Perspective | Published:

Growing a social brain

Nature Human Behaviourvolume 2pages624636 (2018) | Download Citation

Abstract

It has long been assumed that social animals, such as humans, are born with a brain system that has evolved to support social affiliation. However, the evidence does not necessarily support this assumption. Alternatively, social animals can be defined as those who cannot survive alone and rely on members from their group to regulate their ongoing physiology (or allostasis). The rather simple evolutionary constraint of social dependency for survival can be sufficient to make the social environment vitally salient, and to provide the ultimate driving force for socially crafted brain development and learning. In this Perspective, we propose a framework for sociality and specify a set of hypotheses on the mechanisms of social development and underlying neural systems. The theoretical shift proposed here implies that profound human characteristics, including but not limited to sociality, are acquired at an early age, while social interactions provide key wiring instructions that determine brain development.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Change history

  • 22 August 2018

    In the version of this Perspective originally published, at the end of the first paragraph of the section ‘Neural prediction as a potential mechanism for how experience sculpts the developing brain’ the citation to ref. 76 should have been to ref. 74, and at the end of the first sentence of the next paragraph ref. 76 should have been cited alongside ref. 74. These have now been corrected.

References

  1. 1.

    Rand, D. G. & Nowak, M. A. Human cooperation. Trends Cogn. Sci. 17, 413–425 (2013).

  2. 2.

    Johnson, Z. V. & Young, L. J. Neurobiological mechanisms of social attachment and pair bonding. Curr. Opin. Behav. Sci. 3, 38–44 (2015).

  3. 3.

    Hawkes, K. Grandmothers and the evolution of human longevity. Am. J. Hum. Biol. 15, 380–400 (2003).

  4. 4.

    Dunbar, R. I. & Shultz, S. Evolution in the social brain. Science 317, 1344–1347 (2007).

  5. 5.

    Sterling, P. Allostasis: a model of predictive regulation. Physiol. Behav. 106, 5–15 (2012).

  6. 6.

    Atzil, S. & Barrett, L. F. Social regulation of allostasis: Commentary on “Mentalizing homeostasis: the social origins of interoceptive inference” by Fotopoulou & Tsakiris. Neuropsychoanalysis 19, 1–24 (2017).

  7. 7.

    Rao, P. N. S., Shashidhar, A. & Ashok, C. In utero fuel homeostasis: lessons for a clinician. Indian J. Endocrinol. Metab. 17, 60–68 (2013).

  8. 8.

    Winberg, J. Mother and newborn baby: mutual regulation of physiology and behavior — a selective review. Dev. Psychobiol. 47, 217–229 (2005).

  9. 9.

    Hofer, M. A. Hidden regulators in attachment, separation, and loss. Monogr. Soc. Res. Child Dev. 59, 192–207 (1994).

  10. 10.

    Feldman, R., Magori-Cohen, R., Galili, G., Singer, M. & Louzoun, Y. Mother and infant coordinate heart rhythms through episodes of interaction synchrony. Infant Behav. Dev. 34, 569–577 (2011).

  11. 11.

    Feldman, R., Eidelman, A. I., Sirota, L. & Weller, A. Comparison of skin-to-skin (kangaroo) and traditional care: parenting outcomes and preterm infant development. Pediatrics 110, 16–26 (2002).

  12. 12.

    Keramati, M. & Gutkin, B. Homeostatic reinforcement learning for integrating reward collection and physiological stability. eLife 3, e04811 (2014).

  13. 13.

    Barrett, L. F. & Satpute, A. B. Large-scale brain networks in affective and social neuroscience: towards an integrative functional architecture of the brain. Curr. Opin. Neurobiol. 23, 361–372 (2013).

  14. 14.

    Kleckner, I. et al. Evidence for a large-scale brain system supporting allostasis and interoception in humans. Nat. Hum. Behav. 1, 0069 (2017).

  15. 15.

    Barrett, L. F. How Emotions are Made (Houghton Mifflin Harcourt, Boston, MA, 2017).

  16. 16.

    Gao, W., Lin, W., Grewen, K. & Gilmore, J. H. Functional connectivity of the infant human brain plastic and modifiable. Neuroscientist 23, 169–184 (2016).

  17. 17.

    Bullmore, E. & Sporns, O. The economy of brain network organization. Nat. Rev. Neurosci. 13, 336–349 (2012).

  18. 18.

    Yeo, B. T. et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 1125–1165 (2011).

  19. 19.

    van den Heuvel, M. P. & Sporns, O. Rich-club organization of the human connectome. J. Neurosci. 31, 15775–15786 (2011).

  20. 20.

    van den Heuvel, M. P. et al. Abnormal rich club organization and functional brain dynamics in schizophrenia. JAMA Psychiatry 70, 783–792 (2013).

  21. 21.

    Atzil, S., Hendler, T. & Feldman, R. Specifying the neurobiological basis of human attachment: brain, hormones, and behavior in synchronous and intrusive mothers. Neuropsychopharmacology 36, 2603–2615 (2011).

  22. 22.

    Atzil, S., Hendler, T. & Feldman, R. The brain basis of social synchrony. Soc. Cogn. Affect. Neurosci. 9, 1193–1202 (2013).

