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"Researchers often employ implicit measures as dependent variables to investigate processes of attitude formation and change. In such studies, experimentally induced differences are typically interpreted as reflecting change in automatic evaluations. We argue that experimentally induced effects on implicit measures may not always reflect genuine changes in evaluative responses, but can be driven by the mechanisms underlying the measurement procedure. In line with this assumption, the present research shows that these mechanisms can produce opposite effects of the same experimental manipulation for otherwise equivalent implicit measures. These results indicate that merely observing experimental effects on implicit measures does not allow direct inferences regarding changes in automatic evaluations. Instead, psychological interpretations of such effects hinge upon the mechanics of how a given measurement procedure responds to variations in the context. Implications for research using implicit measures are discussed." [author's abstract]

Two experiments examined the relation between stereotype disconfirmation and attentional processes. Using an instrumental learning-paradigm, we successfully simulated stereotype acquisition and the subsequent subtyping of disconfirming exemplars. While replicating established markers of subtyping, the present research demonstrates a hitherto neglected cognitive consequence of subtyping: Predictable stereotype disconfirmation increased attention to features that facilitated discriminating between confirming and disconfirming exemplars, and reduced attention to features associated with the original stereotype. These effects were not observed when stereotype disconfirmation was not easily predictable and, hence, subtyping proved difficult. The discussion focuses on implications for research on subtyping and stereotype change.

Abstract. In increasingly diverse societies, discrimination against social groups and their members continues to be a public and political concern. Research has addressed three basic cognitive processes that precede discrimination: categorization, stereotype/prejudice activation, and stereotype/prejudice application, suggesting that these processes occur in an automatic fashion. However, there are multiple components of automaticity, including unawareness, efficiency, unintentionality, and uncontrollability. Most of the previous research implies that these components of automaticity converge with respect to cognitive antecedents of discrimination. Here, we review evidence on the distinct components of automaticity in order to assess whether (a) categorization, (b) stereotype/prejudice activation, and (c) stereotype/prejudice application occur (1) without awareness, (2) efficiently, and (3) goal-independently. We highlight evidence indicating convergence or divergence of the automaticity components during each of the processing stages. This analysis provides readers with an up-to-date review that helps to evaluate whether a multi-component approach to automaticity is of additional benefit in aggregating knowledge about the cognitive antecedents of discrimination. We discuss open issues and avenues for future research.

AbstractThe guidelines for setting environmental quality standards are increasingly based on probabilistic risk assessment due to a growing general awareness of the need for probabilistic procedures. One of the commonly used tools in probabilistic risk assessment is the species sensitivity distribution (SSD), which represents the proportion of species affected belonging to a biological assemblage as a function of exposure to a specific toxicant. Our focus is on the inverse use of the SSD curve with the aim of estimating the concentration, HCp, of a toxic compound that is hazardous to p% of the biological community under study. Toward this end, we propose the use of robust statistical methods in order to take into account the presence of outliers or apparent skew in the data, which may occur without any ecological basis. A robust approach exploits the full neighborhood of a parametric model, enabling the analyst to account for the typical real‐world deviations from ideal models. We examine two classic HCp estimation approaches and consider robust versions of these estimators. In addition, we also use data transformations in conjunction with robust estimation methods in case of heteroscedasticity. Different scenarios using real data sets as well as simulated data are presented in order to illustrate and compare the proposed approaches. These scenarios illustrate that the use of robust estimation methods enhances HCp estimation.

The Social Vulnerability Index (SoVI), created by Cutter et al. (2003), examined the spatial patterns of social vulnerability to natural hazards at the county level in the United States in order to describe and understand the social burdens of risk. The purpose of this article is to examine the sensitivity of quantitative features underlying the SoVI approach to changes in its construction, the scale at which it is applied, the set of variables used, and to various geographic contexts. First, the SoVI was calculated for multiple aggregation levels in the State of South Carolina and with a subset of the original variables to determine the impact of scalar and variable changes on index construction. Second, to test the sensitivity of the algorithm to changes in construction, and to determine if that sensitivity was constant in various geographic contexts, census data were collected at a submetropolitan level for three study sites: Charleston, SC; Los Angeles, CA; and New Orleans, LA. Fifty‐four unique variations of the SoVI were calculated for each study area and evaluated using factorial analysis. These results were then compared across study areas to evaluate the impact of changing geographic context. While decreases in the scale of aggregation were found to result in decreases in the variance explained by principal components analysis (PCA), and in increases in the variance of the resulting index values, the subjective interpretations yielded from the SoVI remained fairly stable. The algorithm's sensitivity to certain changes in index construction differed somewhat among the study areas. Understanding the impacts of changes in index construction and scale are crucial in increasing user confidence in metrics designed to represent the extremely complex phenomenon of social vulnerability.