How Dynamic Brain Networks Tune Social Behavior in Real Time
2018, Current Directions in Psychological Science
https://doi.org/10.1177/0963721418773362Abstract
During social interaction, the brain has the enormous task of interpreting signals that are fleeting, subtle, contextual, abstract, and often ambiguous. Despite the signal complexity, the human brain has evolved to be highly successful in the social landscape. Here, we propose that the human brain makes sense of noisy dynamic signals through accumulation, integration, and prediction, resulting in a coherent representation of the social world. We propose that successful social interaction is critically dependent on a core set of highly connected hubs that dynamically accumulate and integrate complex social information and, in doing so, facilitate social tuning during moment-to-moment social discourse. Successful interactions, therefore, require adaptive flexibility generated by neural circuits composed of highly integrated hubs that coordinate context appropriate responses. Adaptive properties of the neural substrate, including predictive and adaptive coding, and neural reuse, along with perceptual, inferential, and motivational inputs, provide the ingredients for pliable, hierarchical predictive models that guide our social interactions.
References (48)
- Anderson, M. L. (2010). Neural reuse: A fundamental orga- nizational principle of the brain [Target article and dis- cussion]. Behavioral and Brain Sciences, 33, 245-313. doi:10.1017/S0140525X10000853
- Bassett, D. S., & Mattar, M. G. (2017). A network neuroscience of human learning: Potential to inform quantitative theo- ries of brain and behavior. Trends in Cognitive Sciences, 21, 250-264.
- Bassett, D. S., & Sporns, O. (2017). Network neuroscience. Nature Neuroscience, 20, 353-364. doi:10.1038/nn.4502
- Bassett, D. S., Wymbs, N. F., Porter, M. A., Mucha, P. J., Carlson, J. M., & Grafton, S. T. (2011). Dynamic recon- figuration of human brain networks during learning. Proceedings of the National Academy of Sciences, USA, 108, 7641-7646. doi:10.1073/pnas.1018985108
- Bassett, D. S., Wymbs, N. F., Rombach, M. P., Porter, M. A., Mucha, P. J., & Grafton, S. T. (2013). Task-based core- periphery organization of human brain dynamics. PLOS Computational Biology, 9(9), Article e1003171. doi:10 .1371/journal.pcbi.1003171
- Betzel, R. F., Satterthwaite, T. D., Gold, J. I., & Bassett, D. S. (2017). Positive affect, surprise, and fatigue are correlates of network flexibility. Scientific Reports, 7, Article 520. doi:10.1038/s41598-017-00425-z
- Braun, U., Schafer, A., Bassett, D. S., Rausch, F., Schweiger, J. I., Bilek, E., . . . Tost, H. (2016). Dynamic brain net- work reconfiguration as a potential schizophrenia genetic risk mechanism modulated by NMDA receptor function. Proceedings of the National Academy of Sciences, USA, 113, 12568-12573. doi:10.1073/pnas.1608819113
- Braun, U., Schafer, A., Walter, H., Erk, S., Romanczuk- Seiferth, N., Haddad, L., . . . Bassett, D. S. (2015). Dynamic reconfiguration of frontal brain networks dur- ing executive cognition in humans. Proceedings of the National Academy of Sciences, USA, 112, 11678-11683. doi:10.1073/pnas.1422487112
- Bullmore, E. T., & Bassett, D. S. (2011). Brain graphs: Graphical models of the human brain connectome. Annual Review Clinical Psychology, 7, 113-140. doi:10.1146/annurev- clinpsy-040510-143934
- Carter, R. M., Bowling, D. L., Reeck, C., & Huettel, S. A. (2012). A distinct role of the temporal-parietal junction in predicting socially guided decisions. Science, 337, 109-111. doi:10.1126/science.1219681