  23. 23.

    Atzil, S. et al. Dopamine in the medial amygdala network mediates human bonding. Proc. Natl Acad. Sci. USA 114, 2361–2366 (2017).

  24. 24.

    Bickart, K. C., Hollenbeck, M. C., Barrett, L. F. & Dickerson, B. C. Intrinsic amygdala-cortical functional connectivity predicts social network size in humans. J. Neurosci. 32, 14729–14741 (2012).

  25. 25.

    Uddin, L. Q. et al. Salience network–based classification and prediction of symptom severity in children with autism. JAMA Psychiatry 70, 869–879 (2013).

  26. 26.

    Di Martino, A. et al. The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol. Psychiatry 19, 659–667 (2014).

  27. 27.

    Dubois, J. et al. The early development of brain white matter: a review of imaging studies in fetuses, newborns and infants. Neuroscience 276, 48–71 (2014).

  28. 28.

    Gao, W. et al. Temporal and spatial evolution of brain network topology during the first two years of life. PLoS ONE 6, e25278 (2011).

  29. 29.

    Gao, W. et al. The synchronization within and interaction between the default and dorsal attention networks in early infancy. Cereb. Cortex 23, 594–603 (2013).

  30. 30.

    Gao, W. et al. Temporal and spatial development of axonal maturation and myelination of white matter in the developing brain. Am. J. Neuroradiol. 30, 290–296 (2009).

  31. 31.

    Smyser, C. D., Snyder, A. Z. & Neil, J. J. Functional connectivity MRI in infants: exploration of the functional organization of the developing brain. Neuroimage 56, 1437–1452 (2011).

  32. 32.

    Fransson, P., Aden, U., Blennow, M. & Lagercrantz, H. The functional architecture of the infant brain as revealed by resting-state fMRI. Cereb. Cortex 21, 145–154 (2011).

  33. 33.

    Fransson, P. et al. Resting-state networks in the infant brain. Proc. Natl Acad. Sci. USA 104, 15531–15536 (2007).

  34. 34.

    Elton, A., Alcauter, S. & Gao, W. Network connectivity abnormality profile supports a categorical-dimensional hybrid model of ADHD. Hum. Brain Mapp. 35, 4531–4543 (2014).

  35. 35.

    Fair, D. A. et al. The maturing architecture of the brain's default network. Proc. Natl Acad. Sci. USA 105, 4028–4032 (2008).

  36. 36.

    Tau, G. Z. & Peterson, B. S. Normal development of brain circuits. Neuropsychopharmacology 35, 147–168 (2010).

  37. 37.

    Stiles, J. & Jernigan, T. L. The basics of brain development. Neuropsychol. Rev. 20, 327–348 (2010).

  38. 38.

    Dubois, J. et al. Primary cortical folding in the human newborn: an early marker of later functional development. Brain 131, 2028–2041 (2008).

  39. 39.

    Finlay, B. L. & Uchiyama, R. in Evolution of Nervous Systems 2nd edn (ed. Kaas, J. H.) 123–148 (Elsevier, Oxford, 2017).

  40. 40.

    Rogers, C. E. et al. Regional cerebral development at term relates to school-age social-emotional development in very preterm children. J. Am. Acad. Child Adolesc. Psychiatry 51, 181–191 (2012).

  41. 41.

    Woodward, L. J., Clark, C. A., Bora, S. & Inder, T. E. Neonatal white matter abnormalities an important predictor of neurocognitive outcome for very preterm children. PLoS ONE 7, e51879 (2012).

  42. 42.

    Curley, J. P. & Champagne, F. A. Influence of maternal care on the developing brain: mechanisms, temporal dynamics and sensitive periods. Front. Neuroendocrinol. 40, 52–66 (2016).

  43. 43.

    Johnson, M. H. Functional brain development in humans. Nat. Rev. Neurosci. 2, 475–483 (2001).

  44. 44.

    Feldman, R. Parent–infant synchrony and the construction of shared timing; physiological precursors, developmental outcomes, and risk conditions. J. Child Psychol. Psychiatry 48, 329–354 (2007).

  45. 45.

    Tomoda, A. et al. Reduced prefrontal cortical gray matter volume in young adults exposed to harsh corporal punishment. Neuroimage 47, T66–T71 (2009).

  46. 46.

    Whittle, S. et al. Positive parenting predicts the development of adolescent brain structure: a longitudinal study. Dev. Cogn. Neurosci. 8, 7–17 (2014).

  47. 47.

    Teicher, M. H., Anderson, C. M. & Polcari, A. Childhood maltreatment is associated with reduced volume in the hippocampal subfields CA3, dentate gyrus, and subiculum. Proc. Natl Acad. Sci. USA 109, E563–E572 (2012).

  48. 48.

    Luby, J. L. et al. Maternal support in early childhood predicts larger hippocampal volumes at school age. Proc. Natl Acad. Sci. USA 109, 2854–2859 (2012).

  49. 49.

    Champagne, F. A. et al. Maternal care associated with methylation of the estrogen receptor-α1b promoter and estrogen receptor-α expression in the medial preoptic area of female offspring. Endocrinology 147, 2909–2915 (2006).

  50. 50.

