Functional connectivities of the right temporoparietal junction and moral network predict social framing effect: Evidence from resting-state fMRI

doi:10.3724/SP.J.1041.2021.00055

Abstract

Abstract:

As an important cognitive bias, the framing effect shows that individuals' decision preferences are sensitive to the verbal description (i.e., frame) of options. The social framing effect could be distinguished from the non-social one according to whether the decision would influence others. The psychological mechanism of the non-social framing effect (e.g., Gain/Loss framing effect) and that of the social one are essentially different. When people make non-social decisions, frames affect their judgment of which option is more beneficial or less risky. When people make social decisions, frames affect their preferences through other-regarding concerns and social norms.
In the present study, a new paradigm was developed to induce the social framing effect. We asked participants to make a tradeoff between economic benefits and the feelings of others; when participants showed a stronger preference for income maximization, the probability for their partners to receive a painful electrical shock would increase proportionally. This decision was described as either a “harm” to, or simply “not helping” other persons in two frame conditions. 30 participants (age: 20.58 ± 1.91 years old) were enrolled in the experiment and 24 of them were included in the final analysis. The resting-state functional magnetic resonance (rs-fMRI) data was acquired using the Echo Planar Imaging (EPI) sequence from a 3-T Siemens scanner. This scanning acquired 180 volumes with TR = 2 s (lasting 6 min). Rs-fMRI data were processed and analyzed using the DPABI and RESTplus toolbox to calculate the amplitude of low-Frequency Fluctuation (ALFF) and Functional Connectivity (FC).
At the behavioral level, we found that participants made more prosocial decisions in the Harm frame compared to the Help frame condition, resulting in a significant social framing effect. For the resting fMRI analysis, we first run a whole-brain correlation analysis between ALFF and the behavioral index and found the ALFF of the right temporoparietal junction (rTPJ) could significantly predict the behavioral index of the social framing effect. Considering the observed social framing effect would result from different levels of moral conflict between Harm and Help frames, we predicted that it would be closely related to the moral network. Therefore, we further localized 12 seeds from a new, meta-analysis of functional MRI studies for moral processing. Seed-based FC analysis showed that the functional connectivity between the medial prefrontal cortex and the caudate was significantly associated with the behavioral index of the social framing effect. Multivariate machine learning-based regression analysis further confirmed these results, suggesting the importance of rTPJ and moral network for the observed social framing effect.
The present study is based on a novel experimental paradigm, using resting functional imaging techniques to explore the brain mechanism of the social framing effect. We found that the ALFF value of the right TPJ and the strength of the functional connectivity value between the medial prefrontal lobe and the caudate within a moral network can effectively predict the social framing effect. This study is the very first one to explore the extent to which individual social decision-making can be influenced by verbal description and its underlying neural mechanisms, which shed light on the further exploration of individual differences in social decision-making.

Key words: social framing effect, resting-state fMRI, functional connectivity, right temporoparietal junction, moral network

CUI Fang, YANG Jiamiao, GU Ruolei, LIU Jie. (2021). Functional connectivities of the right temporoparietal junction and moral network predict social framing effect: Evidence from resting-state fMRI. Acta Psychologica Sinica, 53(1), 55-66.

Figures/Tables 5

Figure 1. Experimental design (a) harm frame and help frame; (B) trial structure.

Table 1 ROI information

Source	Number	x	y	z	Hemispheres	Cerebral area
Voxel level analysis	ROI 1	39	-54	6	right	Temporoparietal junction (TPJ)
Moral Brain Network (meta-analysis)	ROI 2	-6	44	20	left	Medial prefrontal lobe (MPFC1)
	ROI 3	-44	-56	18	left	Superior Temporal Gyrus (STG1)
	ROI 4	-2	-56	26	left	Cingulate gyrus (CG)
	ROI 5	50	6	-20	right	Superior Temporal Gyrus (STG2)
	ROI 6	2	54	2	right	Medial prefrontal lobe (MPFC2)
	ROI 7	-4	48	-6	left	Medial prefrontal lobe (MPFC3)
	ROI 8	-44	-64	20	left	Middle Temporal Gyrus (MTG1)
	ROI 9	-2	-60	30	left	Precuneus
	ROI 10	2	44	36	right	Medial prefrontal lobe (MPFC4)
	ROI 11	44	-60	24	right	Middle Temporal Gyrus (MTG)
	ROI 12	36	28	-12	right	Inferior Frontal Gyrus (IFG)
	ROI 13	-12	4	12	left	Caudate nucleus

Figure 2. Behavior results (a) the helping rates under the harm and help frame; (b) the framing effect scores of all participants.

