Infant Behavior & Development最新文献

Negative affect (NA) is a central dimension of infant temperament and an early marker of risk for later psychopathology. While maternal mental health has been associated with increased infant NA, few studies have explored how maternal mental health symptoms relate to the specific subdomains of NA throughout infancy. This study examined longitudinal associations between maternal mental health and infant NA, comparing the general domain with its specific subdomains. We analyzed data from 557 mother-infant dyads enrolled in the Germina cohort in São Paulo, Brazil. Maternal symptoms of depression, anxiety, and stress, along with infant NA and its subdomains-sadness, fear, distress to limitations, and falling reactivity-were assessed at 3, 5-9, and 10-16 months postpartum. Longitudinal associations were examined using linear mixed-effects models with successive-differences contrasts, adjusting for sociodemographic covariates. Maternal stress consistently predicted higher NA and its subdomains-sadness, fear, and distress-across infancy, and was linked to reduced falling reactivity. Depression was associated with increased NA, distress, and decreased reactivity throughout infancy. Anxiety exhibited a time-varying association with distress, increasing from 3 to 9 months before declining, but showed no link with overall NA. Subdomain-specific analyses uncovered maternal mental health associations not evident in general NA models. Examining NA subdomains provides a more detailed understanding of their evolving, dynamic relationships with maternal mental health across infancy. These insights highlight the importance of integrating NA subdomains into screening and intervention strategies to more effectively support at-risk children.

Multimodal caregiver-infant interactions have both concurrent and long-term impacts on child attention, cognitive and social skills. These behaviours are typically manually coded by human researchers, making this approach susceptible to observer bias, dependent on inter-rater reliability, and substantial demands on time and resources. In this study, we aimed to develop a multimodal machine-learning model that could be capable of automatically detecting and classifying multimodal behaviours from video recordings of caregivers and their infants (N = 81; infant mean age = 251.3 ± 34.9 days) engaging with objects. We focused on caregiver scaffolding, caregiver intrusiveness, infant object engagement and infant distractibility. Low-level features from audio, video, and pose data were extracted using specific AI models, and input into a Transformer-based architecture capable of learning temporal patterns across modalities. Our findings revealed a significant contrast in model performance depending on how the data was partitioned. Following previous research, when the dataset was split such that data from all dyads contributed to the training, validation, and test sets - the models achieved notably high classification accuracy of over 98 %. However, when tested on entirely unseen dyads, the performance dropped markedly to around 55 %. These results suggest that the models did not learn behaviors of interest but instead relied on video-specific or dyad-specific details - underscoring key generalizability challenges in applying Transformer-based models to complex, multimodal behavioral data. Nonetheless, this work lays a foundation for future research aimed at refining these models and extending their applicability across diverse caregiving contexts.