    McGowan, P. O. et al. Epigenetic regulation of the glucocorticoid receptor in human brain associates with childhood abuse. Nat. Neurosci. 12, 342–348 (2009).

  51. 51.

    Pena, C. J., Neugut, Y. D., Calarco, C. A. & Champagne, F. A. Effects of maternal care on the development of midbrain dopamine pathways and reward-directed behavior in female offspring. Eur. J. Neurosci. 39, 946–956 (2014).

  52. 52.

    Insel, T. R. Oxytocin — a neuropeptide for affiliation: evidence from behavioral, receptor autoradiographic, and comparative studies. Psychoneuroendocrinology 17, 3–35 (1992).

  53. 53.

    Webb, A. R., Heller, H. T., Benson, C. B. & Lahav, A. Mother’s voice and heartbeat sounds elicit auditory plasticity in the human brain before full gestation. Proc. Natl Acad. Sci. USA 112, 3152–3157 (2015).

  54. 54.

    Teicher, M. H., Samson, J. A., Anderson, C. M. & Ohashi, K. The effects of childhood maltreatment on brain structure, function and connectivity. Nat. Rev. Neurosci. 17, 652–666 (2016).

  55. 55.

    Suomi, S. J. Early determinants of behaviour: evidence from primate studies. Br. Med. Bull. 53, 170–184 (1997).

  56. 56.

    Arling, G. L. & Harlow, H. F. Effects of social deprivation on maternal behavior of rhesus monkeys. J. Comp. Physiol. Psychol. 64, 371–377 (1967).

  57. 57.

    Harlow, H. F. Total social isolation: effects on macaque monkey behavior. Science 148, 666 (1965).

  58. 58.

    Champagne, F. A., Francis, D. D., Mar, A. & Meaney, M. J. Variations in maternal care in the rat as a mediating influence for the effects of environment on development. Physiol. Behav. 79, 359–371 (2003).

  59. 59.

    Champagne, F. A. Epigenetic mechanisms and the transgenerational effects of maternal care. Front. Neuroendocrinol. 29, 386–397 (2008).

  60. 60.

    Champagne, F. & Meaney, M. J. Like mother, like daughter: evidence for non-genomic transmission of parental behavior and stress responsivity. Prog. Brain Res. 133, 287–302 (2001).

  61. 61.

    Pena, C. J., Neugut, Y. D. & Champagne, F. A. Developmental timing of the effects of maternal care on gene expression and epigenetic regulation of hormone receptor levels in female rats. Endocrinology 154, 4340–4351 (2013).

  62. 62.

    Feldman, R. The adaptive human parental brain: implications for children’s social development. Trends Neurosci. 38, 387–399 (2015).

  63. 63.

    Granat, A., Gadassi, R., Gilboa-Schechtman, E. & Feldman, R. Maternal depression and anxiety, social synchrony, and infant regulation of negative and positive emotions. Emotion 17, 11–27 (2016).

  64. 64.

    Herba, C. M. Maternal depression and child behavioural outcomes. Lancet Psychiatry 1, 408–409 (2014).

  65. 65.

    Raby, K. L., Roisman, G. I., Simpson, J. A., Collins, W. A. & Steele, R. D. Greater maternal insensitivity in childhood predicts greater electrodermal reactivity during conflict discussions with romantic partners in adulthood. Psychol. Sci. 26, 348–353 (2015).

  66. 66.

    Feldman, R. Parent–infant synchrony: biological foundations and developmental outcomes. Curr. Dir. Psychol. Sci. 16, 340–345 (2007).

  67. 67.

    Carey, S. & Spelke, E. Science and core knowledge. Philos. Sci. 63, 515–533 (1996).

  68. 68.

    Spelke, E. S. & Kinzler, K. D. Core knowledge. Dev. Sci. 10, 89–96 (2007).

  69. 69.

    Clark, A. Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behav. Brain Sci. 36, 181–204 (2013).

  70. 70.

    Hohwy, J. The Predictive Mind (Oxford Univ. Press, Oxford, 2013).

  71. 71.

    Rao, R. P. & Ballard, D. H. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nat. Neurosci. 2, 79–87 (1999).

  72. 72.

    Friston, K. The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11, 127–138 (2010).

  73. 73.

    Clark, A. Are we predictive engines? Perils, prospects, and the puzzle of the porous perceiver. Behav. Brain Sci. 36, 233–253 (2013).

  74. 74.

    Barrett, L. F. & Simmons, W. K. Interoceptive predictions in the brain. Nat. Rev. Neurosci. 16, 419–429 (2015).

  75. 75.

    Chanes, L. & Barrett, L. F. Redefining the role of limbic areas in cortical processing. Trends Cogn. Sci. 20, 96–106 (2016).

  76. 76.

    Friston, K. A theory of cortical responses. Philos. Trans. R. Soc. London Ser. B 360, 815–836 (2005).

  77. 77.

    Gopnik, A. The Philosophical Baby (Bodley Head, London, 2009).

  78. 78.

    Siegelman, N. & Frost, R. Statistical learning as an individual ability: theoretical perspectives and empirical evidence. J. Mem. Lang. 81, 105–120 (2015).

  79. 79.

