The Role of Self-Involvement in Shifting IAT Effects

Author(s):  
Maddalena Marini ◽  
Sandro Rubichi ◽  
Giuseppe Sartori

Explicit measures can be affected by self-involvement in processing of a message (Johnson & Eagly, 1989). Here, we show that self-involvement in a counter-stereotypical message also influences implicit measures such as the Implicit Association Test (Greenwald, McGhee, & Schwartz, 1998). In our study, racial attitudes changed only after reading a counter-stereotypical scenario in which participants were asked to imagine themselves as victims of an assault as opposed to simply imagine an assault to a person. This shift did not depend on evaluative instructions and it was transient as it was no longer present after 1 week. These results suggest that the self-involvement might be an important factor in shifting implicit measures.

2020 ◽  
Vol 35 (4) ◽  
pp. 23-44
Author(s):  
Axelle Faure-Ferlet ◽  
Sonia Capelli ◽  
William Sabadie

This research investigates whether a label on cooperative governance influences the perceived taste of a product through a sensation transfer process. The first study measures perceived taste of unbranded products implicitly (via an Implicit Association Test) and explicitly (via a survey). The label improves the implicitly and explicitly perceived taste. The second study, reproducing the same protocols with branded products, confirms this result for implicitly perceived taste, but the effect of the label on explicitly perceived taste disappears. Because implicit measures are more predictive of routine purchasing than are explicit measures, we recommend spotlighting cooperative governance on food products.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Maddalena Marini ◽  
Pamela D. Waterman ◽  
Emry Breedlove ◽  
Jarvis T. Chen ◽  
Christian Testa ◽  
...  

Abstract Background To date, research assessing discrimination has employed primarily explicit measures (i.e., self-reports), which can be subject to intentional and social desirability processes. Only a few studies, focusing on sex and race/ethnicity discrimination, have relied on implicit measures (i.e., Implicit Association Test, IAT), which permit assessing mental representations that are outside of conscious control. This study aims to advance measurement of discrimination by extending the application of implicit measures to multiple types of discrimination and optimizing the time required for the administration of these instruments. Methods Between September 27th 2019 and February 9th 2020, we conducted six experiments (984 participants) to assess implicit and explicit discrimination based on race/ethnicity, sex, gender identity, sexual orientation, weight, and age. Implicit discrimination was measured by using the Brief-Implicit Association Test (B-IAT), a new validated version of the IAT developed to shorten the time needed (from ≈15 to ≈2 min) to assess implicit mental representations, while explicit discrimination was assessed using self-reported items. Results Among participants (mean age = 37.8), 68.6% were White Non-Hispanic; 69% were females; 76.1% were heterosexual; 90.7% were gender conforming; 52.8% were medium weight; and 41.5% had an advanced level of education. Overall, we found implicit and explicit recognition of discrimination towards all the target groups (stronger for members of the target than dominant groups). Some exceptions emerged in experiments investigating race/ethnicity and weight discrimination. In the racism experiment, only people of Color showed an implicit recognition of discrimination towards the target group, while White people were neutral. In the fatphobia experiment, participants who were not heavy showed a slight implicit recognition of discrimination towards the dominant group, while heavy participants were neutral. Conclusions This study provides evidence that the B-IAT is a valuable tool for quickly assessing multiple types of implicit discrimination. It shows also that implicit and explicit measures can display diverging results, thus indicating that research would benefit from the use of both these instruments. These results have important implications for the assessment of discrimination in health research as well as in social and psychological science.


2016 ◽  
Author(s):  
Brian A. Nosek ◽  
Yoav Bar-Anan ◽  
Natarajan Sriram ◽  
Jordan Axt ◽  
Anthony G. Greenwald

A brief version of the Implicit Association Test (BIAT) has been introduced. The present research identified analytical best practices for overall psychometric performance of the BIAT. In 7 studies and multiple replications, we investigated analytic practices with several evaluation criteria: sensitivity to detecting known effects and group differences, internal consistency, relations with implicit measures of the same topic, relations with explicit measures of the same topic and other criterion variables, and resistance to an extraneous influence of average response time. The data transformation algorithms D outperformed other approaches. This replicates and extends the strong prior performance of D compared to conventional analytic techniques. We conclude with recommended analytic practices for standard use of the BIAT.


