The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Moral foundations vignettes: a standardized stimulus database of scenarios based on moral foundations theory
Open accessAbstract
Research on the emotional, cognitive, and social determinants of moral judgment has surged in recent years. The development of moral foundations theory (MFT) has played an important role, demonstrating the breadth of morality. Moral psychology has responded by investigating how different domains of moral judgment are shaped by a variety of psychological factors. Yet, the discipline lacks a validated set of moral violations that span the moral domain, creating a barrier to investigating influences on judgment and how their neural bases might vary across the moral domain. In this paper, we aim to fill this gap by developing and validating a large set of moral foundations vignettes (MFVs). Each vignette depicts a behavior violating a particular moral foundation and not others. The vignettes are controlled on many dimensions including syntactic structure and complexity making them suitable for neuroimaging research. We demonstrate the validity of our vignettes by examining respondents’ classifications of moral violations, conducting exploratory and confirmatory factor analysis, and demonstrating the correspondence between the extracted factors and existing measures of the moral foundations. We expect that the MFVs will be beneficial for a wide variety of behavioral and neuroimaging investigations of moral cognition.
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Cites
The emotional dog and its rational tail: A social intuitionist approach to moral judgment.
Haidt, J. (2001) · Psychological Review · doi:10.1037/0033-295x.108.4.814 · Cited by 8,048
Liberals and conservatives rely on different sets of moral foundations.
Graham, J., Haidt, J., & Nosek, B. A. (2009) · Journal of Personality and Social Psychology · doi:10.1037/a0015141 · Cited by 4,560
Graham, J., Nosek, B. A., Haidt, J., Iyer, R., Koleva, S., & Ditto, P. H. (2011) · Journal of Personality and Social Psychology · doi:10.1037/a0021847 · Cited by 2,666
The Neural Bases of Cognitive Conflict and Control in Moral Judgment
Greene, J. D., Nystrom, L. E., Engell, A. D., Darley, J. M., & Cohen, J. D. (2004) · Neuron · doi:10.1016/j.neuron.2004.09.027 · Cited by 2,620
Berinsky, A. J., Margolis, M. F., & Sances, M. W. (2014) · American Journal of Political Science · doi:10.1111/ajps.12081 · Cited by 951
Hypnotic Disgust Makes Moral Judgments More Severe
Wheatley, T., & Haidt, J. (2005) · Psychological Science · doi:10.1111/j.1467-9280.2005.01614.x · Cited by 851
Moll, J., de Oliveira-Souza, R., Eslinger, P. J., Bramati, I. E., Mourão-Miranda, J., Andreiuolo, P. A., & Pessoa, L. (2002) · The Journal of Neuroscience · doi:10.1523/jneurosci.22-07-02730.2002 · Cited by 721
Tracing the threads: How five moral concerns (especially Purity) help explain culture war attitudes
Koleva, S. P., Graham, J., Iyer, R., Ditto, P. H., & Haidt, J. (2012) · Journal of Research in Personality · doi:10.1016/j.jrp.2012.01.006 · Cited by 682 · cited 1×
“In political science, MFT has been used to explain political attitudes and ideology (Graham, Haidt, & Nosek, 2009; Koleva et al., 2012; Weber & Federico, 2013; Kertzer et al., 2014), to classify moral rhetoric (Clifford & Jerit, 2013;”
Culture-sensitive neural substrates of human cognition: a transcultural neuroimaging approach
Han, S., & Northoff, G. (2008) · Nature Reviews Neuroscience · doi:10.1038/nrn2456 · Cited by 577
The Moral Affiliations of Disgust: A Functional MRI Study
Moll, J., de Oliveira-Souza, R., Moll, F. T., Ignácio, F. A., Bramati, I. E., Caparelli-Dáquer, E. M., & Eslinger, P. J. (2005) · Cognitive and Behavioral Neurology · doi:10.1097/01.wnn.0000152236.46475.a7 · Cited by 355
Neural correlates of regulating negative emotions related to moral violations
Harenski, C. L., & Hamann, S. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2005.09.034 · Cited by 272
The role of analytic thinking in moral judgements and values
Pennycook, G., Cheyne, J. A., Barr, N., Koehler, D. J., & Fugelsang, J. A. (2014) · Thinking & Reasoning · doi:10.1080/13546783.2013.865000 · Cited by 170
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Curry, O. S., Jones Chesters, M., & Van Lissa, C. J. (2019) · Journal of Research in Personality · doi:10.1016/j.jrp.2018.10.008 · Cited by 235
Building Ethics into Artificial Intelligence
Yu, H., Shen, Z., Miao, C., Leung, C., Lesser, V. R., & Yang, Q. (2018) · Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/779 · Cited by 210
Normative Evaluation of Large Language Models with Everyday Moral Dilemmas
Sachdeva, P., & van Nuenen, T. (2025) · Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3715275.3732044 · Cited by 26
Are Large Language Models Moral Hypocrites? A Study Based on Moral Foundations
Nunes, J. L., Almeida, G. F. C. F., Araujo, M. D., & Barbosa, S. D. J. (2024) · Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1609/aies.v7i1.31704 · Cited by 17
Metadata
- Authors
- Clifford, S., Iyengar, V., Cabeza, R., & Sinnott-Armstrong, W.
- Venue
- Behavior Research Methods
- Published
- 2015-01-13
- Volume
- 47
- Issue
- 4
- Pages
- 1178-1198
- ISSN
- 1554-351X (ISSN-L)
- 1554-3528
- OpenAlex
- W2070850637
- Cited by
- 351
- References
- 12
- FWCI
- 11.80
- Type
- journal article
- Language
- en
- Open access
- bronze
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