Ageist attitudes have been discovered in children as early as 3 years. However, so far very few studies, especially during the last decade, have examined age-related stereotypes in preschool children. Available questionnaires adapted to this population are scarce. Our study was designed to probe old age-related views in 3- to 6-year-old children ( n = 126) using both an open-ended Image-of-Aging question and a new pilot tool, called Young Children’s Views of Older People (YCVOP), based on a visual analog scale (...) illustrated by cartoons. Parental views of older people were also collected. The YCVOP was easy to use and internally consistent. Both that scale and the Image-of-Aging question showed globally favorable views of older people in preschool children, especially regarding warmth and smartness traits. However, assessment of physical capacity and independence tended to be negative. The overall results were in line with the low-competence, high-warmth stereotype of older people that is common in young adults and school-age children and was found in parents in the current study (Stereotype Content Model). Strikingly, children’s views did not correlate with those of their parents’: The children’s responses appeared more personal and emotional, while the parents tended to adopt global stereotypes. The preschoolers’ views of older people were much more positive in those who spontaneously evoked their grandparents when asked to think of an old person. In conclusion, this study, introducing a new visual tool to assess age-related stereotypes, suggests ambivalent views of older adults start in preschool children and are influenced by grandparents relationships. (shrink)

Emotion is closely associated with language, but we know very little about how children express emotion in their own writing. We used a large‐scale, cross‐sectional, and data‐driven approach to investigate emotional expression via writing in children of different ages, and whether it varies for boys and girls. We first used a lexicon‐based bag‐of‐words approach to identify emotional content in a large corpus of stories (N>100,000) written by 7‐ to 13‐year‐old children. Generalized Additive Models were then used to model changes in (...) sentiment across age and gender. Two other machine learning approaches (BERT and TextBlob) validated and extended these analyses, converging on the finding that positive sentiments in children's writing decrease with age. These findings echo reports from previous studies showing a decrease in mood and an increased use of negative emotion words with age. We also found that stories by girls contained more positive sentiments than stories by boys. Our study shows the utility of large‐scale data‐driven approaches to reveal the content and nature of children's writing. Future experimental work should build on these observations to understand the likely complex relationships between written language and emotion, and how these change over development. (shrink)