1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Developmental Psychology
  4. Volume 1, 2019
  5. Article

Annual Review of Developmental Psychology

Volume 1, 2019

Brain Plasticity in Human Lifespan Development: The Exploration–Selection–Refinement Model

Abstract

Plasticity can be defined as the brain's capacity to achieve lasting structural changes in response to environmental demands that are not fully met by the organism's current functional capacity. Plasticity is triggered when experiential forces interact with genetic programs in the maturation of species-common functions (e.g., vision), but it is also required for less universal forms of learning that sculpt individuals into unique members of their species. Hence, delineating the mechanisms that regulate plasticity is critical for understanding human ontogeny. Nevertheless, mechanisms of plasticity in the human brain and their relations to individual differences in learning and lifespan development are not well understood. Drawing on animal models, developmental theory, and concepts from reinforcement learning, we introduce the exploration–selection–refinement (ESR) model of human brain plasticity. According to this model, neuronal microcircuits potentially capable of implementing the computations needed for executing a task are, early in learning, widely probed and therefore structurally altered. This phase of exploration is followed by phases of experience-dependent selection and refinement of reinforced microcircuits and the concomitant gradual elimination of novel structures associated with unselected circuits. The ESR model makes a number of predictions that are testable in humans and has implications for the study of individual differences in lifespan development.

Keyword(s): development, exploration, lifespan, plasticity, reinforcement learning, transfer
Loading

Article metrics loading...

/content/journals/10.1146/annurev-devpsych-121318-085229
2019-12-15
2024-04-20
Loading full text...

Full text loading...

/deliver/fulltext/devpsych/1/1/annurev-devpsych-121318-085229.html?itemId=/content/journals/10.1146/annurev-devpsych-121318-085229&mimeType=html&fmt=ahah

