By: D. Gage Jordan
A review: First, one should appropriately define what an implicit measure is, as well as what an attitude is. Briefly, an implicit measure is a questionnaire or paradigm that seeks to infer the mental contents of a respondent or participant (Gawronski, 2009). These measures differ from traditional self-report (i.e., “explicit”) questionnaires in that they attempt to tap into unconscious representations (whereas, for example, a questionnaire about depressive symptoms is going to explicitly ask about the respondent’s depressive symptoms). An attitude, on the other hand, is a positive or negative judgment about an attitude object (i.e., the entity one has an attitude about; Fiske, 2014). Thus, you cannot have an attitude without having a source for that attitude.
So, if an attitude bears a positive or negative valence, it may be worthwhile to examine them more objectively, if possible. Thus, research turns to implicit measures as a paradigm used to examine underlying attitudes about certain things. One example is the Implicit Association Test (IAT) developed by Greenwald, McGhee, and Schwartz (1998). A typical IAT task measures implicit racial preferences (Green et al., 2007):
Subjects are presented with images of black and white faces, with positive and negative words on a computer screen. | Subjects are instructed to categorize these stimuli as quickly as possible according to the following categories: (1) either a white face or a positive word; (2) either a black face or a negative word; (3) either a white face or a negative word; or (4) either a black face or a positive word (Boorsboom, 2006). |
Training implicit preferences?
Researchers have considered the latter idea for quite some time. Recently, an article by Lai et al. (2014) examined a panoply of studies using the IAT that could possibly reduce implicit preferences for White faces amongst White participants. Lai and colleagues review previous literature, finding that implicit racial preferences can actually be shifted through changes in emotional states (e.g., inducing these states), exposing the participant to counter-stereotypical exemplars (e.g., exemplary people from minority races), and setting egalitarian goals (with the prospect that everyone wants to work towards the greater good). Thus, their research contest was on – and it involved a lot of failure, but some success. The ones that are successful obviously stand out; across a variety of studies, it appeared that exposing participants to an evocative story, wherein a White man assaults the participant and a Black man rescues the participant, was quite effective. In addition, seeing pairings of Black faces with positively valenced words and White faces with negatively valenced words appeared effective at reducing implicit preference (notably, an earlier study also paired Black with Good repeatedly in the IAT). Another notable study asked participants to use “implementation strategies,” such as telling themselves they will respond to Black faces by thinking “good.” Interestingly, this was also effective at reducing implicit preferences. Taken together, these results add to the literature delineating under which conditions implicit racial biases/preferences may be reduced, particularly imagining vivid counter-stereotypic scenarios where the participant is part of the story, using intentional strategies to overcome biases (e.g., telling oneself to associated Black with Good), and evaluative conditioning (i.e., pairing an attitude object with something emotional can shift the attitude object to that emotion), such as pairing Black faces with positive words such as “good.”
Further consideration
As mentioned earlier, implicit measures such as the IAT are quite seductive, even meretricious at times. Examining the extant literature more closely, one uncovers a host of criticisms against the use of implicit measures (a non-exhaustive list) including: poor psychometric validation (e.g., the IAT’s internal consistency regularly fluctuates), under which conditions implicit measures predict, and their relationship with explicit, self-report measures (Gawronski, 2009). The latter criticism may be particularly germane to the Lai et al. (2014) study. If we can change one’s underlying preferences for a certain race, how does that translate to actual, observable behavior? In sum, researchers and consumers of researchers alike should continue to understand how implicit measures related to actual behavior, and under what conditions this behavior is predicted (e.g., moderators). Such methods (along with longitudinal data), may make the use of implicit measures as clinical (or attitude change) tools more appealing.
References
- Borsboom, D. (2006). The attack of the psychometricians. Psychometrika, 71, 425-440.
- Fiske, S. T. (2014). Social Beings: Core Motives in Social Psychology (3rd edition). Hoboken, NJ: Wiley.
- Gawronski, B. (2009). Ten frequently asked questions about implicit measures and their frequently supposed, but not entirely correct answers. Canadian Psychology/Psychologie canadienne, 50, 141-150.
- Green, A. R., Carney, D. R., Pallin, D. J., Ngo, L. H., Raymond, K. L., Iezzoni, L. I., & Banaji, M. R. (2007). Implicit bias among physicians and its prediction of thrombolysis decisions for black and white patients. Journal of General Internal Medicine, 22, 1231-1238.
- Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. (1998). Measuring individual differences in implicit cognition: the implicit association test. Journal of Personality and Social Psychology, 74, 1464-1480.
- Lai, C. K., Marini, M., Lehr, S. A., Cerruti, C., Shin, J. E. L., Joy-Gaba, J. A., ... & Frazier, R. S. (2014). Reducing implicit racial preferences: I. A comparative investigation of 17 interventions. Journal of Experimental Psychology: General, 143, 1765-1785.