Factor structure and aetiological architecture of the BRIEF: A twin study

Executive function is a broad construct that encompasses various processes involved in goal‐directed behaviour in non‐routine situations (Banich, 2009). The present study uses a sample of 560 5‐ to 16‐year‐old twin pairs (M = 11.14, SD = 2.53): 219 monozygotic twin pairs (114 female; 105 male) and 341 dizygotic twin pairs (136 female, 107 male; 98 opposite sex) to extend prior literature by providing information about the factor structure and the genetic and environmental architecture of the Behavior Rating Inventory of Executive Function (BRIEF; Gioia et al., 2000, Child Neuropsychol., 6, 235; Gioia et al., 2000, Behavior rating inventory of executive function, Lutz, FL: Psychological Assessment Resources), a multifaceted rating scale of everyday executive functions. Phenotypic results revealed a 9‐scale, 3‐factor model best represents the BRIEF structure within the current sample. Results of the genetically sensitive analyses indicated the presence of rater bias/contrast effects for the Initiate, Working Memory, and Task‐Monitor scales. Additive genetic and non‐shared environmental influences were present for the Initiate, Plan/Organize, Organization of Materials, Shift, and Monitor and Self‐Monitor scales. Influences on Emotional Control were solely environmental. Interestingly, the aetiological architecture observed was similar to that of performance‐based measures of executive function. This observed similarity provided additional evidence for the usefulness of the BRIEF as a measure of ‘everyday’ executive function.     

Estimating Incremental Validity Under Missing Data

A common form of missing data is caused by selection on an observed variable (e.g., Z). If the selection variable was measured and is available, the data are regarded as missing at random (MAR). Selection biases correlation, reliability, and effect size estimates when these estimates are computed on listwise deleted (LD) data sets. On the other hand, maximum likelihood (ML) estimates are generally unbiased and outperform LD in most situations, at least when the data are MAR. The exception is when we estimate the partial correlation. In this situation, LD estimates are unbiased when the cause of missingness is partialled out. In other words, there is no advantage of ML estimates over LD estimates in this situation. We demonstrate that under a MAR condition, even ML estimates may become biased, depending on how partial correlations are computed. Finally, we conclude with recommendations about how future researchers might estimate partial correlations even when the cause of missingness is unknown and, perhaps, unknowable.     

Explaining White Opposition to Black Political Leadership: The Role of Fear of Racial Favoritism

Despite the election of America's first Black president, most non‐Hispanic Whites continue to oppose Black political leadership. The conventional explanation for White opposition is sheer racial prejudice, yet the available empirical evidence for this theory is inconsistent. I test an alternative theory that Whites perceive Black political leaders as a threat to their group's interests. Using a new survey measure and nationally representative panel data covering the 2008, 2010, and 2012 U.S. elections, I find that a majority of Whites perceive Black elected officials as likely to favor Blacks over Whites. Moreover, fear of racial favoritism predicts support for Barack Obama in both cross‐sectional models and fixed‐effects models of within‐person change, controlling for negative racial stereotypes. I replicate these findings using a separate cross‐sectional survey fielded after the 2014 election that controls for racial resentment. Collectively, these results suggest that perceptions of conflicting group interests—and not just prejudice—drive White opposition to Black political leadership.     

The Interdisciplinarity of Collaborations in Cognitive Science

We introduce a new metric for interdisciplinarity, based on co‐author publication history. A published article that has co‐authors with quite different publication histories can be deemed relatively “interdisciplinary,” in that the article reflects a convergence of previous research in distinct sets of publication outlets. In recent work, we have shown that this interdisciplinarity metric can predict citations. Here, we show that the journal Cognitive Science tends to contain collaborations that are relatively high on this interdisciplinarity metric, at about the 80th percentile of all journals across both social and natural sciences. Following on Goldstone and Leydesdorff (2006), we describe how scientometric tools provide a valuable means of assessing the role of cognitive science in broader scientific work, and also as a tool to investigate teamwork and distributed cognition. We describe how data‐driven metrics of this kind may facilitate this exploration without relying upon rapidly changing discipline and topic keywords associated with publications.     

Birth of the cool: a two-centuries decline in emotional expression in Anglophone fiction

The presence of emotional words and content in stories has been shown to enhance a story’s memorability, and its cultural success. Yet, recent cultural trends run in the opposite direction. Using the Google Books corpus, coupled with two metadata-rich corpora of Anglophone fiction books, we show a decrease in emotionality in English-speaking literature starting plausibly in the nineteenth century. We show that this decrease cannot be explained by changes unrelated to emotionality (such as demographic dynamics concerning age or gender balance, changes in vocabulary richness, or changes in the prevalence of literary genres), and that, in our three corpora, the decrease is driven almost entirely by a decline in the proportion of positive emotion-related words, while the frequency of negative emotion-related words shows little if any decline. Consistently with previous studies, we also find a link between ageing and negative emotionality at the individual level.     

An exploratory analysis of the contexts and circumstances of filicide‐suicide in Chicago, 1965–1994

Filicide is the killing of a ward by a parent. Relative to many other types of homicide, filicide is an infrequent event. Filicide followed by the offender's suicide is less frequent still. The contexts and circumstances surrounding filicide‐suicide may nevertheless provide insight into parental psychology. Some research suggests, for example, that filicidal genetic parents are more likely to commit suicide than are filicidal stepparents. Five hypotheses are tested for this study, using a database that includes incident‐level information on over 22,000 homicides committed in Chicago during the years 1965–1994. Findings do not support the hypothesis of differential risk of suicide following filicide by genetic parents and stepparents. Previous work is replicated, indicating that: (1) filicides that include multiple victims are more likely to end in the offender's suicide than are filicides that include a single victim, (2) parents are more likely to commit suicide following a filicide of an older child than a filicide of a younger child, (3) older parents, relative to younger parents, are more likely to commit suicide following filicide, and that (4) fathers, relative to mothers, are more likely to commit suicide following filicide. Discussion suggests future directions for research that can inform our understanding of filicide and of filicide‐suicide. Aggr. Behav. 00:000–000, 2005. © 2005 Wiley‐Liss, Inc.     

Graph coloring, minimum-diameter partitioning, and the analysis of confusion matrices

It is well known that minimum-diameter partitioning of symmetric dissimilarity matrices can be framed within the context of coloring the vertices of a graph. Although confusion data are typically represented in the form of asymmetric similarity matrices, they are also amenable to a graph-coloring perspective. In this paper, we propose the integration of the minimum-diameter partitioning method with a neighborhood-based coloring approach for analyzing digraphs corresponding to confusion data. This procedure is capable of producing minimum-diameter partitions with the added desirable property that vertices with the same color have similar in-neighborhoods (i.e., directed edges entering the vertex) and out-neighborhoods (i.e., directed edges exiting the vertex) for the digraph corresponding to the minimum partition diameter.