Literature Cited

  1. Bäckman L, Lindenberger U, Li S-C, Nyberg L 2010. Linking cognitive aging to alterations in dopamine neurotransmitter functioning: recent data and future avenues. Neurosci. Biobehav. Rev. 34:670–77
     [Google Scholar]
  2. Baltes PB. 1997. On the incomplete architecture of human ontogeny: selection, optimization and compensation as foundation of developmental theory. Am. Psychol. 52:366–80
     [Google Scholar]
  3. Baltes PB, Kliegl R. 1992. Further testing of limits of cognitive plasticity: negative age differences in a mnemonic skill are robust. Dev. Psychol. 28:121–25
     [Google Scholar]
  4. Baltes PB, Kliegl R, Dittmann-Kohli F 1988. On the locus of training gains in research on the plasticity of fluid intelligence in old age. J. Educ. Psychol. 80:392–400
     [Google Scholar]
  5. Baltes PB, Lindenberger U, Staudinger UM 2006. Life span theory in developmental psychology. Handbook of Child Psychology: Theoretical Models of Human Development W Damon, RM Lerner 569–664 New York: Wiley
     [Google Scholar]
  6. Barkat TR, Polley DB, Hensch TK 2011. A critical period for auditory thalamocortical connectivity. Nat. Neurosci. 14:1189–94
     [Google Scholar]
  7. Bassett DS, Yang M, Wymbs NF, Grafton ST 2015. Learning-induced autonomy of sensorimotor systems. Nat. Neurosci. 18:744–51
     [Google Scholar]
  8. Bavelier D, Levi DM, Li RW, Dan Y, Hensch TK 2010. Removing brakes on adult brain plasticity: from molecular to behavioral interventions. J. Neurosci. 30:14964–71
     [Google Scholar]
  9. Beam CR, Turkheimer E. 2013. Phenotype–environment correlations in longitudinal twin models. Dev. Psychopathol. 25:7–16
     [Google Scholar]
  10. Bediou B, Adams DM, Mayer RE, Tipton E, Green CS, Bavelier D 2018. Meta-analysis of action video game impact on perceptual, attentional, and cognitive skills. Psychol. Bull. 144:77–110
     [Google Scholar]
  11. Bellander M, Bäckman L, Liu T, Schjeide B-MM, Bertram L et al. 2015. Lower baseline performance but greater plasticity of working memory for carriers of the val allele of the COMT Val158Met polymorphism. Neuropsychology 29:247–54
     [Google Scholar]
  12. Blagosklonny MV, Hall MN. 2009. Growth and aging: a common molecular mechanism. Aging 1:357–62
     [Google Scholar]
  13. Blair C, Kuzawa CW, Willoughby MT 2020. The development of executive function in early childhood is inversely related to change in body mass index: evidence for an energetic tradeoff?. Dev. Sci. 23:e12860
     [Google Scholar]
  14. Bourgeois JP. 1997. Synaptogenesis, heterochrony and epigenesis in the mammalian neocortex. Acta Paediatr 422:27–33
     [Google Scholar]
  15. Brandmaier AM, von Oertzen T, Ghisletta P, Lindenberger U, Hertzog C 2018a. Precision, reliability and effect size of slope variance in latent growth curve models: implications for statistical power analysis. Front. Psychol. 9:294
     [Google Scholar]
  16. Brandmaier AM, Wenger E, Bodammer NC, Kühn S, Raz N, Lindenberger U 2018b. Assessing reliability in neuroimaging research through intra-class effect decomposition (ICED). eLife 7:e35718
     [Google Scholar]
  17. Brehmer Y, Li S-C, Müller V, Oertzen TV, Lindenberger U 2007. Memory plasticity across the life span: uncovering children's latent potential. Dev. Psychol. 43:465–78
     [Google Scholar]
  18. Brehmer Y, Li S-C, Straube B, Stoll G, Oertzen TV et al. 2008. Comparing memory skill maintenance across the lifespan: preservation in adults, increase in children. Psychol. Aging 23:227–38
     [Google Scholar]
  19. Butkovíc A, Ullén F, Mosing MA 2015. Personality and related traits as predictors of music practice: underlying environmental and genetic influences. Personal. Individ. Differ. 74:133–38
     [Google Scholar]