Figure 3. (a) voxel-level analysis results; (B) the functional connection values between the left MPFC1, MPFC3 and caudate nucleus were significantly correlated with the social framing effect scores (different colored spheres represent the corresponding ROI; black arrows represent the functional connectivities; only the connectivity with absolute weight ranking in the top three were shown).

Figure 4. Functional connectivity between the MPFC 3 and Caudate correlated with social framing effect scores.

References 61

[1]	Aupperle, R. L., Melrose, A. J., Francisco, A., Paulus, M. P., & Stein, M. B. (2015). Neural substrates of approach-avoidance conflict decision-making. Human Brain Mapping, 36(2), 449-462.
[2]	Aupperle, R. L., Sullivan, S., Melrose, A. J., Paulus, M. P., & Stein, M. B. (2011). A reverse translational approach to quantify approach-avoidance conflict in humans. Behavioural Brain Research, 225(2), 455-463.
[3]	Berns, G. S., Bell, E., Capra, C. M., Prietula, M. J., Moore, S., Anderson, B., ... Atran, S. (2012). The price of your soul: Neural evidence for the non-utilitarian representation of sacred values. Philosophical Transactions of the Royal Society B: Biological Sciences, 367(1589), 754-762.
[4]	Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995). Functional connectivity in the motor cortex of resting human brain using echo‐planar MRI. Magnetic Resonance in Medicine, 34(4), 537-541.
[5]	Brosch, T., Coppin, G., Schwartz, S., & Sander, D. (2012). The importance of actions and the worth of an object: Dissociable neural systems representing core value and economic value. Social Cognitive and Affective Neuroscience, 7(5), 497-505.
[6]	Bzdok, D., Schilbach, L., Vogeley, K., Schneider, K., Laird, A. R., Langner, R., & Eickhoff, S. B. (2012). Parsing the neural correlates of moral cognition: ALE meta-analysis on morality, theory of mind, and empathy. Brain Structure Function, 217(4), 783-796.
[7]	Cole, M. W., Ito, T., Bassett, D. S., & Schultz, D. H. (2016). Activity flow over resting-state networks shapes cognitive task activations. Nature Neuroscience, 19(12), 1718-1726.
[8]	Crockett, M. J., Kurth-Nelson, Z., Siegel, J. Z., Dayan, P., & Dolan, R. J. (2014). Harm to others outweighs harm to self in moral decision making. Proceedings of the National Academy of Sciences of the United States of America, 111(48), 17320-17325.
[9]	Cui, Z., & Gong, G. (2018). The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features. Neuroimage, 178, 622-637.
[10]	De Martino, B., Kumaran, D., Seymour, B., & Dolan, R. J. (2006). Frames, biases, and rational decision-making in the human brain. Science, 313(5787), 684-687.
[11]	De Vos, F., Koini, M., Schouten, T. M., Seiler, S., van der Grond, J., Lechner, A., ... Rombouts, S. A. R. B. (2018). A comprehensive analysis of resting state fMRI measures to classify individual patients with Alzheimer's disease. Neuroimage, 167, 62-72.
[12]	Decety, J., & Porges, E. C. (2011). Imagining being the agent of actions that carry different moral consequences: An fMRI study. Neuropsychologia, 49(11), 2994-3001.
[13]	Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., van Essen, D. C., & Raichle, M. E. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences of the United States of America, 102(27), 9673-9678. URL pmid: 15976020
[14]	Gan, T., Shi, R., Liu, C., & Luo, Y. J. (2018). Cathodal transcranial direct current stimulation on the right temporo-parietal junction modulates the helpful intention processing. Acta Psychologica Sinica, 45(9), 1004-1014. doi: 10.3724/SP.J.1041.2013.01004 URL
