References
- Adams, R., Rule, N., Franklin Jr., R., Wang, E., Stevenson, M., Yoshikawa, S., Nomura, M., Sato, W., Kestutis, K., & Ambady, N. (2010). Cross-cultural reading the mind in the eyes: An fMRI investigation. Journal of Cognitive Neuroscience, 22(1), 97–108. doi:https://doi.org/10.1162/jocn.2009.21187
- Baron-Cohen, S., Wheelwright, S., Hill, J., Raste, Y., & Plumb, I. (2001). The “reading the mind in the eyes” test revised version: A study with normal adults, and adults with asperger syndrome or high-functioning autism. Journal of Psychology and Psychiatry, 42(2), 241–251. https://doi.org/https://doi.org/10.1017/S0021963001006643
- Bates, D., Maechler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1–48. doi:https://doi.org/10.18637/jss.v067.i01
- Becker, D., Neel, R., Srinivasan, N., Neufeld, S., & Kumar, D. (2012). The vividness of happiness in dynamic facial displays of emotion. PLOS one, 7(1), e26551. doi:https://doi.org/10.1371/journal.pone.0026551
- Becker, D., & Srinivasan, N. (2014). The vividness of the happy face. Current Directions in Psychological Science, 23(3), 189–194. doi:https://doi.org/10.1177/0963721414533702
- Cassidy, B., Wiley, R., Sim, M., & Hugenberg, K. (in press). Decoding complex emotions and humanization show related face processing effects. Emotion. https://doi.org/https://doi.org/10.1037/emo0000990
- Demoulin, S., Leyens, J., Paladino, M., Rodriguez-Torres, R., Rodriguez-Perez, A., & Dovidio, J. (2004). Dimensions of “uniquely” and “non-uniquely” human emotions. Cognition and Emotion, 18(1), 71–96. doi:https://doi.org/10.1080/02699930244000444
- Devillez, H., Mollison, M., Hagen, S., Tanaka, J., Scott, L., & Curran, T. (2019). Color and spatial frequency differentially impact early stages of perceptual expertise training. Neuropsychologia, 122, 62–75. doi:https://doi.org/10.1016/j.neuropsychologia.2018.11.011
- Franklin Jr, R., & Zebrowitz, L. (2016). Aging-related changes in decoding negative complex mental states from faces. Experimental Aging Research, 42(5), 471–478. doi:https://doi.org/10.1080/0361073X.2016.1224667
- Goffaux, V., & Rossion, B. (2006). Faces are "spatial"--Holistic face perception is supported by low spatial frequencies.. Journal of Experimental Psychology: Human Perception and Performance, 32(4), 1023–1039. doi:https://doi.org/10.1037/0096-1523.32.4.1023
- Goren, D., & Wilson, H. (2006). Quantifying facial expression recognition across viewing conditions. Vision Research, 46(8-9), 1253–1262. doi:https://doi.org/10.1016/j.visres.2005.10.028
- Harkness, K., Sabbagh, M., Jacobson, J., Chowdrey, N., & Chen, T. (2005). Enhanced accuracy of mental state decoding in dysphoric college students. Cognition & Emotion, 19(7), 999–1025. doi:https://doi.org/10.1080/02699930541000110
- Jennings, B., Yu, Y., & Kingdom, F. (2017). The role of spatial frequency in emotional face classification. Attention, Perception, and Psychophysics, 79(6), 1573–1577. doi:https://doi.org/10.3758/s13414-017-1377-7
- Krumhuber, E., Lai, Y., Rosin, P., & Hugenberg, K. (2019). When facial expressions do and do not signal minds: The role of face inversion, expression dynamism, and emotion type.. Emotion, 19(4), 746–750. doi:https://doi.org/10.1037/emo0000475
- Kumar, D., & Srinivasan, N. (2011). Emotion perception is mediated by spatial frequency content. Emotion, 11(5), 1144–1151. doi:https://doi.org/10.1037/a0025453
- Kuznetsova, A., Brockhoff, P., & Christensen, R. (2017). Lmertest package: Tests in linear mixed effects models. Journal of Statistical Software, 82(13), 1–26. doi:https://doi.org/10.18637/jss.v082.i13
- Lenth, R. (2018). Emmeans: Estimated marginal means, aka least-squares means. R package version 1.4.7.
- Lockenhoff, C., & Carstensen, L. (2007). Aging, emotion, and health-related decision strategies: Motivational manipulations can reduce age differences. Psychology and Aging, 22(1), 134–146. doi:https://doi.org/10.1037/0882-7974.22.1.134
- Oosterhof, N., & Todorov, A. (2008). The functional basis of face evaluation. Proceedings of the National Academy of Sciences, 105(32), 11087–11092. doi:https://doi.org/10.1073/pnas.0805664105
- Royer, J., Blais, C., Charbonneau, I., Dery, K., Tardif, J., Duchaine, B., Gosselin, F., & Fiset, D. (2018). Greater reliance on the eye region predicts better face recognition ability. Cognition, 181, 12–20. doi:https://doi.org/10.1016/j.cognition.2018.08.004
- Schmidtmann, G., Jennings, B., Sandra, D., Pollock, J., & Gold, I. (2020). The McGill Face Database: Validation and insights into the recognition of facial expressions of complex mental states. Perception, 49(3), 310–329. doi:https://doi.org/10.1177/0301006620901671
- Shaver, P., Wu, S., & Schwartz, J. (1992). Cross-cultural similarities and differences in emotion and its representation. In M. Clark (Ed.), Review of personality and social Psychology (Vol. 13 (pp. 175–212). Sage Publications, Inc.
- Tracy, J., & Robins, R. (2004). Show your pride: Evidence for a discrete emotion expression. Psychological Science, 15(3), 194–197. doi:https://doi.org/10.1111/j.0956-7976.2004.01503008.x
- Vuilleumier, P., Armony, J., Driver, J., & Dolan, R. (2003). Distinct spatial frequency sensitivities for processing faces and emotional expressions. Nature Neuroscience, 6(6), 624–631. doi:https://doi.org/10.1038/nn1057
- Wegrzyn, M., Vogt, M., Kireclioglu, B., Schneider, J., & Kissler, J. (2017). Mapping the emotional face. How individual face parts contribute to successful emotion recognition. PLOS one, 12(5), e0177239. doi:https://doi.org/10.1371/journal.pone.0177239