The representational momentum effect and the reference dependence effect on the evaluation of dynamic happy expressions

doi:10.3724/SP.J.1041.2024.00029

Abstract

Abstract:

Most of the existing studies on facial expressions have used static facial picture materials, and dynamic expressions have been relatively understudied. However, people's expressions are often dynamic in life. In order to investigate the emotional processing characteristics of dynamic expressions, the present study examined the effects of the direction of change and the average summary representation on the three-dimensional evaluation of emotion in three experiments that included both dynamic and static happy expression picture materials. It was found that the arousal ratings were higher for the dynamic happy expression with a higher average summary representation. Faces that went from strong to weak had lower valence scores and higher dominance scores than static faces with the same intensity of expression in the previous frame. Indicative of the the representational momentum effect, faces that went from weak to strong had higher valence scores. Furthermore, the dynamic happy expressions that moved from strong to weak had a larger impact on perceived representational momentum than the dynamic happy expressions that moved from weak to strong. In addition, when assessing a facial expression, the perceiver will make a relative assessment based on the internal reference standard: a lower standard is associated with a higher score, and vice versa. This finding is consistent with the reference dependence effect on expression perception. These processing characteristics are used as a reminder to academics to consider the difference between dynamic and static expressions and to think about the impact of various materials when using facial expression data in the future.

Key words: dynamic expression, emotion three-dimensional, representational momentum, reference dependence effect

TIAN Yangyang, LI Dong, YAN Xiangbo, LI Zhao, CUI Qian, JIANG Zhongqing. (2024). The representational momentum effect and the reference dependence effect on the evaluation of dynamic happy expressions. Acta Psychologica Sinica, 56(1), 29-43.

