A longitudinal twin study of intelligence in the second year

Data from 408 pairs of identical and same-sex fraternal twins assessed at home and in the laboratory at 14, 20, and 24 months are used to describe cognitive development in the second year and to identify genetic and environmental influences on phenotypic similarity. The primary dependent variables are the Bayley Mental Development Index and separate constructs (based on items from the Bayley and the Sequenced Inventory of Communication Development) to measure nonverbal ability, expressive language, and receptive language. These variables are supplemented with laboratory tests of word comprehension, visual attentiveness, and memory for locations. Various patterns of development emerge for separate constructs, for females versus males on each construct, and for individuals across constructs. These data suggest developmental transitions for many infants during the second year, but the timing of these transitions varies by measure. The dependent variables tend to be intercorrelated and are reasonably stable for individuals, with greater stability late in the second year, suggesting either increasing stability or more effective measurement. Expressive and receptive language scores are correlated and have comparable patterns of change within individuals, but there are also differences (e.g., receptive language accounts for the most variance in MDI at each age and across ages). There are genetic effects on MDI at each age and effects of shared environment at 20 and 24 months. Analyses of separate constructs reveal distinct patterns. Effects on nonverbal abilities are entirely genetic. Effects on language are primarily environmental, but genetic influence emerges for expressive language at 20 and 24 months and for receptive language at 14 months. Visual attentiveness tended to reveal effects comparable to the nonverbal construct, and word comprehension was related to the receptive composite. Scores on the memory for locations task were relatively uninformative. A Cholesky decomposition is used to identify influences that account for the same variance at each age (i.e., promote continuity) and that account for new variance at each assessment (i.e., promote change) and to explore overlap and distinctiveness among measures at each age.