2019 ◽  
Author(s):  
Louis H. Irving ◽  
Colin Smith

The Implicit Association Test (IAT) is nearly synonymous with the implicit attitude construct. At the same time, correlations between the IAT and criterion measures are often remarkably low. Developed within research using explicit measures of attitudes, the correspondence principle posits that measures should better predict criteria when there is a match in terms of the level of generality or specificity at which both are conceptualized (Ajzen & Fishbein, 1977). As such, weak implicit-criterion correlations are to be expected when broad general implicit measures are used to predict highly specific criteria. Research using explicit measures of attitudes consistently supports the correspondence principle, but conceptual correspondence is rarely considered by researchers using implicit measures to predict behavior and other relevant criterion measures. In five experiments (total N = 4650), we provide the first direct evidence demonstrating the relevance of the correspondence principle to the predictive validity of the IAT and Single Target IAT. That said, it is not the case that the IAT always predicts criteria better when correspondence is high. Inconsistency across the pattern of results suggests there is much more that remains to be understood about the relevance of the correspondence principle to the implicit-criterion relationship. Taken together, however, our findings suggest that conceptual correspondence typically increases (and never decreases) the magnitude of implicit-behavior and implicit-explicit relationships. We provide a framework for future research necessary to establish when correspondence is more likely to increase the predictive validity of measures such as the IAT.


2008 ◽  
Vol 29 (2) ◽  
pp. 70-79 ◽  
Author(s):  
Tobias Gschwendner ◽  
Wilhelm Hofmann ◽  
Manfred Schmitt

The current research explored the stability of the Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998 ) depending on contextual constraints and chronic accessibility. Two studies incorporated background pictures into the IAT. In Study 1, the 2-week stability of an IAT assessing anxiety was higher when IAT stimuli were embedded in an anxiety-relevant background (e.g., a snake). In Study 2, this context effect could be replicated in the domain of racial attitudes. Moreover, the context effect in Study 2 was especially pronounced for participants with high chronic access to the relevant concept. The results support the assumption that implicit measures like the IAT are sensitive to contextual constraints and that these constraints can be utilized to enhance the stability of the IAT.


Author(s):  
Brad Pinter ◽  
Anthony G. Greenwald

Abstract. A. Karpinski (2004 ) recently criticized Implicit Association Test (IAT) measures of self-esteem, arguing that their measurements of self-associations are compromised by their contrasting self with a putatively extremely negative second category, the nonspecific other. The present data show, to the contrary, that the nonspecific other category in the self-esteem IAT is near neutral in valence. Validity of the self-esteem IAT is most appropriately assessed by examining its correlations with conceptually related measures. That has been done in several previous studies that are reviewed here. The nonspecific other category is only one of several choices for representing the concept of other in self-esteem IATs. Choice of the appropriate other category to contrast with self in self-esteem IATs should be guided by the needs of the research question being addressed.


2018 ◽  
Author(s):  
Ian Hussey ◽  
Jan De Houwer

The Implicit Association Test has been used in online studies to assess implicit racial attitudes in over seven million participants. Although typically used as an assessment measure, results from four pre-registered experiments (N = 940) demonstrated that completing a Race IAT exacerbates the negative implicit attitudes that it seeks to assess. Increases in White participants’ negative automatic racial evaluations of Black people were observed across two different implicit measures (SC-IAT and AMP) but did not generalize to another measure of automatic racial bias (Shooter Bias task). Results highlight an important caveat for the Race IAT, but also for many other forms of psychological assessment: that by measuring, we often perturb the system that we wish to understand.


2020 ◽  
Author(s):  
Jessica Röhner ◽  
Calvin K. Lai

<p>Performance on implicit measures reflects construct-specific and non-construct-specific processes. This creates an interpretive issue for understanding interventions to change implicit measures: change in performance could reflect changes in the constructs-of-interest or changes in other mental processes. We re-analyzed data from six studies (<i>N</i> = 23,342) to examine the process-level effects of 17 interventions and one sham intervention to change race Implicit Association Test (IAT) performance. Diffusion models decompose overall IAT performance (<i>D</i>-scores) into construct-specific (ease of decision-making), and non-construct-specific processes (speed-accuracy tradeoffs, non-decision-related processes like motor execution). Interventions that effectively reduced <i>D-</i>scores changed ease of decision-making on compatible and incompatible trials. They also eliminated differences in speed-accuracy tradeoffs between compatible and incompatible trials. Non-decision-related processes were impacted by two interventions only. There was little evidence that interventions had any long-term effects. These findings highlight the value of diffusion modeling for understanding the mechanisms by which interventions affect implicit measure performance.</p>


2021 ◽  
pp. 194855062110371
Author(s):  
Benedek Kurdi ◽  
Thomas C. Mann ◽  
Melissa J. Ferguson

Implicit evaluations can be malleable via reinterpretation of previously encountered evidence. Here, we report three studies ( N = 1,007) investigating the robustness of this updating modality using ecologically realistic materials. Participants were first introduced to a target who killed an endangered black rhino in Namibia. They then listened to a real podcast providing counterattitudinal information on the benefits of trophy hunting. The podcast resulted in considerable revisions of initially negative implicit evaluations toward positivity, consistently across implicit measures (affect misattribution procedures vs. implicit association test), samples (American students vs. nonstudents from various countries), study settings (lab vs. online), and the presence versus absence of a memory retrieval manipulation prompting reflection on participants’ views on trophy hunting. Taken together, these findings suggest that reinterpretation can shift implicit evaluations of even highly negative targets, including under conditions of external validity.


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