  20. Cabeza R, Albert M, Belleville S, Craik FIM, Duarte A et al. 2018. Maintenance, reserve and compensation: the cognitive neuroscience of healthy ageing. Nat. Rev. Neurosci. 19:701–10
     [Google Scholar]
  21. Cahill LS, Steadman PE, Jones CE, Laliberté CL, Dazai J et al. 2015. MRI-detectable changes in mouse brain structure induced by voluntary exercise. NeuroImage 113:175–83
     [Google Scholar]
  22. Changeux J-P, Dehaene S. 1989. Neuronal models of cognitive functions. Cognition 33:63–109
     [Google Scholar]
  23. Chen SX, Kim AN, Peters AJ, Komiyama T 2015. Subtype-specific plasticity of inhibitory circuits in motor cortex during motor learning. Nat. Neurosci. 18:1109–15
     [Google Scholar]
  24. Church JA, Bunge SA, Petersen SE, Schlaggar BL 2017. Preparatory engagement of cognitive control networks increases late in childhood. Cereb. Cortex 27:2139–53
     [Google Scholar]
  25. Clopath C, Bonhoeffer T, Hübener M, Rose T 2017. Variance and invariance of neuronal long-term representations. Philos. Trans. R. Soc. B 372:20160161
     [Google Scholar]
  26. Condé F, Lund JS, Lewis DA 1996. The hierarchical development of monkey visual cortical regions as revealed by the maturation of parvalbumin-immunoreactive neurons. Brain Res. Dev. 96:261–76
     [Google Scholar]
  27. Dahlin E, Stigsdotter Neely A, Larsson A, Bäckman L, Nyberg L 2008. Transfer of learning after updating training mediated by the striatum. Science 320:1510–12
     [Google Scholar]
  28. Daw ND, O'Doherty JP, Dayan P, Seymour B, Dolan RJ 2006. Cortical substrates for exploratory decisions in humans. Nature 441:876–79
     [Google Scholar]
  29. Dayan P, Daw ND. 2008. Decision theory, reinforcement learning, and the brain. Cogn. Affect. Behav. Neurosci. 8:429–53
     [Google Scholar]
  30. Decker JH, Otto AR, Daw ND, Hartley CA 2016. From creatures of habit to goal-directed learners: tracking the developmental emergence of model-based reinforcement learning. Psychol. Sci. 27:848–58
     [Google Scholar]
  31. Dhawale AK, Smith MA, Ölveczky BP 2017. The role of variability in motor learning. Annu. Rev. Neurosci. 40:479–98
     [Google Scholar]
  32. Dolan RJ, Dayan P. 2013. Goals and habits in the brain. Neuron 80:312–25
     [Google Scholar]
  33. Donato F, Rompani SB, Caroni P 2013. Parvalbumin-expressing basket-cell network plasticity induced by experience regulates adult learning. Nature 504:272–76
     [Google Scholar]
  34. Dong WK, Greenough WT. 2004. Plasticity of nonneuronal brain tissue: roles in developmental disorders. Ment. Retard. Dev. Disabil. Res. Rev. 10:85–90
     [Google Scholar]
  35. Donner Y, Hardy JL. 2015. Piecewise power laws in individual learning curves. Psychon. Bull. Rev. 22:1308–19
     [Google Scholar]
  36. Doyon J, Benali H. 2005. Reorganization and plasticity in the adult brain during learning of motor skills. Curr. Opin. Neurobiol. 15:161–67
     [Google Scholar]
  37. Draganski B, Gaser C, Busch V, Schuierer G, Bogdahn U, May A 2004. Changes in grey matter induced by training. Nature 427:311–12
     [Google Scholar]
  38. Draganski B, May A. 2008. Training-induced structural changes in the adult human brain. Behav. Brain Res. 192:137–42
     [Google Scholar]
  39. Driemeyer J, Boyke J, Gaser C, Buchel C, May A 2008. Changes in gray matter induced by learning—revisited. PLOS ONE 3:e2669
     [Google Scholar]
  40. Edelman GM. 1987. Neural Darwinism: The Theory of Neuronal Group Selection New York: Basic
  41. Elbert T, Pantev C, Wienbruch C, Rockstroh B, Taub E 1995. Increased cortical representation of the fingers of the left hand in string players. Science 270:305–7
     [Google Scholar]
  42. Elman JL, Bates EA, Johnson MH, Karmiloff-Smith A, Parisi D, Plunkett K 1996. Rethinking Innateness: A Connectionist Perspective on Development Cambridge, MA: MIT Press
