Brain function rapidly changes in the first 2 years of life. In the last decades, resting‐state EEG has been widely used to explore those changes. Previous studies have focused on the relative power of the signal in established frequency bands (i.e., theta, alpha, and beta). However, EEG power is a mixture of a 1/f‐like background power (aperiodic) in combination with narrow peaks that appear over that curve (periodic activity, e.g., alpha peak). Therefore, it is possible that relative power captures both, aperiodic and periodic brain activity, contributing to changes in electrophysiological activity observed in infancy. For this reason, we explored the early developmental trajectory of the relative power in theta, alpha, and beta frequency bands from infancy to toddlerhood and compared it with changes in periodic activity in a longitudinal study with three waves at age 6, 9, and 16 to 18 months. Finally, we tested the contribution of periodic activity and aperiodic components of the EEG to age changes in relative power. We found that relative power and periodic activity trajectories differed in this period in all the frequency bands but alpha. Furthermore, aperiodic EEG activity flattened between 6 and 18 months. More importantly, only alpha relative power was exclusively related to periodic activity, whereas aperiodic components of the signal significantly contributed to the relative power of activity in theta and beta bands. Thus, relative power in these frequencies is influenced by developmental changes of the aperiodic activity, which should be considered for future studies.

Background. We investigated whether individuals with high levels of autistic traits integrate relevant communicative signals, such as facial expression, when decoding eye-gaze direction. Methods. Students with high vs. low scores on the Autism Spectrum Quotient (AQ) performed a task in which they responded to the eye directions of faces, presented on the left or the right side of a screen, portraying different emotional expressions. Results. In both groups, the identification of gaze direction was faster when the eyes were directed towards the center of the scene. However, in the low AQ group, this effect was larger for happy faces than for neutral faces or faces showing other emotional expressions, whereas participants from high AQ group were not affected by emotional expressions. Conclusions. These results suggest that individuals with more autistic traits may not integrate multiple communicative signals based on their emotional value.

From early ages, gaze acts as a cue to infer the interests, behaviours, thoughts and emotions of social partners. Despite sharing attentional properties with other non‐social directional stimuli, such as arrows, gaze produces unique effects. A spatial interference task revealed this dissociation. The direction of arrows was identified faster on congruent than on incongruent direction‐location trials. Conversely, gaze produced a reversed congruency effect (RCE), with faster identifications on incongruent than congruent trials. To determine the emergence of these gaze‐specific attentional mechanisms, 214 Spanish children (4–17 years) divided into 6 age groups, performed the aforementioned task across three experiments. Results showed stimulus‐specific developmental trajectories. Whereas the standard effect of arrows was unaffected by age, gaze shifted from an arrow‐like effect at age 4 to a gaze‐specific RCE at age 12. The orienting mechanisms shared by gaze and arrows are already present in 4‐year olds and, throughout childhood, gaze becomes a special social cue with additional attentional properties. Besides orienting attention to a direction, as arrows would do, gaze might orient attention towards a specific object that would be attentionally selected. Such additional components may not fully develop until adolescence. Understanding gaze‐specific attentional mechanisms may be crucial for children with atypical socio‐cognitive development.

Effortful control (EC) is a dimension of temperament that encompass individual differences in self-regulation and the control of reactivity. Much research suggests that EC has a strong foundation on the development of executive attention, but increasing evidence also shows a significant contribution of the rearing environment to individual differences in EC. The aim of the current study was to predict the development of EC at 36 months of age from early attentional and environmental measures taken in infancy using a machine learning approach. A sample of 78 infants participated in a longitudinal study running three waves of data collection at 6, 9, and 36 months of age. Attentional tasks were administered at 6 months of age, with two additional measures (i.e., one attentional measure and another self-restraint measure) being collected at 9 months of age. Parents reported household environment variables during wave 1, and their child’s EC at 36 months. A machine-learning algorithm was implemented to identify children with low EC scores at 36 months of age. An “attention only” model showed greater predictive sensitivity than the “environmental only” model. However, a model including both attentional and environmental variables was able to classify the groups (Low-EC vs. Average-to-High EC) with 100% accuracy. Sensitivity analyses indicate that socio-economic variables together with attention control processes at 6 months, and self-restraint capacity at 9 months, are the most important predictors of EC. Results suggest a foundational role of executive attention processes in the development of EC in complex interactions with household environments and provide a new tool to identify early markers of socio-emotional regulation development.

We investigated whether individuals with high levels of autistic traits integrate relevant communicative signals, such as facial expression, when decoding eye-gaze direction. Students with high vs. low scores on the Autism Spectrum Quotient (AQ) performed a task in which they responded to the eyes’ direction of faces, presented on the left or the right side of the screen, portraying different emotional expressions. In both groups, the identification of gaze direction was faster when the eyes were directed towards the center of the scene. However, only in the low AQ group, this effect was larger for happy faces than for neutral faces or faces showing other emotional expressions. High AQ participants were not affected by emotional expressions. These results suggested that individuals with more autistic traits may do not integrate multiple communicative signals based on their emotional value.