Examining the Relationships Among Categorization, Stereotype Activation, and Stereotype Application

Rees, Heather; Debbie, S.; Sherman, Jeffrey W.

doi:10.1177/0146167219861431

Cited by 23 publications

(20 citation statements)

References 26 publications

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“…The weak behavioral and process effects in Experiment 2 might reflect specifics of the category salience manipulation we used (i.e., structuring the WIT so that race or age was more contextually distinctive). Although such “blocked” designs have been used previously to draw attention to particular categories (e.g., Jones & Fazio, 2010; Macrae & Cloutier, 2009; Rees et al, 2020), they are arguably more subtle category salience manipulations than the face categorization tasks used in our other experiments.…”

Section: Discussionmentioning

confidence: 99%

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Category salience and racial bias in weapon identification: A diffusion modeling approach.

Todd¹,

Johnson²,

Lassetter³

et al. 2021

Journal of Personality and Social Psychology

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Stereotypes linking Black Americans with guns can have life-altering outcomes, making it important to identify factors that shape such weapon identification biases and how they do so. We report 6 experiments that provide a mechanistic account of how category salience affects weapon identification bias elicited by male faces varying in race (Black, White) and age (men, boys). Behavioral analyses of error rates and response latencies revealed that, when race was salient, faces of Black versus White males (regardless of age) facilitated the classification of objects as guns versus tools. When a category other than race was salient, racial bias in behavior was reduced, though not eliminated. In Experiments 1–4, racial bias was weaker when participants attended to a social category besides race (i.e., age). In Experiments 5 and 6, racial bias was weaker when participants attended to an applicable, yet nonsubstantive category (i.e., the color of a dot on the face). Across experiments, process analyses using diffusion models revealed that, when race was salient, seeing Black versus White male faces led to an initial bias to favor the “gun” response. When a category besides race (i.e., age, dot color) was salient, racial bias in the relative start point was reduced, though not eliminated. These results suggest that the magnitude of racial bias in weapon identification may differ depending on what social category is salient. The collective findings also highlight the utility of diffusion modeling for elucidating how category salience shapes processes underlying racial biases in behavior.

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Section: Discussionmentioning

confidence: 99%

“…Varying age while holding race constant within each block ensured that age was more distinctive throughout the task. Such “blocked” designs have been used in prior work to direct attention to specific identity dimensions (e.g., Jones & Fazio, 2010; Macrae & Cloutier, 2009; Mitchell et al, 2003; Rees, Ma, & Sherman, 2020).…”

Section: Methodsmentioning

confidence: 99%

Category salience and racial bias in weapon identification: A diffusion modeling approach.

Todd¹,

Johnson²,

Lassetter³

et al. 2021

Journal of Personality and Social Psychology

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“… Blair et al, 2002 ). Building on this initial research, the SMT together with the multinomial model has been used in a growing number of studies on stereotyping ( Rees et al, 2019 , 2020 ; Rivers et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Further Validation of Measures of Target Detection and Stereotype Activation in the Stereotype Misperception Task

Reichardt

Rivers

Reichardt³

et al. 2020

Front. Psychol.

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Previous research presented a multinomial model to estimate four latent processes (target detection, stereotype activation, stereotype application, guessing) that contribute to responses in the Stereotype Misperception Task, an indirect measure of stereotyping (Krieglmeyer and Sherman, 2012). The present research further investigates the validity of the target detection (D) and stereotype activation (SAC) parameters. To this end, the data from Experiment 2 and Experiment 4 in Krieglmeyer and Sherman (2012) were re-analyzed using a bootstrap method to investigate the robustness of the results. Furthermore, two conceptual replication studies were conducted and analyzed with the same bootstrap method. A manipulation of target distinctness influenced the D parameter as predicted. A manipulation of prime prototypicality influenced the SAC parameter as predicted. Taken together, the results support the validity of the D and SAC model parameters.

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“…One such heuristic is stereotyping. Stereotyping refers to the automatic tendency to categorize people based on easily observable categories (such as gender, race, age, or marital status; Rees et al, 2020; Tajfel, 1969). Although stereotyping within an employment interview can reduce the interviewer's cognitive load, it may also promote inaccurate perceptions of the interviewee.…”

Section: Introductionmentioning

confidence: 99%

The structured interview's resistance to gender discrimination under cognitive load

Kith

Cassidy

Powell

2022

Int J Selection Assessment

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Job interviews are cognitively demanding tasks for interviewers. However, it is unclear whether the high cognitive load (CL) that interviewers face will ultimately compromise the resistance to discrimination that otherwise distinguishes structured interviews from other selection methods. Using a two-study experimental design, we explored the effect of cognitive load on gender discrimination in structured job interviews. In Study 1, participants completed an online interview simulation in which they assessed both a male and a female candidate applying for either a maleor female-dominated job, while under either a high or low degree of cognitive load.Participants provided ratings of each candidate's suitability for the job as well as a final, ipsative hiring decision. Study 2 served as a larger replication of Study 1.Overall, CL was not found to affect candidate ratings. These results support the structured interview's general resistance to discrimination.

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Examining the Relationships Among Categorization, Stereotype Activation, and Stereotype Application

Cited by 23 publications

References 26 publications

Category salience and racial bias in weapon identification: A diffusion modeling approach.

Category salience and racial bias in weapon identification: A diffusion modeling approach.

Further Validation of Measures of Target Detection and Stereotype Activation in the Stereotype Misperception Task

The structured interview's resistance to gender discrimination under cognitive load

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