    Krogh, L., Vlach, H. A. & Johnson, S. P. Statistical learning across development: flexible yet constrained. Front. Psychol. 3, 598 (2012).

  80. 80.

    Saffran, J. R., Aslin, R. N. & Newport, E. L. Statistical learning by 8-month-old infants. Science 274, 1926–1928 (1996).

  81. 81.

    Kirkham, N. Z., Slemmer, J. A. & Johnson, S. P. Visual statistical learning in infancy: evidence for a domain general learning mechanism. Cognition 83, B35–B42 (2002).

  82. 82.

    Tenenbaum, J. B., Kemp, C., Griffiths, T. L. & Goodman, N. D. How to grow a mind: statistics, structure, and abstraction. Science 331, 1279–1285 (2011).

  83. 83.

    Sterling, P. & Laughlin, S. Principles of Neural Design (MIT Press, Cambridge, MA, 2015).

  84. 84.

    Carpenter, R. Homeostasis: a plea for a unified approach. Adv. Physiol. Educ. 28, 180–187 (2004).

  85. 85.

    Gu, X. & FitzGerald, T. Interoceptive inference: homeostasis and decision-making. Trends Cogn. Sci. 18, 269–270 (2014).

  86. 86.

    Seth, A. K. Interoceptive inference, emotion, and the embodied self. Trends Cogn. Sci. 17, 565–573 (2013).

  87. 87.

    Seth, A. K., Suzuki, K. & Critchley, H. D. An interoceptive predictive coding model of conscious presence. Front. Psychol. 2, 395 (2012).

  88. 88.

    Finlay, B. L. & Syal, S. The pain of altruism. Trends Cogn. Sci. 18, 615–617 (2014).

  89. 89.

    Lummaa, V., Vuorisalo, T., Barr, R. G. & Lehtonen, L. Why cry? Adaptive significance of intensive crying in human infants. Evol. Hum. Behav. 19, 193–202 (1998).

  90. 90.

    Davis, E. P. et al. Exposure to unpredictable maternal sensory signals influences cognitive development across species. Proc. Natl Acad. Sci. USA 114, 10390–10395 (2017).

  91. 91.

    Stein, B. E., Stanford, T. R. & Rowland, B. A. Development of multisensory integration from the perspective of the individual neuron. Nat. Rev. Neurosci. 15, 520–535 (2014).

  92. 92.

    Petanjek, Z., Judaš, M., Kostović, I. & Uylings, H. B. M. Lifespan alterations of basal dendritic trees of pyramidal neurons in the human prefrontal cortex: a layer-specific pattern. Cereb. Cortex 18, 915–929 (2008).

  93. 93.

    Alcauter, S. et al. Development of thalamocortical connectivity during infancy and its cognitive correlations. J. Neurosci. 34, 9067–9075 (2014).

  94. 94.

    Alcauter, S., Lin, W., Keith Smith, J., Gilmore, J. H. & Gao, W. Consistent anterior-posterior segregation of the insula during the first 2 years of life. Cereb. Cortex 25, 1176–1187 (2015).

  95. 95.

    Trachtenberg, J. T. & Stryker, M. P. Rapid anatomical plasticity of horizontal connections in the developing visual cortex. J. Neurosci. 21, 3476–3482 (2001).

  96. 96.

    Singer, T. & Lamm, C. The social neuroscience of empathy. Ann. N. Y. Acad. Sci. 1156, 81–96 (2009).

  97. 97.

    Andrews-Hanna, J. R., Smallwood, J. & Spreng, R. N. The default network and self-generated thought: component processes, dynamic control, and clinical relevance. Ann. N. Y. Acad. Sci. 1316, 29–52 (2014).

  98. 98.

    Shamay-Tsoory, S. G. The neural bases for empathy. Neuroscientist 17, 18–24 (2011).

  99. 99.

    Ganzel, B. L., Morris, P. A. & Wethington, E. Allostasis and the human brain: integrating models of stress from the social and life sciences. Psychol. Rev. 117, 134–174 (2010).

  100. 100.

    Bickart, K. C., Dickerson, B. C. & Barrett, L. F. The amygdala as a hub in brain networks that support social life. Neuropsychologia 63, 235–248 (2014).

  101. 101.

    Schultz, W. & Dickinson, A. Neuronal coding of prediction errors. Annu. Rev. Neurosci. 23, 473–500 (2000).

  102. 102.

    Lindquist, K. A., Satpute, A. B., Wager, T. D., Weber, J. & Barrett, L. F. The brain basis of positive and negative affect: evidence from a meta-analysis of the human neuroimaging literature. Cereb. Cortex 26, 1910–1922 (2015).

  103. 103.

    Bar, M. The proactive brain: memory for predictions. Philos. Trans. R. Soc. London Ser. B 364, 1235–1243 (2009).

  104. 104.

    Bar, M. Predictions: a universal principle in the operation of the human brain. Introduction. Philos. Trans. R. Soc. London Ser. B 364, 1181–1182 (2009).

  105. 105.

    Murphy, G. The Big Book of Concepts (MIT Press, Cambridge, MA, 2004).

  106. 106.

    Barsalou, L. W. Ad hoc categories. Mem. Cogn. 11, 211–227 (1983).