  43. Ericsson KA, Simon HA. 1993. Protocol Analysis: Verbal Reports as Data Cambridge, MA: Bradford/MIT Press Revis. ed .
  44. Fields RD, Dutta DJ. 2019. Treadmilling model for plasticity of the myelin sheath. Trends Neurosci 42:443–47
     [Google Scholar]
  45. Forstmann BU, Anwander A, Schäfer A, Neumann J, Brown S et al. 2010. Cortico-striatal connections predict control over speed and accuracy in perceptual decision making. PNAS 107:15916–20
     [Google Scholar]
  46. Fox SE, Levitt P, Nelson CA 2010. How the timing and quality of early experiences influence the development of brain architecture. Child Dev 81:28–40
     [Google Scholar]
  47. Freund J, Brandmaier AM, Lewejohann L, Kirste I, Kritzler M et al. 2013. Emergence of individuality in genetically identical mice. Science 340:756–59
     [Google Scholar]
  48. Fries P. 2015. Rhythms for cognition: communication through coherence. Neuron 88:220–35
     [Google Scholar]
  49. Fries P, Reynolds JH, Rorie AE, Desimone R 2001. Modulation of oscillatory neuronal synchronization by selective visual attention. Science 291:1560–63
     [Google Scholar]
  50. Fu M, Yu X, Lu J, Zuo Y 2012. Repetitive motor learning induces coordinated formation of clustered dendritic spines in vivo. Nature 483:92–95
     [Google Scholar]
  51. Fu M, Zuo Y. 2011. Experience-dependent structural plasticity in the cortex. Trends Neurosci 34:177–87
     [Google Scholar]
  52. Garrett DD, Epp SM, Perry A, Lindenberger U 2018. Local temporal variability reflects functional integration in the human brain. NeuroImage 183:776–87
     [Google Scholar]
  53. Garrett DD, Samanez-Larkin GR, MacDonald SWS, Lindenberger U, McIntosh AR, Grady CL 2013. Moment-to-moment brain signal variability: a next frontier in human brain mapping?. Neurosci. Biobehav. Rev. 37:610–24
     [Google Scholar]
  54. Gdalyahu A, Tring E, Polack P-O, Gruver R, Golshani P et al. 2012. Associative fear learning enhances sparse network coding in primary sensory cortex. Neuron 75:121–32
     [Google Scholar]
  55. Geary DC. 2006. Development of mathematical understanding. Handbook of Child Psychology: Cognition, Perception, and Language D Kuhn, RS Siegler 777–810 Hoboken, NJ: Wiley
     [Google Scholar]
  56. Gelinas JN, Baillet S, Bertrand O, Galván A, Kolling T et al. 2018. Late adolescence: critical transitions into adulthood. Emergent Brain Dynamics: Prebirth to Adolescence AA Benasich, U Ribary 243–64 Cambridge, MA: MIT Press
     [Google Scholar]
  57. Gervain J, Vines BW, Chen LM, Seo RJ, Hensch TK et al. 2013. Valproate reopens critical-period learning of absolute pitch. Front. Syst. Neurosci. 7:102
     [Google Scholar]
  58. Gordon EM, Laumann TO, Gilmore AW, Newbold DJ, Greene DJ et al. 2017. Precision functional mapping of individual human brains. Neuron 95:791–807
     [Google Scholar]
  59. Gottlieb G. 1998. Normally occurring environmental and behavioral influences on gene activity: from central dogma to probabilistic epigenesis. Psychol. Rev. 105:792–802
     [Google Scholar]
  60. Greenough WT, Black JE, Wallace CS 1987. Experience and brain development. Child Dev 58:539–59
     [Google Scholar]
  61. Guerreiro MJS, Putzar L, Röder B 2016. Persisting cross-modal changes in sight-recovery individuals modulate visual perception. Curr. Biol. 26:3096–100
     [Google Scholar]
  62. Guo J-Z, Graves AR, Guo WW, Zheng J, Lee A et al. 2015. Cortex commands the performance of skilled movement. eLife 4:e10774
     [Google Scholar]
  63. Hayashi-Takagi A, Yagishita S, Nakamura M, Shirai F, Wu YI et al. 2015. Labelling and optical erasure of synaptic memory traces in the motor cortex. Nature 525:333–35
     [Google Scholar]
  64. Hensch TK. 2004. Critical period regulation. Annu. Rev. Neurosci. 27:549–79
     [Google Scholar]