[15]	Garrigan, B., Adlam, A. L. R., & Langdon, P. E. (2016). The neural correlates of moral decision-making: A systematic review and meta-analysis of moral evaluations and response decision judgements. Brain and Cognition, 108, 88-97. doi: 10.1016/j.bandc.2016.07.007 URL
[16]	Gotts, S. J., Saad, Z. S., Jo, H. J., Wallace, G. L., Cox, R. W., & Martin, A. (2013). The perils of global signal regression for group comparisons: A case study of Autism Spectrum Disorders. Frontiers in Human Neuroscience, 7, 356.
[17]	Hare, T. A., Camerer, C. F., & Rangel, A. (2009). Self-control in decision-making involves modulation of the vmPFC valuation system. Science, 324(5927), 646-648.
[18]	Hoptman, M. J., Zuo, X. N., Butler, P. D., Javitt, D. C., D'Angelo, D., Mauro, C. J., & Milham, M. P. (2010). Amplitude of low-frequency oscillations in schizophrenia: A resting state fMRI study. Schizophrenia Research, 117(1), 13-20. URL pmid: 19854028
[19]	Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage, 17(2), 825-841.
[20]	Jia, X.-Z., Wang, J., Sun, H.-Y., Zhang, H., Liao, W., Wang, Z., ... Zang, Y.-F. (2019). RESTplus: An improved toolkit for resting-state functional magnetic resonance imaging data processing. Science Bulletin, 64(14), 953-954.
[21]	Jiang, Q., Hou, L., Qiu, J., Li, C., & Wang, H. (2018). The relationship between the caudate nucleus-orbitomedial prefrontal cortex connectivity and reactive aggression: A resting-state fMRI study. Acta Psychologica Sinica, 50(6), 655-666.
[22]	Kaanders, P., & Hunt, L. T. (2018). Neuroscience: Intracranial recordings of value. Current Biology, 28(18), 1106-1108.
[23]	Koster-Hale, J., Saxe, R., Dungan, J., & Young, L. L. (2013). Decoding moral judgments from neural representations of intentions. Proceedings of the National Academy of Sciences of the United States of America, 110(14), 5648-5653.
[24]	Krall, S. C., Rottschy, C., Oberwelland, E., Bzdok, D., Fox, P. T., Eickhoff, S. B., ... Konrad, K. (2015). The role of the right temporoparietal junction in attention and social interaction as revealed by ALE meta-analysis. Brain Structure Function, 220, 587-604.
[25]	Lewis, C. M., Baldassarre, A., Committeri, G., Romani, G. L., & Corbetta, M. (2009). Learning sculpts the spontaneous activity of the resting human brain. Proceedings of the National Academy of Sciences of the United States of America, 106(41), 17558-17563.
[26]	Liberman, V., Samuels, S. M., & Ross, L. (2004). The Name of the game: Predictive power of reputations versus situational labels in determining prisoner’s dilemma game moves. Personality & Social Psychology Bulletin, 30(9), 1175-1185.
[27]	Liu, J., Gu, R., Liao, C., Lu, J., Fang, Y., Xu, P., ... Cui, F. (2020). The Neural mechanism of the social framing effect: Evidence from fMRI and tDCS studies. Journal of Neuroscience, 40(18), 3646-3656. doi: 10.1523/JNEUROSCI.1385-19.2020 URL
[28]	Liu, J., Liao, C., Lu, J., Luo, Y.-J., & Cui, F. (2019). Moral contagion: Devaluation effect of immorality on hypothetical judgments of economic value. Human Brain Mapping, 40(7), 2076-2088. URL pmid: 30624839
[29]	Liu, J., Xia, M., Dai, Z., Wang, X., Liao, X., Bi, Y., & Yong, H. (2017). Intrinsic brain hub connectivity underlies individual differences in spatial working memory. Cerebral Cortex, 27(12), 5496-5508. URL pmid: 28334075
[30]	Liu, J., Yuan, B., Luo, Y.-J., & Cui, F. (2020). Intrinsic functional connectivity of medial prefrontal cortex predicts the individual moral bias in economic valuation partially through the moral sensitivity trait. Brain Imaging and Behavior, 14(5), 2024-2036. doi: 10.1007/s11682-019-00152-1 URL
