Stereotypes, Emotion Recognition &amp; Facemasks - II
Creators: name: Primbs, Maximilian
name: Bijlstra, Gijsbert
name: Rinck, Mike
Publisher: name: Open Science Framework
Year: 2022
Subjects: subject: Social and Behavioral Sciences

description: No, no data have been collected for this study yet. Hypothesis We will investigate whether face masks increase the reliance on stereotypical associations between certain social groups and emotions when recognising emotions. Participants will be asked to categorise emotions when presented with various emotional faces with and without face masks. First, we will investigate whether introducing face masks increases the stereotype effect when classifying Dutch or Morrocan-Dutch faces as angry or sad. Bayesian Model: Following the prediction that decreasing the likelihood function increases reliance on the prior, we expect that introducing face masks increases the size of the stereotype effect. Hypotheses: 1a) Anger is classified faster for Moroccan-Dutch compared to Dutch faces, and sadness is classified faster for Dutch compared to Moroccan-Dutch faces (= ethnicity stereotype effect). 1b) The stereotype effect described in 1a) is larger for masked faces compared to unmasked faces. Biracial Faces Research: Masks decrease the size of the stereotype effect because they induce more deliberative thinking. We test the hypotheses of both models against another. Dependent variable Reaction times - measured by a speeded categorisation task. Error rates – number / proportion of incorrect trials. Conditions How many and which conditions will participants be assigned to? Participants will be presented with Moroccan-Dutch or Dutch faces displaying anger or sadness while wearing a mask or not wearing a mask. All 3 factors are hereby within subject and all participants are exposed to all unique combinations of trials. Analyses To analyse the latency data, we will conduct a specification curve analysis based on Simonsohn et al (2020; https://www.nature.com/articles/s41562-020-0912-z). We outline the approach we take below: 1. We will conduct a multiverse ANOVA 3-way (within subject factors: ModelEthnicity Moroccan/Dutch), Emotion (Sad/Angry), Mask (Masked/Unmasked). The multiverse consists of: Minimum: We will vary response time cut offs from 0ms to 300ms in steps of 50ms (7 levels). Data Transformation: To supplement the analyses based on the log-transformed data, we will perform analyses based on the raw and the latency-normalised data (3 levels). Data-based trimming: We will vary the number of median absolute deviations from the median from 1.0 to 3.0 or apply no data-based trimming (6 levels). The full combination of minimum value, data transformation, and data-based trimming gives rise to a multiverse of 126 analyses. 2. We will conduct permutation analysis by randomly shuffling the independent variables (i.e. randomly assigning the independent variables to break up any potential effects in the data while maintaining within-subject correlations, and related things) and repeat the multiverse analysis 500 times. 3. We will assess the 1) the proportion of permutations with an equally large or larger median effect size 2) the proportion of permutations with an equally large or larger proportion of significant specifications 4. We will conduct follow-up multiverse t-tests featuring identical multiverse specifications to follow up on the 2x2 (Emotion*ModelEthnicity) and the 2x2x2 interactions. The follow-up t-tests follow the same procedure as the ANOVA, with the exception that we will additionally calculate the proportion of permutations with an equally large or larger proportion of significant specifications with an effect in the expected direction (since Cohen´s d can take negative values, but partial eta squared cannot). 5) Finally, we will conduct a multiverse analysis identical to the one described in 4 to investigate the 2x2 (Emotion* ModelEthnicity)) interaction in the subset of unmasked faces, to most closely replicate the findings by Bijlstra et al., 2010. 6) Error rates: We will analyse error rates using a 2x2x2 repeated measures ANOVA (Emotion* ModelEthnicity)*MaskStatus). Outliers and Exclusions Outlier exclusion on a trial-level is systematically varied as part of the multiverse analysis. Participants will be excluded based on the following criteria: #1) They indicated that they did not participate sincerely in the seriousness check (https://pubmed.ncbi.nlm.nih.gov/23055170/) #2) They took longer or shorter than 3SD from the mean study duration #3) They failed both of the attention checks #4) They did not complete all trials #5) They did not finish within one hour. #6) Their number of errors was greater than 3SD from the mean number of errors across participants. Sample Size We will collect the data of 180 participants. Other No response Name Do face masks increase the stereotype effect in emotion recognition? Evidence from a specification curve analysis - II