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Inhibition of irrelevant information is a crucial process within the working memory (WM) system. In the present study, we explored how inhibition of distractors is learned within a Sternberg-like WM task and how this learning process affects readout performance. We introduced distractors of varying strength as well as novel stimuli to a classic Sternberg task. Our findings show that the length of the training period affected obstructiveness of distractors as well as differences between WM loads. Significant differences between conditions observed early on in accuracy became void after general ceiling performance was reached. Moreover, accuracy and reaction times, which are commonly used as readouts of WM performance, were affected differently by training. While both improved significantly, only accuracy exhibited differential dependency of experimental conditions on training. It is essential to consider these dynamics when introducing a WM task which requires a long training period.

Sensorimotor µ-rhythm phase is correlated with corticospinal excitability. Transcranial magnetic stimulation (TMS) of motor cortex results in larger motor evoked potentials (MEPs) during the negative peak of the EEG oscillation as extracted with a surface Laplacian. However, the anatomical source of the relevant oscillation is not clear and demonstration of the relationship is sensitive to the choice of EEG montage.Here, we compared two EEG montages preferentially sensitive to oscillations originating from the crown of precentral gyrus (dorsal premotor cortex) vs. postcentral gyrus (secondary somatosensory cortex). We hypothesized that the EEG signal from precentral gyrus would correlate more strongly with MEP amplitude, given that the corticospinal neurons are located in the anterior wall of the sulcus and the corticospinal tract has input from premotor cortex.Real-time EEG-triggered TMS of motor cortex was applied in 6 different conditions in randomly interleaved order, 3 phase conditions (positive peak, negative peak, random phase of the ongoing µ-oscillation), and each phase condition for 2 different EEG montages corresponding to oscillations preferentially originating in precentral gyrus (premotor cortex) vs. postcentral gyrus (somatosensory cortex), extracted using FCC3h vs. C3 centered EEG montages.The negative vs. positive peak of sensorimotor µ-rhythm as extracted from the C3 montage (postcentral gyrus, somatosensory cortex) correlated with states of high vs. low corticospinal excitability (p 0.001), replicating previous findings. However, no significant correlation was found for sensorimotor µ-rhythm as extracted from the neighboring FCC3 montage (precentral gyrus, premotor cortex). This implies that EEG-signals from the somatosensory cortex are better predictors of corticospinal excitability than EEG-signals from the motor areas.The extraction of a brain oscillation whose phase corresponds to corticospinal excitability is highly sensitive to the selected EEG montage and the location of the EEG sensors on the scalp. Here, the cortical source of EEG oscillations predicting response amplitude does not correspond to the cortical target of the stimulation, indicating that even in this simple case, a specific neuronal pathway from somatosensory cortex to primary motor cortex is involved.

This article analyzes some verbal features in the West Russian Chronicles (WRC) (15–16th cc.). The 1st part examines contexts with the construction “быти + participle in -ъш- / -въш-”. This construction is extremely uncommon for Old Russian texts, nevertheless the very possibility of its use is important for understanding the history of the development of the temporal system in Russian. The material of WRC allows us to widen the list of contexts with this construction with two more examples. The 2nd part discusses the functions of pluperfect forms. Rare forms of pluperfect with the linking verb in the aorist form are found in WRC which is quite unexpected for a late text. This article discusses possible explanations for this phenomenon. As for the semantics of the pluperfect forms, all of the basic pluperfect meanings are presented in WRC. The comparison of the contexts with these meanings with the material of the other chronicles allows us to draw a number of conclusions about the history of the pluperfect’s development in Old Russian and its dialects.

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Empathy is a diverse and complicated mechanism present in human interactions. It accounts for our ability to co-feel and to model what another person experiences at a given moment. For better understanding, empathy can be explained as an ability to experience an output of something, for which one didn’t receive any input, only observed someone else experiencing it. Major implications of this mechanism are our abilities to imitate and to understand another person. The former is helpful in a process of learning, while the latter is absolutely necessary in communication with others (and interaction in general). Existing studies suggest different kinds and typologies of empathy. Bringing insights in their fields of interest, they don’t provide a general consistent theory which would explain what empathy is. What are these kinds – distinct neurobiological systems or different analytical ways to look at the same mechanism? If they are distinct systems, to which extent are they interconnected?

Objective. Being able to characterize functional connectivity (FC) state dynamics in a real-time setting, such as in brain–computer interface, neurofeedback or closed-loop neurostimulation frameworks, requires the rapid detection of the statistical dependencies that quantify FC in short windows of data. The aim of this study is to characterize, through extensive realistic simulations, the reliability of FC estimation as a function of the data length. In particular, we focused on FC as measured by phase-coupling (PC) of neuronal oscillations, one of the most functionally relevant neural coupling modes. Approach. We generated synthetic data corresponding to different scenarios by varying the data length, the signal-to-noise ratio (SNR), the phase difference value, the spectral analysis approach (Hilbert or Fourier) and the fractional bandwidth. We compared seven PC metrics, i.e. imaginary part of phase locking value (iPLV), PLV of orthogonalized signals, phase lag index (PLI), debiased weighted PLI, imaginary part of coherency, coherence of orthogonalized signals and lagged coherence. Main results. Our findings show that, for a SNR of at least 10 dB, a data window that contains 5–8 cycles of the oscillation of interest (e.g. a 500–800 ms window at 10 Hz) is generally required to achieve reliable PC estimates. In general, Hilbert-based approaches were associated with higher performance than Fourier-based approaches. Furthermore, the results suggest that, when the analysis is performed in a narrow frequency range, a larger window is required. Significance. The achieved results pave the way to the introduction of best-practice guidelines to be followed when a real-time frequency-specific PC assessment is at target.