  107. 107.

    Barsalou, L. W., Kyle Simmons, W., Barbey, A. K. & Wilson, C. D. Grounding conceptual knowledge in modality-specific systems. Trends Cogn. Sci. 7, 84–91 (2003).

  108. 108.

    Hollerman, J. R. & Schultz, W. Dopamine neurons report an error in the temporal prediction of reward during learning. Nat. Neurosci. 1, 304–309 (1998).

  109. 109.

    Barrett, L. F. The theory of constructed emotion: an active inference account of interoception and categorization. Soc. Cogn. Affect. Neurosci. 12, 1–23 (2017).

  110. 110.

    Feldman, R., Rosenthal, Z. & Eidelman, A. I. Maternal-preterm skin-to-skin contact enhances child physiologic organization and cognitive control across the first 10 years of life. Biol. Psychiatry 75, 56–64 (2014).

  111. 111.

    Levin, B. E. Metabolic imprinting: critical impact of the perinatal environment on the regulation of energy homeostasis. Philos. Trans. R. Soc. London Ser. B 361, 1107–1121 (2006).

  112. 112.

    Bauman, D. in Ruminant Physiology: Digestion, Metabolism, Growth and Reproduction (eds Dobson, A. & Dobson, M. J.) 238–256 (Comstock Publishing Associates, Ithaca, NY, 2000).

  113. 113.

    Arrieta, M. C., Stiemsma, L. T., Amenyogbe, N., Brown, E. M. & Finlay, B. The intestinal microbiome in early life: health and disease. Front. Immunol. 5, 427 (2014).

  114. 114.

    Nakata, T. & Trehub, S. E. Infants’ responsiveness to maternal speech and singing. Infant Behav. Dev. 27, 455–464 (2004).

  115. 115.

    Tomasello, M. in Joint Attention: Its Origins and Role in Development (eds ​Moore, C. & Dunham, P.) 103–130 (Psychology Press, New York, NY, 1995).

  116. 116.

    Amso, D. & Scerif, G. The attentive brain: insights from developmental cognitive neuroscience. Nat. Rev. Neurosci. 16, 606–619 (2015).

  117. 117.

    Baron-Cohen, S. The development of a theory of mind in autism: deviance and delay? Psychiatry Clin. North Am. 14, 33–51 (1991).

  118. 118.

    Belmonte, M. K. et al. Autism as a disorder of neural information processing: directions for research and targets for therapy. Mol. Psychiatry 9, 646–663 (2004).

  119. 119.

    Trehub, S. E. & Gudmundsdottir, H. R. in The Oxford Handbook of Singing (eds Welch, G. & Sergeant, D.) 1–20 (Oxford Univ. Press, Oxford, 2015).

  120. 120.

    MacLean, P. C. et al. Mother–infant mutual eye gaze supports emotion regulation in infancy during the still-face paradigm. Infant Behav. Dev. 37, 512–522 (2014).

  121. 121.

    Mantis, I., Stack, D. M., Ng, L., Serbin, L. A. & Schwartzman, A. E. Mutual touch during mother–infant face-to-face still-face interactions: influences of interaction period and infant birth status. Infant Behav. Dev. 37, 258–267 (2014).

  122. 122.

    Ramsay, D. S. & Woods, S. C. Clarifying the roles of homeostasis and allostasis in physiological regulation. Psychol. Rev. 121, 225 (2014).

  123. 123.

    Muenzinger, K. F. & Fletcher, F. M. Motivation in learning. VI. Escape from electric shock compared with hunger-food tension in the visual discrimination habit. J. Comp. Psychol. 22, 79 (1936).

  124. 124.

    Petrinovich, L. & Bolles, R. Deprivation states and behavioral attributes. J. Comp. Physiol. Psychol. 47, 450 (1954).

  125. 125.

    Okanoya, K. in Evolution of the Brain, Cognition, and Emotion in Vertebrates (eds Watanabe, S., Hofman, M. A. & Shimizu, T.) 207–224 (Springer, Tokyo, 2017).

  126. 126.

    Scott, J. P. Critical periods in the development of social behavior in puppies. Psychosom. Med. 20, 42–54 (1958).

  127. 127.

    Li, S. S. Y. & McNally, G. P. The conditions that promote fear learning: prediction error and Pavlovian fear conditioning. Neurobiol. Learn. Mem. 108, 14–21 (2014).

  128. 128.

    Preuss, T. M. The human brain: rewired and running hot. Ann. N. Y. Acad. Sci. 1225, 182–191 (2011).

  129. 129.

    Spocter, M. A. et al. Neuropil distribution in the cerebral cortex differs between humans and chimpanzees. J. Comp. Neurol. 520, 2917–2929 (2012).

  130. 130.

    Barrett, L. F. The theory of constructed emotion: an active inference account of interoception and categorization. Soc. Cogn. Affect. Neurosci. 12, 1–23 (2017).

  131. 131.

    Finlay, B. & Uchiyama, R. Evolution of Nervous Systems (Oxford Academic Press, Oxford, 2017).

  132. 132.

    Hauser, M. D., Chomsky, N. & Fitch, W. T. The faculty of language: what is it, who has it, and how did it evolve? Science 298, 1569–1579 (2002).