- Chai, L. R., Mattar, M. G., Blank, I. A., Fedorenko, E., & Bassett, D. S. (2016). Functional network dynamics of the language system. Cerebral Cortex, 26, 4148-4159. doi:10.1093/cercor/bhw238
- Chen, J., Leong, Y. C., Honey, C. J., Yong, C. H., Norman, K. A., & Hasson, U. (2017). Shared memories reveal shared structure in neural activity across individuals. Nature Neuroscience, 20, 115-125. doi:10.1038/nn.4450
- Cole, M. W., Reynolds, J. R., Power, J. D., Repovs, G., Anticevic, A., & Braver, T. S. (2013). Multi-task connectiv- ity reveals flexible hubs for adaptive task control. Nature Neuroscience, 16, 1348-1355. doi:10.1038/nn.3470
- Corbetta, M., Patel, G., & Shulman, G. L. (2008). The reori- enting system of the human brain: From environment to theory of mind. Neuron, 58, 306-324. doi:10.1016/j .neuron.2008.04.017
- Dajani, D. R., & Uddin, L. Q. (2015). Demystifying cogni- tive flexibility: Implications for clinical and developmen- tal neuroscience. Trends in Neurosciences, 38, 571-578. doi:10.1016/j.tins.2015.07.003
- Diaconescu, A. O., Mathys, C., Weber, L. A. E., Kasper, L., Mauer, J., & Stephan, K. E. (2017). Hierarchical prediction errors in midbrain and septum during social learning. Social Cognitive & Affective Neuroscience, 12, 618-634. doi:10.1093/scan/nsw171
- Dunne, S., D'Souza, A., & O'Doherty, J. P. (2016). The involve- ment of model-based but not model-free learning signals during observational reward learning in the absence of choice. Journal of Neurophysiology, 115, 3195-3203. doi:10.1152/jn.00046.2016
- Echterhoff, G., Higgins, E. T., & Levine, J. M. (2009). Shared reality: Experiencing commonality with others' inner states about the world. Perspectives on Psychological Science, 4, 496-521. doi:10.1111/j.1745-6924.2009.01161.x
- Fang, Z., Zhu, S., Gillihan, S. J., Korczykowski, M., Detre, J. A., & Rao, H. (2013). Serotonin transporter genotype modulates functional connectivity between amygdala and PCC/PCu during mood recovery. Frontiers in Human Neuroscience, 7, Article 704. doi:10.3389/fnhum.2013.00704
- Fedorenko, E., & Thompson-Schill, S. L. (2014). Reworking the language network. Trends in Cognitive Sciences, 18, 120-126. doi:10.1016/j.tics.2013.12.006
- Fingelkurts, A. A., & Fingelurts, A. A. (2017). Information flow in the brain: Ordered sequences of metastable states. Information, 8(1), Article 22. doi:10.3390/info8010022
- Fiske, S. T., Cuddy, A. J. C., & Glick, P. (2007). Universal dimen- sions of social cognition: Warmth and competence. Trends in Cognitive Sciences, 11, 77-83. doi:10.1016/j.tics.2006 .11.005
- Fransson, P., & Marrelec, G. (2008). The precuneus/posterior cingulate cortex plays a pivotal role in the default mode network: Evidence from a partial correlation network analysis. NeuroImage, 42, 1178-1184. doi:10.1016/j.neu roimage.2008.05.059
- Frith, U. (1989). Autism: Explaining the enigma. Oxford, England: Basil Blackwell.
- Frith, U., & Frith, C. (2010). The social brain: Allowing humans to boldly go where no other species has been. Philosophical Transactions of the Royal Society B: Biological Sciences, 365, 165-176. doi:10.1098/rstb.2009.0160
- Grodin, E. N., & White, T. L. (2015). The neuroanatomi- cal delineation of agentic and affiliative extraversion. Cognitive, Affective, and Behavioral Neuroscience, 15, 321-334. doi:10.3758/s13415-014-0331-6
- Happe, F. (1999). Autism: Cognitive deficit or cognitive style? Trends Cognitive Sciences, 3, 216-222.
- Higgins, E. T. (1998). Promotion and prevention: Regulatory focus as a motivational principle. In M. P. Zanna (Ed.), Advances in experimental social psychology (Vol. 30, pp. 1-46). San Diego, CA: Academic Press.
- Holme, P., & Saramaki, J. (2012). Temporal networks. Physics and Society, 519, 97-125.