  65. Hilgard J, Sala G, Boot WR, Simons DJ 2019. Overestimation of action-game training effects: publication bias and salami slicing. Collabra Psychol 5:30
     [Google Scholar]
  66. Hofer SB, Bonhoeffer T. 2010. Dendritic spines: the stuff that memories are made of?. Curr. Biol. 20:R157–59
     [Google Scholar]
  67. Hofer SB, Mrsic-Flogel TD, Bonhoeffer T, Hübener M 2009. Experience leaves a lasting structural trace in cortical circuits. Nature 457:313–18
     [Google Scholar]
  68. Holtmaat A, Svoboda K. 2009. Experience-dependent structural synaptic plasticity in the mammalian brain. Nat. Rev. Neurosci. 10:647–58
     [Google Scholar]
  69. Hübener M, Bonhoeffer T 2010. Searching for engrams. Neuron 67:363–71
     [Google Scholar]
  70. Hübener M, Bonhoeffer T. 2014. Neuronal plasticity: beyond the critical period. Cell 159:727–37
     [Google Scholar]
  71. Huttenlocher PR, Dabholkar AS. 1997. Regional differences in synaptogenesis in human cerebral cortex. J. Comp. Neurol. 387:167–78
     [Google Scholar]
  72. Kaller MS, Lazari A, Blanco-Duque C, Sampaio-Baptista C, Johansen-Berg H 2017. Myelin plasticity and behaviour—connecting the dots. Curr. Opin. Neurobiol. 47:86–92
     [Google Scholar]
  73. Karch JD, Filevich E, Wenger E, Lisofsky N, Becker M et al. 2019. Identifying predictors of within-person variance in MRI-based brain volume estimates. NeuroImage 200:575–89
     [Google Scholar]
  74. Kassem MS, Lagopoulos J, Stait-Gardner T, Price WS, Chohan TW et al. 2013. Stress-induced grey matter loss determined by MRI is primarily due to loss of dendrites and their synapses. Mol. Neurobiol. 47:645–61
     [Google Scholar]
  75. Keresztes A, Ngo CT, Lindenberger U, Werkle-Bergner M, Newcombe NS 2018. Hippocampal maturation drives memory from generalization to specificity. Trends Cogn. Sci. 22:676–86
     [Google Scholar]
  76. Kievit RA, Brandmaier AM, Ziegler G, van Harmelen AL, de Mooij SMM et al. 2018. Developmental cognitive neuroscience using latent change score models: a tutorial and applications. Dev. Cogn. Neurosci. 33:99–117
     [Google Scholar]
  77. Kilgard MP. 2012. Harnessing plasticity to understand learning and treat disease. Trends Neurosci 36:715–22
     [Google Scholar]
  78. Kim H, Ahrlund-Richter S, Wang X, Deisseroth K, Carlen M 2016. Prefrontal parvalbumin neurons in control of attention. Cell 164:208–18
     [Google Scholar]
  79. Kinsbourne M, Hicks RE. 1978. Functional cerebral space: a model for overflow, transfer and interference effects in human performance. A tutorial review Attention and Performance J Requin 345–62 Hillsdale, NJ: Erlbaum
     [Google Scholar]
  80. Kirkwood TBL. 2005. Understanding the odd science of aging. Cell 120:437–47
     [Google Scholar]
  81. Knudsen EI. 1998. Capacity for plasticity in the adult owl auditory system expanded by juvenile experience. Science 279:1531–33
     [Google Scholar]
  82. Koen JD, Rugg MD. 2019. Neural dedifferentiation in the aging brain. Trends Cogn. Sci. 23:547–59
     [Google Scholar]
  83. Krakauer JW, Ghazanfar AA, Gomez-Marin A, MacIver MA, Poeppel D 2017. Neuroscience needs behavior: correcting a reductionist bias. Neuron 93:480–90
     [Google Scholar]
  84. Kriegeskorte N, Kievit RA. 2013. Representational geometry: integrating cognition, computation, and the brain. Trends Cogn. Sci. 17:401–12
     [Google Scholar]
  85. Kühn S, Gleich T, Lorenz RC, Lindenberger U, Gallinat J 2014. Playing Super Mario induces structural brain plasticity: grey matter changes resulting from training with a commercial video game. Mol. Psychiatry 19:265–71
     [Google Scholar]
  86. Kühn S, Lindenberger U. 2016. Research on human plasticity in adulthood: a lifespan agenda. Handbook of the Psychology of Aging KW Schaie, SL Willis 105–23 San Diego, CA: Elsevier, 8th ed..