  133. 133.

    Bloom, P. Precis of How children learn the meanings of words. Behav. Brain Sci. 24, 1095–1103; discussion 1104–1034 (2001).

  134. 134.

    Lupfer, G., Frieman, J. & Coonfield, D. Social transmission of flavor preferences in two species of hamsters (Mesocricetus auratus and Phodopus campbelli). J. Comp. Psychol. 117, 449–455 (2003).

  135. 135.

    Galef, B. G. & Laland, K. N. Social learning in animals: empirical studies and theoretical models. AIBS Bull. 55, 489–499 (2005).

  136. 136.

    Uller, T. Developmental plasticity and the evolution of parental effects. Trends Ecol. Evol. 23, 432–438 (2008).

  137. 137.

    Wolf, J. B. & Brodie, E. D. The coadaptation of parental and offspring characters. Evolution 52, 299–308 (1998).

  138. 138.

    Stigler, J. W., Shweder, R. A. & Herdt, G. (eds) Cultural Psychology 1–44 (Cambridge Univ. Press, New York, NY, 1990).

  139. 139.

    Atzil, S. & Gendron, M. Bio-behavioral synchrony promotes the development of conceptualized emotions. Curr. Opin. Psychol. 17, 162–169 (2017).

  140. 140.

    Gendron, M., Roberson, D. & Barrett, L. F. Cultural variation in emotion perception is real: a response to Sauter, Eisner, Ekman, and Scott (2015). Psychol. Sci. 26, 357–359 (2015).

  141. 141.

    Russell, J. A. Culture and the categorization of emotions. Psychol. Bull. 110, 426–450 (1991).

  142. 142.

    Andrews-Hanna, J. R. The brain’s default network and its adaptive role in internal mentation. Neuroscientist 18, 251–270 (2012).

  143. 143.

    Lombardo, M. V. et al. Shared neural circuits for mentalizing about the self and others. J. Cogn. Neurosci. 22, 1623–1635 (2010).

  144. 144.

    Gao, W. et al. Functional network development during the first year: relative sequence and socioeconomic correlations. Cereb. Cortex 25, 2919–2928 (2015).

  145. 145.

    Gao, W. et al. Evidence on the emergence of the brain’s default network from 2-week-old to 2-year-old healthy pediatric subjects. Proc. Natl Acad. Sci. USA 106, 6790–6795 (2009).

  146. 146.

    Supekar, K. et al. Development of functional and structural connectivity within the default mode network in young children. Neuroimage 52, 290–301 (2010).

  147. 147.

    Blakemore, S. J., den Ouden, H., Choudhury, S. & Frith, C. Adolescent development of the neural circuitry for thinking about intentions. Soc. Cogn. Affect. Neurosci. 2, 130–139 (2007).

  148. 148.

    Alcauter, S. et al. Frequency of spontaneous BOLD signal shifts during infancy and correlates with cognitive performance. Dev. Cogn. Neurosci. 12, 40–50 (2015).

  149. 149.

    Uddin, L. Q., Supekar, K. S., Ryali, S. & Menon, V. Dynamic reconfiguration of structural and functional connectivity across core neurocognitive brain networks with development. J. Neurosci. 31, 18578–18589 (2011).

  150. 150.

    Wegner, D. M. in Theories of Group Behavior (eds Mullen, B. & Goethals, G. R.) 185–208 (Springer, New York, NY, 1987).

  151. 151.

    Syal, S. & Finlay, B. L. Thinking outside the cortex: social motivation in the evolution and development of language. Dev. Sci. 14, 417–430 (2011).

  152. 152.

    Dunbar, R. I. M. The social brain hypothesis. Evol. Anthropol. 6, 178–190 (1998).

  153. 153.

    Gunnar, M. R. & Sullivan, R. M. The neurodevelopment of social buffering and fear learning: integration and crosstalk. Soc. Neurosci. 12, 1–7 (2017).

  154. 154.

    Coan, J. A., Schaefer, H. S. & Davidson, R. J. Lending a hand: social regulation of the neural response to threat. Psychol. Sci. 17, 1032–1039 (2006).

  155. 155.

    Master, S. L. et al. A picture’s worth: partner photographs reduce experimentally induced pain. Psychol. Sci. 20, 1316–1318 (2009).

  156. 156.

    Lantolf, J. P., Thorne, S. L. & Poehner, M. E. in Theories in Second Language Acquisition: An Introduction (eds VanPatten, B. & William, J.) 207–226 (Erlbaum, Mahwah, NJ, 2015).

  157. 157.

    Padilla, A. M. & Perez, W. Acculturation, social identity, and social cognition: a new perspective. Hisp. J. Behav. Sci. 25, 35–55 (2003).

  158. 158.

    Adolphs, R. The social brain: neural basis of social knowledge. Annu. Rev. Psychol. 60, 693–716 (2009).

  159. 159.

    Frith, C. D. The social brain? Philos. Trans. R. Soc. London Ser. B 362, 671–678 (2007).

  160. 160.

    Whitacre, J. M., Rohlfshagen, P., Bender, A. & Yao, X. Evolutionary mechanics: new engineering principles for the emergence of flexibility in a dynamic and uncertain world. Nat. Comput. 11, 431–448 (2012).

