The Role of Explicit Categorization in the Implicit Association, Term Paper Example

This paper provides a summary and a critical analysis of the article “The role of explicit categorization in the implicit association” by (Yamaguchi and Beattie 2020). It describes an in-depth study focused on ways in which a stimulus’ characteristics lead to different reactions in a multi-attribute form of the Implicit Association Test (IAT).In a general context, the purpose of the study was to determine how prejudged (explicit) categorization of people can play a role in studying an alleged (implicit) association.

In their study, Yamaguchi and Beattie experiment to determine the correlation between explicit categorization and implicit association. The independent variable was the implicit association and the dependent variable as the explicit categorization. In the methodology, the first experiment categorized participants based on either race or gender. In contrast, the second did not classify them on any racial basis but on how the general picture would look. The last two experiments gave a heads up that IAT that relied upon tasks given to such groups is determined by whether the task was social or non-social. The results from their study indicated that both race and gender of the participant had an impact on task performance no matter the relevant attribute intended, which could only mean that individuals are still categorized based on task-irrelevant social characteristics. Explicit categorization showed a significant impact on implicit social biases. The second set up reproduced task-relevant IAT effects and their reliance on explicit classification. The last experiment, as stated above, attributes impact entirely on the task at hand.  These results are relevant in that they provide evidence that placing individuals in categories do not usually point out to traits presumed of them.  The results raised more questions about the underlying assumptions that back up the interpretations of results from a typical IAT test, and that leads to our hypothesis that IAT is effective on condition that individuals are not categorized due to traits or prejudged attributes.

The study leads us to the assumption that in as much as IAT might be useful in analyzing attributes of a group of people, it might have many perspectives than imagined. In their study, Hahn and Gawronski (769) conducted six experiments to expand on the conflicting theoretical conceptualization of implicit bias where participants that predicted their responses in line with pictures of various minority groups on future IATs showed increased alignment between implicit and explicit preferences in their first three studies. Self-reported acknowledgments of being racially biased were reported in the other three studies. All the studies reported significant effects even when the participants did not complete the IATs. The significant conclusion from the studies is that directing people’s attention to their spontaneous affective reactions only points to bias, and this is in line with Yamaguchi and Beattie’s finding in their first study, thus a strength.

Forscher et al. ( 522) used a novel technique known as Network metanalysis to investigate the effectiveness of procedures in changing implicit measures, which was defined as response biases on implicit tasks (Forscher et al. 522).  They found out that procedures that associate sets of ideologies invoke goals and motivation or tax mental resources changed implicit measures the most. In contrast, a procedure that induced threat or affirmation like racial segregation like in our case study changed implicit measures the least. The study concluded that changes in implicit measures do not interfere with changes in explicit measures or behavior. Further, it suggested that changes in implicit measures are likely to change; they do not necessarily translate into a do not infer a change in behavior explicit measures. This is a strength to Yamaguchi and Beattie’s study findings as they correlate.

Furthermore, there seems to be increasing evidence showing unequal rates of change in implicit and explicit evaluation. Thus any attitude-focused inconsistency may reveal a shift in attitude progressing towards inclusion. According to Timo and Gosche (427), measures of implicit attitude are likely to be influenced by social appeal.  Their study incorporated a new measure for attitude founded on the Single-Target Implicit Association Test (ST-IAT), where participants completed a questionnaire on their explicit attitude towards education that involved all regardless of their backgrounds, race, or appearance on socially desirable responses. Their results pointed out on the psychometric quality of the inclusion ST-IAT test. This model used here was a complete opposite of Yamaguchi and Beattie’s first experiment in their study. Their article of 2018 (Timo and Grosche 427) further recommends the use of measures of implicit attitude in the reduction of the influence of social appeal in inclusive education research, which was not followed by Yamaguchi and Beattie’s first experimental set up thus a weakness of their study.

Concerning the theories named above, this case study dwelt much on racial segregation as a testing approach for the efficacy of IAT, which only leads to an apparent implicit social bias. IAT is an effective tool for studying social behaviors like inclusivity, attitude, goals and motivation, and tasks at hand and not for explicit categorization, as earlier implied by the first experiment in our case study. In my opinion, I would have used time as a variable instead of racial separation to avoid bias.

Works cited

Forscher, Patrick S, et al. “A meta-analysis of procedures to change implicit measures.” Journal of personality and social psychology (2019): 117(3), 522

Hahn, Adam, and Bertram, Gawronski. “Facing one’s implicit biases: From awareness to acknowledgment.” Journal of Personality and Social psychology (2019): 116(5),769.

Lüke, Timo, and Michael Grosche. “Implicitly measuring attitudes towards inclusive education: A new attitude test based on single-target implicit associations.” European Journal of Special Needs Education 33.3 (2018): 427-436.

Yamaguchi, Motonori, and Geoffrey, Beattie. “The role of explicit categorization in the Implicit Association Test.” Journal of Experimental Psychology: General (2020): 149(5),809-827. https://doi.org/10.1037/xge0000685