B. Keith Payne - US grants

The goal of this project is to understand the role of executive control in implicit attitude measurement. Executive control refers to the capacity to behave in strategic ways, pursuing goals while inhibiting responses that interfere with goal-directed behavior. Recent research provides evidence that executive control may act to inhibit the effect of automatic reactions on implicit test performance. An important consequence of this relationship is that differences in implicit test performance between different individuals or groups could be driven by differences in either automatic activation or executive control. Research in the last few years has demonstrated that implicit attitude measures may be to be influenced by many situational manipulations. These findings are important because they suggest that the automatic processes tapped by implicit attitude measures may be strongly determined by situations as opposed to dispositions, and highly flexible, as opposed to rigid. The guiding hypothesis of this project is that some situational contexts influence implicit test performance by affecting automatically activated associations, whereas other contexts influence the degree of executive control engaged. The process dissociation procedure will be used to separate and quantify the contributions of automatic processes and executive control. Three series of experiments will apply this method to determine those situational features which influence automatic activation, and those which influence executive control. The first series examines contexts expected to change mental associations to the attitude object. The second series examines contexts expected to affect the motivation to process strategically. The third series examines more complex situations (e.g. the presence of an African American experimenter) which might influence implicit prejudice measures through both routes.

One of the most important recent insights in the study of attitudes and preferences is that they can be measured implicitly, without asking a person to introspect and self-report. Implicit measurements allow researchers to study attitudes that respondents may be unwilling or unable to report explicitly. They therefore are valuable for studying socially sensitive topics, where respondents may be less than candid. Yet despite this progress, current methods have several psychometric limitations to overcome. Reliability and predictive validity for implicit methods are often lower than accepted for explicit measurement methods. And explicit and implicit measurements of attitudes toward the same topic often differ in multiple ways at the same time, allowing for confounds and alternative explanations. This project develops a novel method for implicitly measuring attitudes, the Affect Misattribution Procedure. It measures attitudes by using the tendency to unintentionally misattribute affective responses from one source to another. Three sets of studies, focusing on racial attitudes and alcohol abuse, compare the method to currently available methods. The first set of studies tests the ability of this method to predict racial discrimination and alcohol abuse behaviors, in comparison to two commonly used implicit methods. The second set tests for resistance to social desirability. The third set uses the method to equate implicit and explicit measures on all dimensions except the crucial difference of intent to express one's attitude. Unlike other implicit methods, the metric for this method is an evaluation rather than response time. Therefore, self-reports and implicit responses can be directly compared on the same scales. By eliminating extraneous differences between implicit and explicit methods, the relationship between implicit and explicit measures can be tested more directly than previously has been possible.

The project has potentially important broader impacts, increasing the fidelity of measurement in important domains such as racial discrimination and substance abuse. The project will help advance research infrastructure for social and behavioral science by making publicly available a new validated method for implicit attitude measurement. More accurate measurement will facilitate theory testing and future efforts to identify causes of, and remedies for, destructive behavior.

[unreadable] DESCRIPTION (provided by applicant): This project aims to better characterize the neural bases of nicotine craving and emotional responses to smoking cues. Nearly one in four American adults smokes cigarettes and twenty percent of U.S. deaths are caused by diseases linked to smoking. Most of those who start smoking find it difficult to quit. Leading theories of nicotine addiction indicate that emotional and motivational responses to smoking-related cues - such as the sight and smell of cigarettes and other smokers - are important in maintaining nicotine dependence. Brain imaging studies have identified several key regions that are responsive to smoking cues and that are related to reports of craving and affect. To date, research on drug cue reactivity has relied heavily on self-reports of emotions and cravings in response to cues. However, self-reports are limited by participants' willingness and ability to introspect. The current project uses a recently developed indirect behavioral measure of affective responses to drug cues, combined with functional neuro-imaging (fMRI) and traditional self-reports. The indirect measure can be administered during brain imaging to identify regional activity related to performance. The research strategy is to expose smokers and non-smoking participants to smoking-relates images while recording their verbal responses, indirect behavioral responses, and neural responses. A unique aspect of the proposal is integration across several methods to provide converging evidence for neural, behavioral, and subjective components of responses. The goal is to distinguish cortical and sub-cortical activity that is responsive to smoking cues and that is (a) related uniquely to self-reported responses (b) related to the indirect measure but not to self-report responses, (c) related to both or (d) neither. This project takes advantage of the close ties between Duke and UNC instantiated in the Duke-UNC Brain Imaging and Analysis Center (BIAC). The long-term objective is to lay the groundwork for a more extensive project examining reportable and un-reportable neural responses that may uniquely predict nicotine use and cessation versus relapse. The prevalence of nicotine addiction and the widespread health implications of smoking make clear the relevance of these considerations for drug addiction and public health more generally. [unreadable] [unreadable] [unreadable]

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).

