This study examined the effect of counter-stereotypical examples in the application of gender stereotypes, particularly how and when they affect impression formation regarding warmth. Participants (N = 140) evaluated ambiguous drawings after being primed with stereotypical and counter-stereotypical individuals. Targets following counter-stereotypical female primes were evaluated as less warm than targets after stereotypical female primes. However, counter-stereotypical male primes did not prompt warmer evaluations; instead, they elicited the lowest warmth judgments. The multinomial model analysis revealed stereotype activation and application as significant processes explaining the responses, with activation acting as a mediator that negatively influenced application. Individual factors such as sexism and gender essentialism were negatively related to stereotype activation and application, while we found no association with participants’ motivations to respond without sexism. An exploratory analysis combining this data with a prior experiment (N = 136) showed that exposure to counter-stereotypical examples attenuated the dissociation between activation and application.
Past research using the quad model indicates that multiple cognitive processes underlie responses on the Implicit Association Test (IAT). The quad model assumes that one target group is evaluated positively (e.g., Whitegood) and the other target group is evaluated negatively (e.g., Black-bad). Though theoretically defensible, this assumption precludes the possibility that both target groups are evaluated positively, as some theories of intergroup bias posit. We sought to validate an egalitarian extension of the quad model (E-quad model) that estimates positive evaluations of both target groups. In a series of six experiments, we found that the E-quad model fits IAT data, but we did not find consistent evidence of the model’s construct validity. Moreover, model selection indices suggest that the original quad model fits these data better than does the E-quad model. Taken together, these experiments ultimately support the quad model as better for the IAT.
There was a surge in anti-Asian hate in the wake of the coronavirus (COVID-19) outbreak. While people have argued that the coverage of the pandemic was to blame, there has been no empirical investigation of the role that news coverage of COVID-19 played in inflaming vitriol or stereotypes towards Asian people. Natural language processing analyses of more than 12 million tweets from U.S. Twitter between March 2019 and May 2021 showed that overall sentiment became more negative, and that political and racialized news coverage of COVID-19 was associated with increases in negative sentiment. Additionally, the use of warmth stereotypes decreased while competent stereotype content increased. Our results show that social media posts may reflect the biases that seep into our society over time, which may serve as a “canary in the coal mine” of racism.
First (given) names are subject to stereotypes and can be a source of bias when making many important decisions. However, previous research on name bias is fragmented and informed by disjointed theories. Here, we investigate the underlying structure of name biases to provide a unifying theory of the social perception of names. Across two samples collected in separate European countries (UK and Germany; N = 1,083 and 972, respectively), male and female names were rated on multiple social traits, with mean trait ratings for each name then being entered into a Principal Components Analysis. In both samples, and for both male and female names, the same four-component structure was found: These included components corresponding with valence/competence, dominance/warmth, youth/attractiveness, and familiarity. We propose that this four-component model could provide an overarching theoretical framework that could help unite research on name bias and generate new testable hypotheses.
Driving is a task that demands a range of cognitive processes, both simple and complex. Detecting potential hazards is crucial to reducing traffic accidents. This study examined drivers’ perception of hazardous situations in virtual reality (VR). Fifty-two participants completed demographic and driving habit questionnaires before watching 10 hazardous and 11 control videos, which were presented randomly on a Varjo eye-tracking VR headset. After each video, participants rated the perceived risk of the situation. Results indicated that hazardous videos were rated riskier. Additionally, during hazardous videos, participants had longer and more frequent fixations, along with greater saccade duration and count. Eye-tracking data were not correlated with driving habits. Driving experience was related to fewer penalties and accidents. These findings suggest that hazardous driving videos are perceived differently from normal ones in terms of eye-tracking parameters and subjective risk evaluation. Future research should investigate the cues that contribute to these perceptual differences.
