In the fall of 1994, the publication of Herrnstein and Murray's book The Bell Curve sparked a new round of debate about the meaning of intelligence test scores and the nature of intelligence. The debate was characterized by strong assertions as well as by strong feelings. Unfortunately, those assertions often revealed serious misunderstandings of what has (and has not) been demonstrated by scientific research in this field. Although a great deal is now known, the issues remain complex and in many cases still unresolved. Another unfortunate aspect of the debate was that many participants made little effort to distinguish scientific issues from political ones. Research findings were often assessed not so much on their merits or their scientific standing as on their supposed political implications. In such a climate, individuals who wish to make their own judgments find it hard to know what to believe. tatives. Other members were chosen by an extended consultative process, with the aim of representing a broad range of expertise and opinion.The Task Force met twice, in January and March of 1995. Between and after these meetings, drafts of the various sections were circulated, revised, and revised yet again. Disputes were resolved by discussion. As a result, the report presented here has the unanimous support of the entire Task Force.

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Attention is called to a common misinterpretation of a bivariate Cholesky analysis as if it were a common and specific factor analysis. It is suggested that an initial Cholesky behavior genetic analysis should often be transformed into a different form for interpretation. Formulas are provided for four transformations in the bivariate case.KEY WORDS: Cholesky; triangular decomposition; simplex; multivariate behavior-genetic analysis.Cholesky factoring, or triangular decomposition, is becoming a popular approach to multivariate behavior genetic problems (see, e.g., Neale and Cardon, 1992). In such a procedure, illustrated in Fig. 1 for three variables, the first latent variable, F~, has effects on all the variables V~ to V3; the second, F2, is uncorrelated with the first and has effects on the remaining variables V2 and V3; and the last, F3, is specific to V 3. One use of the Cholesky procedure is in temporal contexts. For example, Vt to V 3 might represent measurements of some variable at three successive times. In this case, F~ would represent causes present at time 1 which affect the observed variable at time 1 and on subsequent occasions; F2 would represent additional causes which arise by time 2 and whose effects are added to those of F1 from time 2 on; and, finally, F3 represents new causes at time 3 which affect only the last measurement, V 3.However, a Cholesky decomposition can also represent a multivariate analysis of simultaneously measured variables considered in some rationally defined order of priority. In this case, F 1 is assigned the first priority, to explain V t and as much of V 2 and /I3 as it can. Then Fz, given second priority, explains what is left of V 2 and as much as it can of V 3. Finally, F 3 takes care of what is left of V 3.

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There is abundant evidence, some of it reviewed in this paper, that personality traits are substantially influenced by the genes. Much remains to be understood about how and why this is the case. We argue that placing the behavior genetics of personality in the context of epidemiology, evolutionary psychology, and neighboring psychological domains such as interests and attitudes should help lead to new insights. We suggest that important methodological advances, such as measuring traits from multiple viewpoints, using large samples, and analyzing data by modern multivariate techniques, have already led to major changes in our view of such perennial puzzles as the role of "unshared environment" in personality. In the long run, but not yet, approaches via molecular genetics and brain physiology may also make decisive contributions to understanding the heritability of personality traits. We conclude that the behavior genetics of personality is alive and flourishing but that there remains ample scope for new growth and that much social science research is seriously compromised if it does not incorporate genetic variation in its explanatory models.

In a replication of Turkheimer, Haley, Waldron, D'Onofrio, Gottesman II (2003, Socioeconomic status modifies heritability of IQ in young children. Psychological Science, 14:623-628), we investigate genotype-environment (G × E) interaction in the cognitive aptitude of 839 twin pairs who completed the National Merit Scholastic Qualifying Test in 1962. Shared environmental influences were stronger for adolescents from poorer homes, while genetic influences were stronger for adolescents from more affluent homes. No significant differences were found between parental income and parental education interaction effects. Results suggest that environmental differences between middle-to upper-class families influence the expression of genetic potential for intelligence, as has previously been suggested by Bronfenbrenner and Ceci's (1994, Nature-nurture reconceptualized in developmental perspective: a bioecological model Psychological Review, 101:568-586) bioecological model. KeywordsGene-environment interaction; Intelligence; Socioeconomic status; Cognitive aptitude Turkheimer et al. (2003) published a report of genotype-environment (G × E) interaction in the intelligence of young children. In a sample of 7-year old children from the National Perinatal Collaborative Project, the genetic and shared environmental influences on IQ, as measured by the Weschler Intelligence Scale for Children-Revised (WISC-R), were modified by the socioeconomic status (SES) of the children. For disadvantaged children, shared environmental influences accounted for nearly 60% of the variance in IQ, while genetic factors accounted for negligible variance. In advantaged children, the pattern was nearly the reverse.Although the above results are congruent with some previous research (for reviews see or Turkheimer et al. 2003, researchers have not always found clear evidence for G × E interaction in cognitive ability (e.g., Scarr 1981, Van den Oord and Rowe 1997), leaving open the extent to which the results Turkheimer et al. present can be generalized beyond the NCPP sample. This paper presents results of a replication of Turkheimer et al.'s (2003) investigation; however, there are several key changes in the current study. First, the

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We suggest that correlations between environmental measures and child behavior often have both genetic and environmental components, and we propose a simple model to test this hypothesis. Data from classical adoption studies and new data from the Colorado Adoption Project are used to illustrate the model and to provide quantitative estimates of the genetic and environmental components of environ ment-behavior correlations. The genetic components of these so-called environmental correlations are fully as large as the environmental components. Implications of the model for developmental research are discussed.