  161. 161.

    Boyd, R., Richerson, P. J. & Henrich, J. The cultural niche: why social learning is essential for human adaptation. Proc. Natl Acad. Sci. USA 108, 10918–10925 (2011).

  162. 162.

    Heyes, C. & Pearce, J. M. Not-so-social learning strategies. Proc. R. Soc. B 282, 1709–1715 (2015).

  163. 163.

    Champagne, F. A. & Meaney, M. J. Transgenerational effects of social environment on variations in maternal care and behavioral response to novelty. Behav. Neurosci. 121, 1353–1363 (2007).

  164. 164.

    Francis, D., Diorio, J., Liu, D. & Meaney, M. J. Nongenomic transmission across generations of maternal behavior and stress responses in the rat. Science 286, 1155–1158 (1999).

  165. 165.

    Lorenz, K. Der Kumpan in der Umwelt des Vogels. J. Ornithol. 83, 289–413 (1935).

  166. 166.

    Lorenz, K. in Leaders in the Study of Animal Behavior: Autobiographical Perspectives (ed. Baerends, G. P.) 259–287 (Bucknell Univ. Press, Lewisburg, PA, 1985).

  167. 167.

    Morton, J. & Johnson, M. H. CONSPEC and CONLERN: a two-process theory of infant face recognition. Psychol. Rev. 98, 164–181 (1991).

  168. 168.

    Braddick, O. Human development: faces in the womb. Curr. Biol. 27, R704–R706 (2017).

  169. 169.

    Cook, R., Bird, G., Catmur, C., Press, C. & Heyes, C. Mirror neurons: from origin to function. Behav. Brain Sci. 37, 177–192 (2014).

  170. 170.

    Turati, C., Di Giorgio, E., Bardi, L. & Simion, F. Holistic face processing in newborns, 3-month-old infants, and adults: evidence from the composite face effect. Child Dev. 81, 1894–1905 (2010).

  171. 171.

    Gava, L., Valenza, E., Turati, C. & de Schonen, S. Effect of partial occlusion on newborns’ face preference and recognition. Dev. Sci. 11, 563–574 (2008).

  172. 172.

    Turati, C., Bulf, H. & Simion, F. Newborns’ face recognition over changes in viewpoint. Cognition 106, 1300–1321 (2008).

  173. 173.

    Cassia, V. M., Turati, C. & Simion, F. Can a nonspecific bias toward top-heavy patterns explain newborns’ face preference? Psychol. Sci. 15, 379–383 (2004).

  174. 174.

    Simion, F., Leo, I., Turati, C., Valenza, E. & Dalla Barba, B. How face specialization emerges in the first months of life. Prog. Brain Res. 164, 169–185 (2007).

  175. 175.

    Turati, C. Why faces are not special to newborns: an alternative account of the face preference. Curr. Dir. Psychol. Sci. 13, 5–8 (2004).

  176. 176.

    Gartstein, M. A. & Rothbart, M. K. Studying infant temperament via the revised infant behavior questionnaire. Infant Behav. Dev. 26, 64–86 (2003).

  177. 177.

    Huffman, L. C. et al. Infant temperament and cardiac vagal tone: assessments at twelve weeks of age. Child Dev. 69, 624–635 (1998).

  178. 178.

    Davidov, M., Knafo-Noam, A., Serbin, L. A. & Moss, E. The influential child: how children affect their environment and influence their own risk and resilience. Dev. Psychopathol. 27, 947–951 (2015).

  179. 179.

    Rothbart, M. K. & Ahadi, S. A. Temperament and the development of personality. J. Abnorm. Psychol. 103, 55 (1994).

  180. 180.

    George, O., Le Moal, M. & Koob, G. F. Allostasis and addiction: role of the dopamine and corticotropin-releasing factor systems. Physiol. Behav. 106, 58–64 (2012).

  181. 181.

    Koob, G. F. & Le Moal, M. Drug addiction, dysregulation of reward, and allostasis. Neuropsychopharmacology 24, 97–129 (2001).

  182. 182.

    Beauchaine, T. P., Neuhaus, E., Zalewski, M., Crowell, S. E. & Potapova, N. The effects of allostatic load on neural systems subserving motivation, mood regulation, and social affiliation. Dev. Psychopathol. 23, 975–999 (2011).

  183. 183.

    Buckner, R. L., Andrews-Hanna, J. R. & Schacter, D. L. The brain’s default network: anatomy, function, and relevance to disease. Ann. N. Y. Acad. Sci. 1124, 1–38 (2008).

  184. 184.

    Young, L. J. & Barrett, C. E. Neuroscience. Can oxytocin treat autism? Science 347, 825–826 (2015).

  185. 185.

    Farmer, C., Thurm, A. & Grant, P. Pharmacotherapy for the core symptoms in autistic disorder: current status of the research. Drugs 73, 303–314 (2013).

  186. 186.

    Pellicano, E. & Burr, D. When the world becomes ‘too real’: a Bayesian explanation of autistic perception. Trends Cogn. Sci. 16, 504–510 (2012).

  187. 187.