[31]	Luo, J., Ye, H., Zheng, H., Jia, Y., Chen, Z., & Huang, D. (2017). Modulating the activities of right and left temporo-parietal junction influences the capability of moral intention processing: A transcranial direct current stimulation study. Acta Psychologica Sinica, 49(2), 228-240. doi: 10.3724/SP.J.1041.2017.00228 URL
[32]	Mennes, M., Kelly, C., Zuo, X-N., Di Martino, A., Biswal, B.B., Castellanos, F.X., & Milham, M.P. (2010). Inter-individual differences in resting-state functional connectivity predict task-induced BOLD activity. Neuroimage, 50(4), 1690-1701. doi: 10.1016/j.neuroimage.2010.01.002 URL
[33]	Mennes, M., Zuo, X-N., Kelly, C., Di Martino, A., Zang, Y. F., Biswal, B., … Milham, M. P. (2011). Linking inter-individual differences in neural activation and behavior to intrinsic brain dynamics. Neuroimage, 54(4), 2950-2959. doi: 10.1016/j.neuroimage.2010.10.046 URL
[34]	Morishima, Y., Schunk, D., Bruhin, A., Ruff, C. C., & Fehr, E. (2012). Linking brain structure and activation in temporoparietal junction to explain the neurobiology of human altruism. Neuron, 75(1), 73-79.
[35]	Murphy, K., & Fox, M. D. (2017). Towards a consensus regarding global signal regression for resting state functional connectivity MRI. Neuroimage, 154, 169-173. doi: 10.1016/j.neuroimage.2016.11.052 URL
[36]	Nostro, A. D., Müller, V. I., Varikuti, D. P., Pl?Schke, R. N., Hoffstaedter, F., Langner, R., ... Eickhoff, S. B. (2018). Predicting personality from network-based resting-state functional connectivity. Brain Structure & Function. 223, 2699-2719
[37]	Posner, J., Park, C., & Wang, Z. (2014). Connecting the dots: A review of resting connectivity MRI studies in attention-deficit/hyperactivity disorder. Neuropsychology Review, 24(1), 3-15. doi: 10.1007/s11065-014-9251-z URL
[38]	Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012). Spurious but systematic correlations in functional connectivity MRI networks arise from participant motion. Neuroimage, 59(3), 2142-2154. doi: 10.1016/j.neuroimage.2011.10.018 URL
[39]	Rege, M., & Telle, K. (2004). The impact of social approval and framing on cooperation in public good situations. Journal of Public Economics, 88(7-8), 1625-1644. doi: 10.1016/S0047-2727(03)00021-5 URL
[40]	Rolls, E. T., McCabe, C., & Redoute, J. (2008). Expected value, reward outcome, and temporal difference error representations in a probabilistic decision task. Cerebral Cortex, 18(3), 652-663. URL pmid: 17586603
[41]	Shenhav, A., & Greene, J. D. (2010). Moral judgments recruit domain-general valuation mechanisms to integrate representations of probability and magnitude. Neuron, 67(4), 667-677. doi: 10.1016/j.neuron.2010.07.020 URL
[42]	Shenhav, A., & Greene, J. D. (2014). Integrative moral judgment: Dissociating the roles of the amygdala and ventromedial prefrontal cortex. Journal of Neuroscience, 34(13), 4741-4749. doi: 10.1523/JNEUROSCI.3390-13.2014 URL
[43]	Takeuchi, H., Taki, Y., Hashizume, H., Sassa, Y., Nagase, T., Nouchi, R., & Kawashima, R. (2012). The association between resting functional connectivity and creativity. Cerebral Cortex, 22(12), 2921-2929. doi: 10.1093/cercor/bhr371 URL
[44]	Taubert, M., Lohmann, G., Margulies, D. S., Villringer, A., & Ragert, P. (2011). Long-term effects of motor training on resting-state networks and underlying brain structure. Neuroimage, 57(4), 1492-1498. doi: 10.1016/j.neuroimage.2011.05.078 URL