- Hutchison, R. M., Womelsdorf, T., Allen, E. A., Bandettini, P. A., Calhoun, V. D., Corbetta, M., . . . Chang, C. (2013). Dynamic functional connectivity: Promise, issues, and interpretations. NeuroImage, 80, 360-378. doi:10.1016/j .neuroimage.2013.05.079
- Lewis, P. A., Rezaie, R., Brown, R., Roberts, N., & Dunbar, R. I. (2011). Ventromedial prefrontal volume predicts under- standing of others and social network size. NeuroImage, 57, 1624-1629. doi:10.1016/j.neuroimage.2011.05.030
- Mars, R. B., Neubert, F.-X., Noonan, M. P., Sallet, J., Toni, I., & Rushworth, M. F. S. (2012). On the relationship between the "default mode network" and the "social brain." Frontiers in Human Neuroscience, 6, Article 189. doi:10.3389/fnhum.2012.00189
- Mattar, M. G., Cole, M. W., Thompson-Schill, S. L., & Bassett, D. S. (2015). A functional cartography of cognitive systems. PLOS Computational Biology, 11(12), Article e1004533. doi:10.1371/journal.pcbi.1004533
- McNally, L., Brown, S. P., & Jackson, A. L. (2012). Cooperation and the evolution of intelligence. Proceedings of the Royal Society B: Biological Sciences, 279, 3027-3034. doi:10.1098/rspb.2012.0206
- Medaglia, J. D., Lynall, M. E., & Bassett, D. S. (2015). Cognitive network neuroscience. Journal of Cognitive Neuroscience, 27, 1471-1491. doi:10.1162/jocn_a_00810
- Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J. P. (2010). Community structure in time- dependent, multiscale, and multiplex networks. Science, 328, 876-878. doi:10.1126/science.1184819
- Ramsey, R., Cross, E. S., & Hamilton, A. F. (2011). Eye can see what you want: Posterior intraparietal sulcus encodes the object of an actor's gaze. Journal of Cognitive Neuroscience, 23, 3400-3409. doi:10.1162/jocn_a_00074
- Schilbach, L., Eickhoff, S. B., Rotarska-Jagiela, A., Fink, G. R., & Vogeley, K. (2008). Minds at rest? Social cognition as the default mode of cognizing and its putative relationship to the "default system" of the brain. Consciousness and Cognition, 17, 457-467. doi:10.1016/j.concog.2008.03.013
- Shine, J. M., Bissett, P. G., Bell, P. T., Koyejo, O., Balsters, J. H., Gorgolewski, K. J., . . . Poldrack, R. A. (2016). The dynamics of functional brain networks: Integrated net- work states during cognitive task performance. Neuron, 92, 544-554. doi:10.1016/j.neuron.2016.09.018
- Shteynberg, G. (2010). A silent emergence of culture: The social tuning effect. Journal of Personality and Social Psychology, 99, 683-689. doi:10.1037/a0019573
- Smith, L. B., & Thelen, E. (2003). Development as a dynamic system. Trends in Cognitive Sciences, 7, 343-348.
- Stanley, D. A., & Adolphs, R. (2013). Toward a neural basis for social behavior. Neuron, 80, 816-826. doi:10.1016/j .neuron.2013.10.038
- Stephens, G. J., Silbert, L. J., & Hasson, U. (2010). Speaker- listener neural coupling underlies successful communi- cation. Proceedings of the National Academy of Sciences, USA, 107, 14425-14430. doi:10.1073/pnas.1008662107
- Tamir, D. I., Thornton, M. A., Contreras, J. M., & Mitchell, J. P. (2016). Neural evidence that three dimensions organize mental state representation: Rationality, social impact, and valence. Proceedings of the National Academy of Sciences, USA, 113, 194-199. doi:10.1073/pnas.1511905112
- Telesford, Q. K., Lynall, M. E., Vettel, J., Miller, M. B., Grafton, S. T., & Bassett, D. S. (2016). Detection of functional brain network reconfiguration during task-driven cognitive states. NeuroImage, 142, 198-210. doi:10.1016/j.neuroim age.2016.05.078
- Tononi, G. (2012). Integrated information theory of conscious- ness: An updated account. Archives Italiennes de Biologie, 150, 293-329.
- Tononi, G., & Koch, C. (2015). Consciousness: Here, there and everywhere? Philosophical Transactions of the Royal Society B: Biological Sciences, 370(1668). doi:10.1098/rstb.2014.0167
- Yahata, N., Morimoto, J., Hashimoto, R., Lisi, G., Shibata, K., Kawakubo, Y., . . . Kawato, M. (2016). A small number of abnormal brain connections predicts adult autism spec- trum disorder. Nature Communications, 7, Article 11254. doi:10.1038/ncomms11254