RESUMO
For over a hundred years, children's drawings have been used to assess children's intellectual, emotional, and physical development, characterizing children on the basis of intuitively derived checklists to identify the presence or absence of features within children's drawings. The current study investigates whether contemporary data science tools, including deep neural network models of vision and crowd-based similarity ratings, can reveal latent structure in human figure drawings beyond that captured by checklists, and whether such structure can aid in understanding aspects of the child's cognitive, perceptual, and motor competencies. We introduce three new metrics derived from innovations in machine vision and crowd-sourcing of human judgments and show that they capture a wealth of information about the participant beyond that expressed by standard measures, including age, gender, motor abilities, personal/social behaviors, and communicative skills. Machine-and human-derived metrics captured somewhat different aspects of structure across drawings, and each were independently useful for predicting some participant characteristics. For example, machine embeddings seemed sensitive to the magnitude of the drawing on the page and stroke density, while human-derived embeddings appeared sensitive to the overall shape and parts of a drawing. Both metrics, however, independently explained variation on some outcome measures. Machine embeddings explained more variation than human embeddings on all subscales of the Ages and Stages Questionnaire (a parent report of developmental milestones) and on measures of grip and pinch strength, while each metric accounted for unique variance in models predicting the participant's gender. This research thus suggests that children's drawings may provide a richer basis for characterizing aspects of cognitive, behavioral, and motor development than previously thought.
RESUMO
Parent-child conversations are important for children's cognitive development, children's ability to cope with stressful events, and can shape children's beliefs about the causes of illness. In the context of a global pandemic, families have faced a multitude of challenges, including changes to their routines, that they need to convey to their children. Thus, parent-child conversations about the coronavirus pandemic might convey information about the causes of illness, but also about how and why it is necessary for children to modify their behaviors to comply with new social norms and medical guidance. The main goal of this study was to examine the questions children ask about the COVID-19 pandemic and how parents answer them. This survey included responses from a national sample of 349 predominantly white parents of children between the ages of 3 and 12 recruited through Amazon's Mechanical Turk in United States. Parents reported that although children asked about COVID-19 and its causes (17.3%), children asked primarily about lifestyle changes that occurred as a result of the pandemic (24.0%) and safety (18.4%). Parents reported answering these questions by emphasizing that the purpose of different preventative measures was to protect the child (11.8%) or the family (42.7%) and providing reassurance (13.3%). Many parents discussed how it was their social responsibility to slow the spread of the virus (38.4%). Parents of younger children tended to shield them from information about COVID-19 (p = .038), while parents with more knowledge were more likely to provide explanations (p < .001). Our analysis shows that families not only discuss information about the virus but also information about changes to their lifestyle, preventative measures, and social norms.