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The aims are to conduct a secondary analysis of existing data using Bayesian Hierarchical Logistic Regression Model and a Bayesian Hierarchical Drift Diffusion modeling. The data were collected and described in Vikhanova, A., Mareschal, I., & Tibber, M. (2022). Emotion recognition bias depends on stimulus morphing strategy. Attention, Perception, & Psychophysics, 84(6), 2051–2059. https://doi.org/10.3758/s13414-022-02532-0 Participant sex and face sex will be included as variables in the regression model to establish whether my recently reported findings (large gender stereotype consistent effects in female participants) generalize to a new set of face stimuli. The broader goal is to understand how social category information (e.g., race and gender) influences the recognition of facial expressions that vary in emotional intensity. More specifically, I examine, using a neutral baseline, whether emotion recognition is facilitated for faces that belong to social categories that are stereo-typically associated with specific emotions. So, for example, the study permits a test of whether people are more likely to categorize male vs female neutral expressions as angry and moreover, whether such a bias is increased for female compared to male individuals. In a recent study in which I used the same stimuli, I found that female participants were more likely to categorize the faces of black (vs) white individuals as happy (vs angry). I will see to replicate this finding using Bayesian Hierarchical Logistic Regression Model and a Bayesian Hierarchical Drift Diffusion modeling |
