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Multiple studies show that people prefer attractive over unattractive faces. But what is an attractive face and why is it preferred? Averageness theory claims that faces are perceived as attractive when their facial configuration approximates the mathematical average facial configuration of the population. Conversely, faces that deviate from this average configuration are perceived as unattractive. The theory predicts that both attractive and mathematically averaged faces should be processed more fluently than unattractive faces, whereas the averaged faces should be processed marginally more fluently than the attractive faces. We compared neurocognitive and behavioral responses to attractive, unattractive, and averaged human faces to test these predictions. We recorded event-related potentials (ERPs) and reaction times (RTs) from 48 adults while they discriminated between human and chimpanzee faces. Participants categorized averaged and high attractive faces as “human” faster than low attractive faces. The posterior N170 (150 – 225 ms) face-evoked ERP component was smaller in response to high attractive and averaged faces versus low attractive faces. Single-trial EEG analysis indicated that this reduced ERP response arose from the engagement of fewer neural resources and not from a change in the temporal consistency of how those resources were engaged. These findings provide novel evidence that faces are perceived as attractive when they approximate a facial configuration close to the population average and suggest that processing fluency underlies preferences for attractive faces.

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.

Background Sleep disturbance is a common feature of depression. However, recent work has found that individuals who are vulnerable to depression report poorer sleep quality compared to their low-risk counterparts, suggesting that sleep disturbance may precede depression. In addition, both sleep disturbance and depression are related to deficits in cognitive control processes. Thus we examined if poor sleep quality predicts subsequent increases in depressive symptoms and if levels of cognitive control mediated this relation. Methods Thirty-five undergraduate students participated in 2 experimental sessions separated by 3 weeks. Participants wore an actigraph watch between sessions, which provided an objective measure of sleep patterns. We assessed self-reported sleep quality and depressive symptoms at both sessions. Last, individuals completed an exogenous cuing task, which measured ability to disengage attention from neutral and negative stimuli during the second session. Results Using path analyses, we found that both greater self-reported sleep difficulty and more objective sleep stability measures significantly predicted greater difficulty disengaging attention (i.e., less cognitive control) from negative stimuli. Less cognitive control over negative stimuli in turn predicted increased depression symptoms at the second session. Exploratory associations among the circadian locomotor output cycles kaput gene, CLOCK, single nucleotide polymorphism (SNP), rs11932595, as well as sleep assessments and depressive symptoms also are presented. Conclusions These preliminary results suggest that sleep disruptions may contribute to increases in depressive symptoms via their impact on cognitive control. Further, variation in the CLOCK gene may be associated with sleep quality.