    Verly, M. et al. Altered functional connectivity of the language network in ASD: role of classical language areas and cerebellum. Neuroimage Clin. 4, 374–382 (2014).

  188. 188.

    Jaffe-Dax, S., Frenkel, O. & Ahissar, M. Dyslexics’ faster decay of implicit memory for sounds and words is manifested in their shorter neural adaptation. Elife 6, e20557 (2017).

  189. 189.

    Jaffe-Dax, S., Raviv, O., Jacoby, N., Loewenstein, Y. & Ahissar, M. Towards a computational model of Dyslexia. BMC Neurosci. 16, O12 (2015).

  190. 190.

    Leerkes, E. M., Su, J., Calkins, S. D., O’Brien, M. & Supple, A. J. Maternal physiological dysregulation while parenting poses risk for infant attachment disorganization and behavior problems. Dev. Psychopathol. 29, 1–13 (2016).

  191. 191.

    Tasker, F. Lesbian mothers, gay fathers, and their children: a review. J. Dev. Behav. Pediatr. 26, 224–240 (2005).

  192. 192.

    Bornstein, M. H. & Bradley, R. H. Socioeconomic Status, Parenting, and Child Development (Routledge, New York, NY, 2014).

  193. 193.

    Merz, E. C., Tottenham, N. & Noble, K. G. Socioeconomic status, amygdala volume, and internalizing symptoms in children and adolescents. J. Clin. Child Adolesc. Psychol. 47, 312–323 (2018).

  194. 194.

    Kolb, B., Gibb, R. & Robinson, T. E. Brain plasticity and behavior. Curr. Dir. Psychol. Sci. 12, 1–5 (2003).

  195. 195.

    Metcalfe, N. B., Taylor, A. C. & Thorpe, J. E. Metabolic rate, social status and life-history strategies in Atlantic salmon. Anim. Behav. 49, 431–436 (1995).

  196. 196.

    Leonard, W. R. & Robertson, M. L. Evolutionary perspectives on human nutrition: the influence of brain and body size on diet and metabolism. Am. J. Hum. Biol. 6, 77–88 (1994).

  197. 197.

    Dunbar, R. I. The social brain hypothesis and its implications for social evolution. Ann. Hum. Biol. 36, 562–572 (2009).

  198. 198.

    Soares, C. A. & Carneiro, R. S. Social behavior between mothers’ young of sloths Bradypus variegatus Schinz, 1825 (Xenarthra: Bradypodidae). Braz. J. Biol. 62, 249–252 (2002).

  199. 199.

    Richard, A. F. & Nicoll, M. E. Female social dominance and basal metabolism in a Malagasy primate. Propithecus verreauxi. Am. J. Primatol. 12, 309–314 (1987).

  200. 200.

    Curley, J. P. & Keverne, E. B. Genes, brains and mammalian social bonds. Trends Ecol. Evol. 20, 561–567 (2005).

  201. 201.

    Schulkin, J. Allostasis, Homeostasis, and the Costs of Physiological Adaptation (Cambridge Univ. Press, Cambridge, 2004).

  202. 202.

    Shpigler, H. Y. et al. Deep evolutionary conservation of autism-related genes. Proc. Natl Acad. Sci. USA 36, 9653–9658 (2017).

  203. 203.

    Gao, W., Alcauter, S., Smith, J. K., Gilmore, J. H. & Lin, W. Development of human brain cortical network architecture during infancy. Brain Struct. Funct. 220, 1173–1186 (2015).

  204. 204.

    Sepulcre, J., Sabuncu, M. R., Yeo, T. B., Liu, H. & Johnson, K. A. Stepwise connectivity of the modal cortex reveals the multimodal organization of the human brain. J. Neurosci. 32, 10649–10661 (2012).

  205. 205.

    Xu, P. et al. Different topological organization of human brain functional networks with eyes open versus eyes closed. Neuroimage 90, 246–255 (2014).

  206. 206.

    Sterzer, P. & Kleinschmidt, A. Anterior insula activations in perceptual paradigms: often observed but barely understood. Brain Struct. Funct. 214, 611–622 (2010).

  207. 207.

    Angelaki, D. E., Gu, Y. & DeAngelis, G. C. Multisensory integration: psychophysics, neurophysiology, and computation. Curr. Opin. Neurobiol. 19, 452–458 (2009).

Download references

Acknowledgements

We thank K. Toledano for his contribution to the illustrations.

Author information

Affiliations

  1. Hebrew University of Jerusalem, Jerusalem, Israel

    • Shir Atzil
    •  & Isaac Fradkin
  2. Cedars-Sinai Medical Center, Los Angeles, CA, USA

    • Wei Gao
  3. Northeastern University, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    • Lisa Feldman Barrett

Authors

  1. Search for Shir Atzil in:

  2. Search for Wei Gao in:

  3. Search for Isaac Fradkin in:

  4. Search for Lisa Feldman Barrett in:

Contributions

S.A., W.G. I.F. and L.F.B. contributed to writing the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Shir Atzil or Lisa Feldman Barrett.

Supplementary information

  1. Supplementary Information

    Supplementary Table 1

About this article

Publication history

Received

Revised

Accepted

Published

DOI

https://doi.org/10.1038/s41562-018-0384-6