María Ángeles Ballesteros Duperón

Prematurity is a critical biological factor that can lead to increased vulnerability for long-term difficulties in cognitive and behavioral development. Therefore, investigating the impact of gestational age (GA) at birth on neurodevelopmental outcomes is crucial. In this study, we aimed to examine the influence of gestational age on visual attention skills, which are a promising early marker of atypical neurodevelopment. We administered the Gap-Overlap task to measure the attentional performance of 16-month-olds born between the 34th and 41st weeks of gestation. Our findings suggest that attentional disengagement varied depending on GA, with lower GA being associated with slower orienting of visual attention and reduced endogenous control of attention. The results provide evidence for the importance of GA as a critical factor that impacts visual attention skills that in turn may affect cognitive and behavioral development. The study stresses a call to action for further investigation into the neurodevelopmental outcomes of late preterm infants and the potential interventions that can support their healthy development.

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.

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.

Effortful control (EC) describes individual differences in self-regulation, with a strong attentional basis. Moreover, during early childhood EC has a central role on children’s socio-emotional adjustment and academic achievement. 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 75 infants participated in a longitudinal study running different waves of data collection at 6, 9 and 36 months of age. Attentional tasks were administered at 6 months of age and two additional measures were 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 interaction with household environment and provide a new tool to identify early markers of socio-emotional regulation development.

The control of visual attention is key to learning and has a foundational role in the development of self-regulated behavior. Basic attention control skills emerge early in life and show a protracted development along childhood. Prior research suggests that attentional development is influenced by environmental factors in early and late childhood. Although, much less information is available about the impact of the early environment on emerging endogenous attention skills during infancy. In the current study we aimed to test the impact of parental socioeconomic status (SES) and home environment (chaos) in the emerging development of attentional control in a sample of typically-developing infants. A group of 142 (73 female) 6-mo-old babies were longitudinally tested at 6, 9 (n = 122; 60 female) and 16-18 (n = 91; 50 female) months of age using the gap-overlap paradigm. Median saccade latency (mdSL) and disengagement failure (DF) were computed as dependent variables for both overlap and gap conditions. Also, composite scores for Disengagement Cost and Failure were computed considering mdSL and DF of each condition, respectively. Families reported SES and chaos in the first and last follow-up session. Using Linear Mixed Models with Maximum Likelihood estimation (ML) we found a longitudinal decrease in mdSL in the gap but not in the overlap condition, while a general decrease in DF was found with age. Concerning early environmental factors, an SES index, parental occupation and chaos at 6 months were found to show a negative correlation with DF at 16-18 months, although in the former case it was only marginally significant. Hierarchical regression models implementing ML showed that both SES and chaos at 6 months significantly predicted a lower DF at 16-18 months. Results show a longitudinal progression of endogenous attention control, displaying an increased control with age from infancy to toddlerhood, especially in contexts that ease visual disengagement. Also, an equal ability for endogenous control was found in conditions that hinder disengagement between these developmental periods. These attentional mechanisms of control seem to be modulated by early experiences of the individual with the environment.

Brain function rapidly changes in the first two 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 canonical frequency bands (i.e., theta, alpha, beta, gamma). 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, the relative power may capture both, aperiodic and periodic brain activity, misleading actual periodic changes in infancy. For this reason, we explored the early developmental trajectory of the relative power in the canonical frequency bands from infancy to toddlerhood and compared it to changes in periodic activity in a longitudinal study with three waves of data collection at ages 6, 9, and 16 to 18 months. Finally, we analyzed whether the periodic activity and/or aperiodic components of the EEG contributed to explaining 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, and we replicated an increment of alpha peak frequency. We found that age changes in aperiodic parameters (exponent and offset) depend on the frequency range. More importantly, only alpha relative power was directly related to periodic activity but other frequency bands were predicted also by aperiodic components. This suggests that relative power is capturing the developmental changes of the aperiodic brain activity and, therefore, more fine-grained measures are needed.

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.

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.