[45]	Tipping, M. E. (1999). The Relevance Vector Machine. Paper presented at the Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29 - December 4, 1999].
[46]	Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211(4481), 453-458. URL pmid: 7455683
[47]	Wang, L., Lu, X., Gu, R., Zhu, R., Xu, R., Broster, L. S., & Feng, C. (2017). Neural substrates of context‐ and person‐dependent altruistic punishment. Human Brain Mapping. 38(11), 5535-5550.
[48]	Wang, Z., Wang, Y., Sweeney, J. A., Gong, Q., Lui, S., & Mosconi, M. W. (2019). Resting-state brain network dysfunctions associated with visuomotor impairments in autism spectrum disorder. Frontiers in Integrative Neuroscience, 13, 17. URL pmid: 31213995
[49]	Wei, T., Liang, X., He, Y., Zang, Y., Han, Z., Caramazza, A., & Bi, Y. (2012). Predicting conceptual processing capacity from spontaneous neuronal activity of the left middle temporal gyrus. Journal of Neuroscience, 32(2), 481-489. doi: 10.1523/JNEUROSCI.1953-11.2012 URL
[50]	Xu, P., Gu, R., Broster, L. S., Wu, R., van Dam, N. T., Jiang, Y., ... Luo, Y. J. (2013). Neural basis of emotional decision making in trait anxiety. Journal of Neuroscience, 33(47), 18641-18653. URL pmid: 24259585
[51]	Yan, C. G., Wang, X. D., Zuo, X. N., & Zang, Y. F. (2016). DPABI: Data processing & analysis for (resting-state) brain imaging. Neuroinformatics, 14(3), 339-351. URL pmid: 27075850
[52]	Yoder, K. J., & Decety, J. (2014). The Good, the bad, and the just: Justice sensitivity predicts neural response during moral evaluation of actions performed by others. Journal of Neuroscience, 34(12), 4161-4166. doi: 10.1523/JNEUROSCI.4648-13.2014 URL
[53]	Yoder, K. J., Harenski, C., Kiehl, K. A., & Decety, J. (2015). Neural networks underlying implicit and explicit moral evaluations in psychopathy. Translational Psychiatry, 5(8), e625. doi: 10.1038/tp.2015.117 URL
[54]	Young, L., Cushman, F., Hauser, M., & Saxe, R. (2007). The Neural basis of the interaction between theory of mind and moral judgment. Proceedings of the National Academy of Sciences of the United States of America, 104(20), 8235-8240.
[55]	Young, L., & Dungan, J. (2012). Where in the brain is morality? Everywhere and maybe nowhere. Social Neuroscience, 7(1), 1-10. doi: 10.1080/17470919.2011.569146 URL
[56]	Young, L., & Koenigs, M. (2007). Investigating emotion in moral cognition: A review of evidence from functional neuroimaging and neuropsychology. British Medical Bulletin, 84(1), 69-79. doi: 10.1093/bmb/ldm031 URL
[57]	Young, L., & Saxe, R. (2008). The neural basis of belief encoding and integration in moral judgment. Neuroimage, 40(4), 1912-1920. doi: 10.1016/j.neuroimage.2008.01.057 URL
[58]	Young, L., Scholz, J., & Saxe, R. (2011). Neural evidence for “intuitive prosecution”: The use of mental state information for negative moral verdicts. Social Neuroscience, 6(3), 302-315. doi: 10.1080/17470919.2010.529712 URL
[59]	Zang, Y. F., He, Y., Zhu, C.-Z., Cao, Q.-J., Sui, M.-Q., Liang., M., ... Wang, Y. F. (2007). Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain and Development, 29(2), 83-91. URL pmid: 16919409
[60]	Zeng, L.-L., Shen, H., Liu, L., Wang, L., Li, B., Fang, P., ... Hu, D. (2012). Identifying major depression using whole-brain functional connectivity: A multivariate pattern analysis. Brain: A Journal of Neurology, 135(5), 1498-1507. doi: 10.1093/brain/aws059 URL
[61]	Zou, Q. H., Zhu, C.-Z., Yang, Y., Zuo, X.-N., Long, X.-Y., Cao, Q.-J., ... Zang, Y.F. (2008). An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF. Journal of Neuroscience Methods, 172(1), 137-141. doi: 10.1016/j.jneumeth.2008.04.012 URL