation of sounds with respect to faces. Furthermore, we found greater CNV activity in the late prestimulus interval for high vs. low-arousal sounds stimuli in the left centro-posterior scalp regions. Conclusions: These findings, although preliminary, seem to support the hypothesis that the neurophysiological anticipatory activity of random events is specifically driven by either the sensory characteristics or the arousal level of future stimuli.