This thesis presents a small-scale multiverse analysis approach to explore the influence of stress on (dis)honest decision-making and aims to reproduce the findings and expand on the study by Speer...Show moreThis thesis presents a small-scale multiverse analysis approach to explore the influence of stress on (dis)honest decision-making and aims to reproduce the findings and expand on the study by Speer et al. (2023). The hypothesis implies that the effect of stress on decision-making is moderated by an individual’s moral default. The analyses used logistic regression and logistic mixed effects models, focusing on the baseline tendency to cheat (“moral default”) and how this tendency possibly changes in response to stress. The findings suggest that outcomes are sensitive to the choice of statistical model. Logistic regression models indicated significant interaction effects in six pathways, while logistic mixed effect models showed significance in only two out of 20. The analyses also examined the influence of dummy and effect coding, finding that while effect coding resulted in smaller standard errors, the different coding of the stress conditions did not significantly alter the overall conclusions and p-values. Different outlier exclusion methods emphasise the role of the researcher’s degrees of freedom, revealing how different decisions on the outlier rule can significantly influence the results. This study contributes to the understanding of the complex relationship between stress and moral decision-making.Show less
The present study experimentally investigates the effect of linguistic labeling on concept acquisition and identification of abstract objects during a categorization task. Participants (N = 56)...Show moreThe present study experimentally investigates the effect of linguistic labeling on concept acquisition and identification of abstract objects during a categorization task. Participants (N = 56) were randomly allocated to one of two groups and asked to perform a categorization task of previously learned objects. One group learned objects with linguistic labels, while the other did not. Objects included archetypes at the extremes of the color and shape spectrum and borderline ones along the color and shape spectrum. Participants' reaction time and classification accuracy were tested during training and testing. Our analysis revealed no difference in the speed of learning and reaction times between the two groups. Participants in the group without linguistic labeling showed greater classification accuracy for archetypes and borderline objects. In contrast to previous research, an impeding effect of labeling on concept acquisition and categorical knowledge was found. However, the findings must be viewed cautiously due to limitations of possible sampling errors and differences in the study design. Ultimately, previous findings seem less universal than assumed and cannot be replicated for a particular set of experimental designs using abstract objects. Further research, including neuroimaging studies, is needed to understand the brain mechanisms and pathways of categorization processes when impacted by language.Show less
The present study experimentally investigates the effect of linguistic labeling on concept acquisition and identification of abstract objects during a categorization task. Participants (N = 56)...Show moreThe present study experimentally investigates the effect of linguistic labeling on concept acquisition and identification of abstract objects during a categorization task. Participants (N = 56) were randomly allocated to one of two groups and asked to perform a categorization task of previously learned objects. One group learned objects with linguistic labels, while the other did not. Objects included archetypes at the extremes of the color and shape spectrum and borderline ones along the color and shape spectrum. Participants' reaction time and classification accuracy were tested during training and testing. Our analysis revealed no difference in the speed of learning and reaction times between the two groups. Participants in the group without linguistic labeling showed greater classification accuracy for archetypes and borderline objects. In contrast to previous research, an impeding effect of labeling on concept acquisition and categorical knowledge was found. However, the findings must be viewed cautiously due to limitations of possible sampling errors and differences in the study design. Ultimately, previous findings seem less universal than assumed and cannot be replicated for a particular set of experimental designs using abstract objects. Further research, including neuroimaging studies, is needed to understand the brain mechanisms and pathways of categorization processes when impacted by language.Show less
Self-regulation and risk taking are among the most thoroughly researched cognitive variables that show sex differences in school settings. For the past decades, girls have been academically...Show moreSelf-regulation and risk taking are among the most thoroughly researched cognitive variables that show sex differences in school settings. For the past decades, girls have been academically outperforming their male counterparts on a global scale. Differences in self-regulation and risk taking could explain the gap in school performance between boys and girls. Thorough research into sex differences in these variables may provide insight that could minimize this gap in school performance. This meta-analysis explores the link between sex differences in school performance and sex differences in self-regulation and risk taking. Additionally, the development of self-regulation and risk taking was examined for each sex. This meta-analysis compares the results of 22 articles for self-regulation and 19 articles for risk taking. Results showed that there is a significant difference in self-regulation with SMD= -.11, 95%-CI [-0.19, -0.03] p = 0.006, and in risk taking with SMD= .19, 95%-CI [0.09, 0.30] p < 0.000. A significant maturation effect was found for risk taking, with SMD= .19, 95%-CI= [0.09, 0.30], p < .000, but not for self-regulation.Show less