Figures/Tables 12

References 44

[1]	Ambadar Z., Schooler J. W., & Cohn J. F. (2005). Deciphering the enigmatic face: The importance of facial dynamics in interpreting subtle facial expressions. Psychological Science, 16(5), 403-410. doi: 10.1111/j.0956-7976.2005.01548.x pmid: 15869701
[2]	Bradley M. M., & Lang P. J. (1994). Measuring emotion: The self-assessment manikin and the semantic differential. Journal of Behavior Therapy and Experimental Psychiatry, 25(1), 49-59. doi: 10.1016/0005-7916(94)90063-9 pmid: 7962581
[3]	Dong R. (2015). Facing orientation effect on representational momentum. Acta Psychologica Sinica, 47(2), 190-202. doi: 10.3724/SP.J.1041.2015.00190
[4]	Dozolme D., Prigent E., Yang Y. F., & Amorim M. A. (2018). The neuroelectric dynamics of the emotional anticipation of other people’s pain. PloS One, 13(8), e0200535. https://doi.org/10.1371/journal.pone.0200535
[5]	Dyjas O., Bausenhart K. M., & Ulrich R. (2012). Trial-by-trial updating of an internal reference in discrimination tasks: Evidence from effects of stimulus order and trial sequence. Attention, Perception and Psychophysics, 74( 8), 1819-1841.
[6]	Ekman P., Freisen W. V., & Ancoli S. (1980). Facial signs of emotional experience. Journal of Personality and Social Psychology, 39(6), 1125-1134. doi: 10.1037/h0077722 URL
[7]	Fang X., van Kleef G. A., & Sauter D. A. (2018). Person perception from changing emotional expressions: Primacy, recency, or averaging effect? Cognition and Emotion, 32(8), 1597-1610. doi: 10.1080/02699931.2018.1432476 URL
[8]	Finke R. A., & Freyd J. J. (1985). Transformations of visual memory induced by implied motions of pattern elements. Journal of Experimental Psychology: Learning, Memory and Cognition, 11( 4), 780-794.
[9]	Fiorentini C., & Viviani P. (2011). Is there a dynamic advantage for facial expressions? Journal of Vision, 11(3), 17. https://doi.org/10.1167/11.3.17
[10]	Freyd J. J. (1987). Dynamic mental representations. Psychological Review, 94(4), 427-438. pmid: 3317470
[11]	Goldman A. I. (2006). Simulating minds: The philosophy, psychology and neuroscience of mindreading. Oxford: Oxford University Press.
[12]	Gong X., Huang Y. X., Wang Y., & Luo Y. J. (2011). Revision of the Chinese facial affective picture system. Chinese Mental Health Journal, 25(1), 40-46.
[13]	Hareli S., David S., & Hess U. (2016). The role of emotion transition for the perception of social dominance and affiliation. Cognition and Emotion, 30(7), 1260-1270. doi: 10.1080/02699931.2015.1056107 URL
[14]	Hoffmann H., Kessler H., Eppel T., Rukavina S., & Traue H. C. (2010). Expression intensity, gender and facial emotion recognition: Women recognize only subtle facial emotions better than men. Acta Psychologica, 135(3), 278-283. doi: 10.1016/j.actpsy.2010.07.012 pmid: 20728864
[15]	Hubbard T. L. (2005). Representational momentum and related displacements in spatial memory: A review of the findings. Psychonomic Bulletin and Review, 12(5), 822-851. doi: 10.3758/BF03196775 URL
[16]	Hudson M., & Jellema T. (2011). Resolving ambiguous behavioral intentions by means of involuntary prioritization of gaze processing. Emotion, 11(3), 681-686. doi: 10.1037/a0023264 pmid: 21534666
[17]	Jellema T., Pecchinenda A., Palumbo L., & Tan E. G. (2011). Biases in the perception and affective valence of neutral facial expressions induced by the immediate perceptual history. Visual Cognition, 19(5), 616-634. doi: 10.1080/13506285.2011.569775 URL
[18]	Kahneman D., & Tversky A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-292. https://doi.org/10.2307/1914185 doi: 10.2307/1914185 URL
[19]	Korolkova O. A. (2018). The role of temporal inversion in the perception of realistic and morphed dynamic transitions between facial expressions. Vision Research, 143, 42-51. doi: S0042-6989(17)30250-X pmid: 29274357
[20]	Li H. J., Xu F. M., Xiang P., Kong S. X., & Meng Z. Z. (2013). Reference dependence based on prospect theory. Advances in Psychological Science, 21(2), 317-325. doi: 10.3724/SP.J.1042.2013.00317
[21]	Liu J. C., Ran G. M., Zang Q., Hu Y. H., Yu X. H., Zhang Q. Z., & Yang D. (2021). The development of Chinese undergraduate dynamic affective facial database. Chinese Journal of Applied Psychology. 27(3), 234-243.
[22]	Liu P., Lu Q., Zhang Z., Tang J., & Han B. (2021). Age-related differences in affective norms for Chinese words (AANC). Frontiers in Psychology, 12, 585666. https://doi.org/10.3389/fpsyg.2021.585666 doi: 10.3389/fpsyg.2021.585666 URL
[23]	Liu T. S., Ma H., Huang Y. X., Luo Y. J., Yan J., & Liu Z. W. (2006). Primary study of establishing an affective sound system of China. Chinese Mental Health Journal, 20(11), 709-712.
[24]	Liu W. J., Ding X. F., Cheng X. R., & Fan Z. (2022). Serial dependence effect: A novel “history effect”. Advances in Psychological Science, 30(10), 2228-2239. doi: 10.3724/SP.J.1042.2022.02228 URL
[25]	Mehrabian A., & Russell J. A. (1974). The basic emotional impact of environments. Perceptual and Motor Skills, 38(1), 283-301. pmid: 4815507
[26]	Mei G., Chen S., & Dong B. (2019). Working memory maintenance modulates serial dependence effects of perceived emotional expression. Frontiers in Psychology, 10, 1610. https://doi.org/10.3389/fpsyg.2019.01610 doi: 10.3389/fpsyg.2019.01610 URL pmid: 31354595
[27]	Navon D. (1977). Forest before trees: The precedence of global features in visual perception. Cognitive Psychology, 9(3), 353-383. doi: 10.1016/0010-0285(77)90012-3 URL
[28]	Öhman A., & Mineka S. (2001). Fears, phobias, and preparedness: Toward an evolved module of fear and fear learning. Psychological Review, 108(3), 483-522. pmid: 11488376
[29]	Palumbo L., & Jellema T. (2013). Beyond face value: Does involuntary emotional anticipation shape the perception of dynamic facial expressions? PloS One, 8(2), e56003. https://doi.org/10.1371/journal.pone.0056003 doi: 10.1371/journal.pone.0056003 URL
[30]	Parducci A., & Wedell D. H. (1986). The category effect with rating scales: Number of categories, number of stimuli, and method of presentation. Journal of Experimental Psychology: Human Perception and Performance, 12(4), 496-516. pmid: 2946806
[31]	Prigent E., Amorim M., & Oliveira A. M. (2018). Representational momentum in dynamic facial expressions is modulated by the level of expressed pain: Amplitude and direction effects. Attention, Perception, and Psychophysics, 80(1), 82-93.
[32]	Reinl M., & Bartels A. (2015). Perception of temporal asymmetries in dynamic facial expressions. Frontiers in Psychology, 6, 1107. https://doi.org/10.3389/fpsyg.2015.01107 doi: 10.3389/fpsyg.2015.01107 URL pmid: 26300807
[33]	Sato W., Kochiyama T., & Yoshikawa S. (2010). Amygdala activity in response to forward versus backward dynamic facial expressions. Brain Research, 1315, 92-99. doi: 10.1016/j.brainres.2009.12.003 pmid: 20025856
[34]	Sato W., & Yoshikawa S. (2004). The dynamic aspects of emotional facial expressions. Cognition and Emotion, 18(5), 701-710. doi: 10.1080/02699930341000176 URL
[35]	Sato W., & Yoshikawa S. (2007). Enhanced experience of emotional arousal in response to dynamic facial expressions. Journal of Nonverbal Behavior, 31(2), 119-135. doi: 10.1007/s10919-007-0025-7 URL
[36]	Slepian M. L., & Carr E. W. (2019). Facial expressions of authenticity: Emotion variability increases judgments of trustworthiness and leadership. Cognition, 183, 82-98. doi: S0010-0277(18)30267-1 pmid: 30445313
[37]	Ueda Y., Nagoya K., Yoshikawa S., & Nomura M. (2017). Forming facial expressions influences assessment of others' dominance but not trustworthiness. Frontiers in Psychology, 8, 2097. https://doi.org/10.3389/fpsyg.2017.02097
[38]	Warriner A. B., Kuperman V., & Brysbaert M. (2013). Norms of valence, arousal, and dominance for 13, 915 English lemmas. Behavior Research Methods, 45(4), 1191-1207. doi: 10.3758/s13428-012-0314-x pmid: 23404613
[39]	Wedell D. H., Parducci A., & Geiselman R. E. (1987). A formal analysis of ratings of physical attractiveness: Successive contrast and simultaneous assimilation. Journal of Experimental Social Psychology, 23(3), 230-249. doi: 10.1016/0022-1031(87)90034-5 URL
[40]	Wen F. F., Ke W. L., He S. F., Zuo B., Li L. X., Ma S. H., & Wang J. (2022). The effect of group identity shifting on impression updating in older adults: The mediating role of common ingroup identity. Acta Psychologica Sinica, 54(9), 1059-1075. doi: 10.3724/SP.J.1041.2022.01059
[41]	Ying H., & Xu H. (2017). Adaptation reveals that facial expression averaging occurs during rapid serial presentation. Journal of Vision, 17(1), 1-19.
[42]	Yoshikawa S., & Sato W. (2008). Dynamic facial expressions of emotion induce representational momentum. Cognitive, Affective and Behavioral Neuroscience, 8(1), 25-31.
[43]	Zhai K., & Zhang Z. J. (2011). Theoretical models of representation momentum. Advances in Psychological Science, 19(4), 528-536.
[44]	Zhang Q., Yin T. Z., & Ran G. M. (2015). Psychological and neural mechanisms for the superiority effect of dynamic facial expressions. Advances in Psychological Science, 23(9), 1514-1522. doi: 10.3724/SP.J.1042.2015.01514

