Sensitivity to face animacy and inversion in childhood: Evidence from EEG data

Comprehensive assessment of memory function, inhibitory control, neural activity, and cortisol levels in late pregnancy

A considerable proportion of women subjectively perceive a detriment to their cognitive capacity during pregnancy, with decreased memory functions being the most frequently self-reported concerns. However, objective investigation of these perceived cognitive deficits has yielded inconsistent results. This study focused on memory functions during late pregnancy using multiple tasks designed to assess various memory indices, for example, working memory, learning rate, immediate recall, proactive and retroactive interference, delayed recall, retrieval efficiency, visuospatial constructional ability, recognition, and executive function. Additionally, sustained attention and inhibitory control were examined using a combined recognition stop-signal task. Electrophysiological brain activity during this task was recorded using a 128-channel electroencephalographic-event-related potential system. Salivary cortisol levels were assessed both prior to and following the experimental session. In contrast to the widely held belief, results demonstrated that women in late pregnancy did not exhibit a decline in their performance across the various memory tests. In terms of accuracy, there was not a single task in which poorer performance was found for pregnant women. The quality of memory performance was comparable, and in some cases even superior, among women in the pregnancy group. On the stop-signal task, pregnant women exhibited significantly better performance, and their electrophysiological data revealed greater centrally distributed P300 amplitude to "stop" signs, which may signify an enhanced neural efficiency in the domains of inhibitory executive control. Endocrine results revealed that pregnant women exhibited significantly lower levels of salivary cortisol, suggesting an attenuation of hypothalamic-pituitary-adrenocortical axis activity, which may contribute to the optimization of fetal development and growth.

Event-related potential correlates of implicit processing of own- and other-race faces in children

Human adults typically experience difficulties in recognizing and discriminating individual faces belonging to racial groups other than their own. The origin of this "other-race" effect is set in infancy, but the understanding of its developmental course is fragmented. We aimed to access the mechanisms of the other-race effect in childhood by unraveling the neural time course of own- and other-race face processing during a masked priming paradigm. White 6- and 7-year-old children (N = 19) categorized fully visible Asian (other-race) or White (own-race) target faces according to gender. Target faces were preceded by masked same-identity or different-identity prime faces, matching the target for race and gender. We showed an early priming effect on the N100 component, with larger amplitude to different-face pairs than to same-face pairs, and a later race effect on the N200 component, with larger amplitude to own-race face pairs than to other-race face pairs. Critically, race did not interact with priming at any processing stage (P100, N100, P200, N200, or P300). Our results suggest that race could have a temporally limited impact on face processing and that the implicit and unconscious identity processing of own- and other-race faces could be similar in 6- and 7-year-olds, depicting an immature other-race effect during childhood.

Time-resolved multivariate pattern analysis of infant EEG data: A practical tutorial

Time-resolved multivariate pattern analysis (MVPA), a popular technique for analyzing magneto- and electro-encephalography (M/EEG) neuroimaging data, quantifies the extent and time-course by which neural representations support the discrimination of relevant stimuli dimensions. As EEG is widely used for infant neuroimaging, time-resolved MVPA of infant EEG data is a particularly promising tool for infant cognitive neuroscience. MVPA has recently been applied to common infant imaging methods such as EEG and fNIRS. In this tutorial, we provide and describe code to implement time-resolved, within-subject MVPA with infant EEG data. An example implementation of time-resolved MVPA based on linear SVM classification is described, with accompanying code in Matlab and Python. Results from a test dataset indicated that in both infants and adults this method reliably produced above-chance accuracy for classifying stimuli images. Extensions of the classification analysis are presented including both geometric- and accuracy-based representational similarity analysis, implemented in Python. Common choices of implementation are presented and discussed. As the amount of artifact-free EEG data contributed by each participant is lower in studies of infants than in studies of children and adults, we also explore and discuss the impact of varying participant-level inclusion thresholds on resulting MVPA findings in these datasets.