Some of the traits social scientists most want to measure are the ones that research volunteers are least likely to report. Socially sensitive topics such as racial prejudice have historically presented challenges for social science researchers because respondents are reluctant to report about them on questionnaires. Implicit tests offer a way around that obstacle by measuring attitudes without relying on self-reports. Instead, implicit tests assess attitudes indirectly via their impact on task performance. Implicit attitude tests have thus become a critical tool for studying race attitudes in modern societies, where blatant expressions of prejudice are taboo. The goal of this project is to develop an optimally reliable and valid implicit test of racial attitudes. The Affect Misattribution Procedure (AMP) is an implicit measure that works on the principle that people have difficulty distinguishing their feelings toward two items presented in rapid succession. Using this principle, the test measures favorable or unfavorable attitudes toward various racial groups without asking respondents to express their attitudes toward those groups. Initial research has shown the AMP to be a valid test with several advantages over other available tests. This project aims to build on past progress by using item response theory, a state-of-the-art psychometric approach, to optimize the quality of the test. The study will use a nationally representative sample to calibrate a pool of items with known psychometric properties. The items can then be used to measure racial attitudes on a standardized scale. The project will produce a short test with optimal measurement properties that is freely available to researchers in the form of user-friendly software. Such a test will contribute to research infrastructure and enhance scientific understanding across many fields where racial attitudes are important, including psychology, political science, economics, sociology, and law.

DESCRIPTION (provided by applicant): Despite considerable efforts to increase the representation of women and members of racial minorities in STEM disciplines, gender and race gaps persist. Academic stereotypes have been implicated in contributing to these gaps in several ways; the current research is designed to better understand how stereotypes shape children's expectancies for future success and valuing of specific academic domains. Aims of the proposed research are to test competing hypotheses about reasons for age, race, and gender differences in explicit race and gender stereotypes and to examine the relative importance of implicit and explicit stereotypes in predicting the motivational beliefs and behaviors of Black and White youth. If funded, this small project will lay the foundation for a larger longitudinal study examining the development of implicit and explicit academic stereotypes in Black and White youth. Three hundred White and African American youth from Grades 3, 6, and 9 will participate. The sample will be balanced on grade level, gender, and race. Building on new research in developmental and social psychology, the investigators will measure stereotypes both explicitly (using self-report measures) and implicitly (using cognitive tasks that do not require self-reports). Implicit measures will allow us to capture stereotypic thought processes that may operate outside awareness or beyond intentional control. By comparing implicit and explicit stereotypes, we aim to understand at what age's academic stereotypes begin to come automatically to mind, and when youth are willing and able to express versus reject these stereotypes. We will then be able to test hypotheses about age, race, and gender differences in stereotype reports, hoping to clarify inconsistent findings of earlier research. Students will also complete measures of academic self- concept, task interest, causal attributions, and domain-specific (i.e., English, science, and math) academic engagement and teachers will provide ratings of students' domain-specific engagement. Our tests of hypotheses about the relations between children's emerging stereotypes and their motivational beliefs and behaviors will enhance theory about ways that stereotypes lead some students to opt out of advanced classes in STEM disciplines and integrate the large literature on implicit social cognition with well-established theories of the development of achievement motivation. Thus, this project will shed light on the role that these aspects of implicit social cognition play in shaping group differences in achievement in STEM disciplines and identify new targets for interventions, potentially having a major impact on understanding the social dimensions of children's academic development.

In the last half century, economic inequality in the United States and in many other developed economies has increased dramatically. The top 0.1% of Americans now holds as much wealth as the bottom 90%. Researchers across the social sciences, and stakeholders across the political spectrum, have emphasized how growing inequality may pose threats to the social fabric of society and to the health and well-being of individuals within it. This project aims to understand how changes in the inequality of an income distribution affects the decision making of individuals. The specific hypothesis guiding this project is that higher levels of inequality encourage high risk / high reward decision strategies. Such strategies can have both benefits (for those who receive high rewards) and costs (for those who lose due to high risk).

The project will use six behavioral experiments to establish the cause and effect relationship between inequality in economic outcomes and decision making strategies. The project will also employ an analysis of large-scale data from government sources (Census, US Department of Labor) and internet search data to examine the relationship between economic inequality and risk taking in everyday life. This "big data" approach will extend findings from the laboratory to consequential types of risky decisions, including financial decisions (such as taking pay day loans) as well as health decisions (such as smoking or drug use). This research will shed light on how and why economic inequality changes people's priorities when making decisions, with consequences for the health and well-being of individuals and societies.