We introduce the Relational Responding Task (RRT) as a tool for capturing beliefs at the implicit level. Flemish participants were asked to respond as if they believed that Flemish people are more intelligent than immigrants (e.g., respond “true” to the statement “Flemish people are wiser than immigrants”) or to respond as if they believed that immigrants are more intelligent than Flemish people (e.g., respond “true” to the statement “Flemish people are dumber than immigrants”). The difference in performance between these two (...) tasks correlated with ratings of the extent to which participants explicitly endorsed the belief that Flemish people are more intelligent than immigrants and with questionnaire measures of subtle and blatant racism. The current study provides a first step towards validating RRT effects as a viable measure of implicit beliefs. (shrink)

In this article, we describe four theoretical and methodological problems that have impeded implicit attitude research and the popular understanding of its findings. The problems all revolve around assumptions made about the relationships among measures, constructs, cognitive processes, and features of processing. These assumptions have confused our understandings of exactly what we are measuring, the processes that produce implicit evaluations, the meaning of differences in implicit evaluations across people and contexts, the meaning of changes in implicit evaluations in response to (...) intervention, and how implicit evaluations predict behavior. We describe formal modeling as one means to address these problems, and provide illustrative examples. Clarifying these issues has important implications for our understanding of who has particular implicit evaluations and why, when those evaluations are likely to be particularly problematic, how we might best try to change them, and what interventions are best suited to minimize the effects of implicit evaluations on behavior. (shrink)

Identification of propositions as the core of attitudes and beliefs (De Houwer, 2014) has resulted in the development of implicit measures targeting personal evaluations of complex sentences (e.g., the IRAP or the RRT). Whereas their utility is uncontested, these paradigms are subject to limitations inherent in their block based design, such as allowing assessment of only a single belief at a time. We introduce the Propositional Evaluation Paradigm (PEP) for assessment of multiple propositional beliefs within a single experimental block. Two (...) experiments provide first evidence for the PEP’s validity. In Experiment 1 endorsement of racist beliefs measured with the PEP was related to criterion variables such as explicit racism assessed via questionnaire and indicators of behavioral tendencies. Experiment 2 indicates that the PEP’s implicit racism scores may predict actual behavior over and above explicit, self-report measures. Finally, Experiment 3 tested the PEP's applicability in the domain of hiring discrimination. Whereas general PEP based gender stereotypes were not related to hiring bias, results suggest a possible role of female stereotypes in hiring discrimination. In the context of these findings, we discuss both the potential and possible challenges in adopting the PEP to different beliefs. In sum, these initial findings suggest that the PEP may offer researchers a reliable and easily administrable option for the indirect assessment of propositional evaluations. (shrink)

Two decades ago, the introduction of the Implicit Association Test (IAT) sparked enthusiastic reactions. With implicit measures like the IAT, researchers hoped to finally be able to bridge the gap between self-reported attitudes on one hand and behavior on the other. Twenty years of research and several meta-analyses later, however, we have to conclude that neither the IAT nor its derivatives have fulfilled these expectations. Their predictive value for behavioral criteria is weak and their incremental validity over and above self-report (...) measures is negligible. In our review, we present an overview of explanations for these unsatisfactory findings and delineate promising ways forward. Over the years, several reasons for the IAT’s weak predictive validity have been proposed. They point to four potentially problematic features: First, the IAT is by no means a pure measure of individual differences in associations but suffers from extraneous influences like recoding. Hence, the predictive validity of IAT-scores should not be confused with the predictive validity of associations. Second, with the IAT, we usually aim to measure evaluation (“liking”) instead of motivation (“wanting”). Yet, behavior might be determined much more often by the latter than the former. Third, the IAT focuses on measuring associations instead of propositional beliefs and thus taps into a construct that might be too unspecific to account for behavior. Finally, studies on predictive validity are often characterized by a mismatch between predictor and criterion (e.g., while behavior is highly context-specific, the IAT usually takes into account neither situation nor domain). Recent research, however, also revealed advances addressing each of these problems, namely (1) procedural and analytical advances to control for recoding in the IAT, (2) measurement procedures to assess implicit wanting, (3) measurement procedures to assess implicit beliefs, and (4) approaches to increase the fit between implicit measures and behavioral criteria (e.g., by incorporating contextual information). Implicit measures like the IAT hold an enormous potential. In order to allow them to fulfill this potential, however, we have to refine our understanding of these measures, and we should incorporate recent conceptual and methodological advancements. This review provides specific recommendations on how to do so. (shrink)

An implicit association test (IAT) measures differential association of 2 target concepts with an attribute. The 2 concepts appear in a 2-choice task (e.g., flower vs. insect names), and the attribute in a 2nd task (e.g., pleasant vs. unpleasant words for an evaluation attribute). When instructions oblige highly associated categories (e.g., flower + pleasant) to share a response key, performance is faster than when less associated categories (e.g., insect + pleasant) share a key. This performance difference implicitly measures differential association (...) of the 2 concepts with the attribute. In 3 experiments, the IAT was sensitive to (a) near-universal evaluative differences (e.g., flower vs. insect), (b) expected individual differences in evaluative associations (Japanese + pleasant vs. Korean + pleasant for Japanese vs. Korean subjects), and (c) consciously disavowed evaluative differences (Black + pleasant vs. White + pleasant for self-described unprejudiced White subjects). (PsycINFO Database Record (c) 2016 APA, all rights reserved). (shrink)