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Geangu, E.

Publications and source records attributed to Geangu, E..

2 recordsLinked to original sources

Facial Expressions of Emotion are Infrequent in Toddlers' and Caregivers' Egocentric Views: An Ecological Study

Toddlerhood is a critical period in the development of facial expression processing. Prior research suggests that in the natural environment, the frequency of faces in the toddlers egocentric view declines relative to infancy. However, the specific statistics of the emotional facial expressions available to the developing toddler remain unknown. This study implemented a dual-perspective set-up to record the egocentric view of toddlers and their caregivers during everyday situations at home (N = 26 families). Using automated computer vision models, we quantified both the frequency of faces and the emotional expressions displayed. Confirming our hypotheses, faces were sparse in toddler views and significantly less frequent than in caregiver views. Across both perspectives, happiness was the dominant expression, while negative facial expressions were extremely rare. Notably, faces expressing surprise were frequent in toddler view, whereas caregivers encountered significantly more happy and sad facial displays than their children. This is the first ecological study to objectively quantify the occurrence of emotional facial expressions in the home environment. These findings challenge the assumption of an abundance of emotional signals in the early development. Instead, they demonstrate that toddlers develop face representations based on sparse input that is biased towards positive expressions (e.g., happy), suggesting high efficiency in extracting and generalizing information from limited input.

neuroscience↗

ASAP: An automatic sustained attention prediction method for infants and toddlers using wearable device signals

Sustained attention (SA) is a critical cognitive ability that emerges in infancy. The recent development of wearable technology for infants enables the collection of large-scale multimodal data in the natural environment, including physiological signals. To capitalize on these new technologies, psychologists need methods to efficiently extract valid and robust SA measures from large datasets. In this study, we present an innovative automatic sustained attention prediction (ASAP) method that harnesses electrocardiogram (ECG) and accelerometer (Acc) signals recorded with wearable sensors from 75 infants (6-, 9-, 12-, 24- and 36-months). Infants undertook various naturalistic tasks similar to those encountered in their natural environment, including free play with their caregivers. Annotated SA was validated by fixation signals from eye-tracking. ASAP was trained on temporal and spectral features derived from the ECG and Acc signals to detect attention periods, and tested against human-coded SA. ASAPs performance is similar across all age groups, demonstrating its suitability for studying development. We also investigated the relationship between attention periods and low-level perceptual features (visual saliency, visual clutter) extracted from the egocentric videos recorded during caregiver-infant free play. Saliency increased during attention vs inattention periods and decreased with age for attention (but not inattention) periods. Crucially, there was no observable difference in results from ASAP attention detection relative to the human-coded attention. Our results demonstrate that ASAP is a powerful tool for detecting infant SA elicited in natural environments. Alongside the available wearable sensors, ASAP provides unprecedented opportunities for studying infant development in the wild.

neuroscience↗