Converging evidence suggests that human cognition and behavior emerge from functional brain networks interacting on local and global scales. We investigated two information-theoretic measures of functional brain segregation and integration—interaction complexity CI(X), and integration I(X)—as applied to electroencephalographic (EEG) signals and how these measures are affected by choice of EEG reference. CI(X) is a statistical measure of the system entropy accounted for by interactions among its elements, whereas I(X) indexes the overall deviation from statistical independence of the individual elements of a system. We recorded 72 channels of scalp EEG from human participants who sat in a wakeful resting state (interleaved counterbalanced eyes-open and eyes-closed blocks). CI(X) and I(X) of the EEG signals were computed using four different EEG references: linked-mastoids (LM) reference, average (AVG) reference, a Laplacian (LAP) “reference-free” transformation, and an infinity (INF) reference estimated via the Reference Electrode Standardization Technique (REST). Fourier-based power spectral density (PSD), a standard measure of resting state activity, was computed for comparison and as a check of data integrity and quality. We also performed dipole source modeling in order to assess the accuracy of neural source CI(X) and I(X) estimates obtained from scalp-level EEG signals. CI(X) was largest for the LAP transformation, smallest for the LM reference, and at intermediate values for the AVG and INF references. I(X) was smallest for the LAP transformation, largest for the LM reference, and at intermediate values for the AVG and INF references. Furthermore, across all references, CI(X) and I(X) reliably distinguished between resting-state conditions (larger values for eyes-open vs. eyes-closed). These findings occurred in the context of the overall expected pattern of resting state PSD. Dipole modeling showed that simulated scalp EEG-level CI(X) and I(X) reflected changes in underlying neural source dependencies, but only for higher levels of integration and with highest accuracy for the LAP transformation. Our observations suggest that the Laplacian-transformation should be preferred for the computation of scalp-level CI(X) and I(X) due to its positive impact on EEG signal quality and statistics, reduction of volume-conduction, and the higher accuracy this provides when estimating scalp-level EEG complexity and integration.

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22Previous evidence suggests different cortical areas naturally oscillate at distinct 23 frequencies, reflecting tuning properties of each region. The concurrent use of transcranial 24 magnetic stimulation (TMS) and electroencephalography (EEG) has been used to perturb cortical 25 regions, resulting in an observed post-stimulation response that is maximal at the natural frequency 26 of that region. However, little is known about the spatial extent of TMS-induced activation 27 differences in cortical regions when comparing resting state (passive) versus active task 28 performance. Here, we employed TMS-EEG to directly perturb three cortical areas in the right 29 hemisphere while measuring the resultant changes in maximal evoked frequency in healthy human 30 subjects during a resting state (N=12) and during an active sensorimotor task (N=12). Our results 31 revealed that the brain engages a higher dominant frequency mode when actively engaged in a 32 task, such that the frequency evoked during a task is consistently higher across cortical regions, 33 regardless of the region stimulated. These findings suggest that a distinct characteristic of active 34 performance versus resting state is a higher state of natural cortical frequencies.35 36 Introduction 37The influence of task-evoked activation and behavior on the modification of spontaneously 38 occurring patterns of neural activity remains a fundamental question in neuroscience. For decades, 39 non-invasive brain stimulation techniques, such as transcranial magnetic stimulation (TMS), have 40 been used to modulate neural activity in humans and other mammals. Furthermore, in numerous 41 reports, concurrent TMS and electroencephalography (EEG) has been employed to examine 42 cortical reactivity and connectivity. A variety of research using TMS, and some using concurrent 3 43 TMS-EEG, demonstrates TMS-evoked behavioral and neural effects that are dependent on 44 whether the subject is engaged in a task or not [1][2][3][4][5][6][7][8][9][10][11], as well as differences in neural activation 45 during wakeful versus sleeping states [12]. 46In neural stimulation research, more is known about the influence exogenous factors have 47 on the brain's electrical response to TMS (frequency and intensity of stimulation; positioning and 48 orientation of the stimulation coil), as opposed to endogenous factors (e.g., global brain state).49 However, over the past decade, there has been an emergence of research using concurrent TMS-50 EEG to investigate the influence of endogenous factors on neural response. One such study 51 observed an increase in amplitude and spatial spread during the performance of a short-term 52 memory task [1]. Moreover, the observed task-related excitability increased as a result of 53 stimulation to the cortical area, including spread of TMS-evoked currents to functionally 54 connected areas. Globally, the dominant frequency recorded at the scalp matched that of the 55 stimulated area. Yet, local cortical areas oscillated at a rate closer to its own natura...

Sex differences in self-reported homonegativity is prevalent in past research, yet unexplained. We examined possible factors associated with sex differences in overall homonegativity. Heterosexuals self-reported on scales including variables of differential exposure to homosexuals, disgust sensitivity, and reporting biases. Males consistently expressed more negative attitudes toward homosexuals, especially gay men. Many variables were significantly correlated with overall homonegativity and revealed significant sex differences, however, unconvincingly. Self-report measures may lack validity, not always capturing peoples true attitudes. Utilizing a new paradigm of implicit cognitive systems may be more worthwhile in explaining social psychological representations of the abstract cognitive construct of prejudice/stereotyping.

The pacemaker-counter model (PCM) has been the core architecture of scalar expectancy theory for decades. PCM assumes the same timing mechanism applies to every stimulus input, regardless of modality, and has been used to explain differences between perceived durations. In violation, previous studies demonstrate a robust effect of memory-mixing, occurring when the memory trace of a previous time interval influences perception of upcoming ones. We examined the influence of unexpected modality for a trained duration on temporal reproduction, using auditory/visual stimuli with short/long (500/1000ms) standard durations—testing the PCM pure ‘clock speed’ prediction against the memory-mixing account. Here, we report different outcomes of unexpected modality on time reproduction based on modality assignments with specific intervals. When the short interval was trained with an auditory stimulus, and long interval with a visual stimulus, unexpected presence of the opposite modality led to a memory-mixing pattern, in which reproductions shifted toward the other interval. However, no such effect occurred when the short interval was trained with a visual stimulus and long interval with an auditory stimulus. We propose that durations stored in reference memory retain its paired modality, but that during averaging durations are combined based on both their modality and relative duration.