		The average summary representation
		75% happy	25% happy
direction of intensity change	From strong toweak	100% happy – 50% happy	50% happy – neutral
direction of intensity change	From weak to strong	50% happy – 100% happy	Neutral – 50% happy

		The average summary representation
		75% happy	25% happy
direction of intensity change	From strong toweak	100% happy – 50% happy	50% happy – neutral
direction of intensity change	From weak to strong	50% happy – 100% happy	Neutral – 50% happy

Emotional dimension	Experimental conditions	M	t	df	p
Valence	100% Happy – 50% happy	3.84	–1.24	31	0.226
	50% Happy – neutral	3.08	–7.17	31	< 0.001
	50% Happy – 100% happy	5.40	14.12	31	< 0.001
	Neutral – 50% happy	4.87	8.06	31	< 0.001
Arousal	100% Happy – 50% happy	3.97	–0.25	31	0.803
	50% Happy – neutral	3.63	–1.72	31	0.096
	50% Happy – 100% happy	4.30	1.59	31	0.122
	Neutral – 50% happy	3.93	–0.51	31	0.616
Dominance	100% Happy – 50% happy	4.24	2.28	31	0.029
	50% Happy – neutral	4.76	6.10	31	< 0.001
	50% Happy – 100% happy	3.22	–5.38	31	< 0.001
	Neutral – 50% happy	3.49	–5.15	31	< 0.001

Emotional dimension	Experimental conditions	M	t	df	p
Valence	100% Happy – 50% happy	3.84	–1.24	31	0.226
	50% Happy – neutral	3.08	–7.17	31	< 0.001
	50% Happy – 100% happy	5.40	14.12	31	< 0.001
	Neutral – 50% happy	4.87	8.06	31	< 0.001
Arousal	100% Happy – 50% happy	3.97	–0.25	31	0.803
	50% Happy – neutral	3.63	–1.72	31	0.096
	50% Happy – 100% happy	4.30	1.59	31	0.122
	Neutral – 50% happy	3.93	–0.51	31	0.616
Dominance	100% Happy – 50% happy	4.24	2.28	31	0.029
	50% Happy – neutral	4.76	6.10	31	< 0.001
	50% Happy – 100% happy	3.22	–5.38	31	< 0.001
	Neutral – 50% happy	3.49	–5.15	31	< 0.001

		The intensity of the last frame expression
		50% Happy	100% Happy
Expression state	Dynamic	100% Happy – 50% happy	50% Happy – 100% happy
Expression state	Static	Static 50% happy	Static 100% happy