     [Google Scholar]
  87. Kühn S, Schmiedek F, Noack H, Wenger E, Bodammer NC et al. 2013. The dynamics of change in striatal activity following updating training. Hum. Brain Mapp. 34:1530–41
     [Google Scholar]
  88. Kuzawa CW, Chugani HT, Grossman LI, Lipovich L, Muzik O et al. 2014. Metabolic costs and evolutionary implications of human brain development. PNAS 111:13010–15
     [Google Scholar]
  89. Lensjø KK, Lepperod ME, Dick G, Hafting T, Fyhn M 2017. Removal of perineuronal nets unlocks juvenile plasticity through network mechanisms of decreased inhibition and increased gamma activity. J. Neurosci. 37:1269–83
     [Google Scholar]
  90. Lerch JP, van der Kouwe AJW, Raznahan A, Paus T, Johansen-Berg H et al. 2017. Studying neuroanatomy using MRI. Nat. Neurosci. 20:314–26
     [Google Scholar]
  91. Lerch JP, Yiu AP, Martinez-Canabal A, Pekar T, Bohbot VD et al. 2011. Maze training in mice induces MRI-detectable brain shape changes specific to the type of learning. NeuroImage 54:2086–95
     [Google Scholar]
  92. Li S-C. 2003. Biocultural orchestration of developmental plasticity across levels: the interplay of biology and culture in shaping the mind and behavior across the life span. Psychol. Bull. 129:171–94
     [Google Scholar]
  93. Lindenberger U. 2014. Human cognitive aging: corriger la fortune?. Science 346:572–78
     [Google Scholar]
  94. Lindenberger U. 2018. Plasticity beyond early development: hypotheses and questions. Emergent Brain Dynamics: Prebirth to Adolescence AA Benasich, U Ribary 207–23 Cambridge, MA: MIT Press
     [Google Scholar]
  95. Lindenberger U, Burzynska AZ, Nagel IE 2013. Heterogeneity in frontal-lobe aging. Principles of Frontal Lobe Functions DT Stuss, RT Knight 609–27 New York: Oxford Univ. Press
     [Google Scholar]
  96. Lindenberger U, Li S-C, Bäckman L 2006. Delineating brain–behavior mappings across the lifespan: substantive and methodological advances in developmental neuroscience. Neurosci. Biobehav. Rev. 30:713–17
     [Google Scholar]
  97. Lindenberger U, Wenger E, Lövdén M 2017. Towards a stronger science of human plasticity. Nat. Rev. Neurosci. 18:261–62
     [Google Scholar]
  98. Lorenz KZ. 1937. The companion in the bird's world. Auk 54:245–73
     [Google Scholar]
  99. Lövdén M, Bäckman L, Lindenberger U, Schaefer S, Schmiedek F 2010a. A theoretical framework for the study of adult cognitive plasticity. Psychol. Bull. 136:659–76
     [Google Scholar]
  100. Lövdén M, Bodammer NC, Kühn S, Kaufmann J, Schütze H et al. 2010b. Experience-dependent plasticity of white-matter microstructure extends into old age. Neuropsychologia 48:3878–83
     [Google Scholar]
  101. Lövdén M, Brehmer Y, Li S-C, Lindenberger U 2012a. Training-induced compensation versus magnification of individual differences in memory performance. Front. Hum. Neurosci. 6:141
     [Google Scholar]
  102. Lövdén M, Fratiglioni L, Glymour MM, Lindenberger U, Tucker-Drob EM 2020. The role of education in lifespan cognitive development. Psychol. Sci. Public Interest. In press
    [Google Scholar]
  103. Lövdén M, Schaefer S, Noack H, Bodammer NC, Kühn S et al. 2012b. Spatial navigation training protects the hippocampus against age-related changes during early and late adulthood. Neurobiol. Aging 33:620.e9–e22
     [Google Scholar]
  104. Lövdén M, Wenger E, Mårtensson J, Lindenberger U, Bäckman L 2013. Structural brain plasticity in adult learning and development. Neurosci. Biobehav. Rev. 37:2296–310
     [Google Scholar]
  105. Luders E, Narr KL, Thompson PM, Toga AW 2009. Neuroanatomical correlates of intelligence. Intelligence 37:156–63
     [Google Scholar]
  106. Ma L, Wang B, Narayana S, Hazeltine E, Chen X et al. 2010. Changes in regional activity are accompanied with changes in inter-regional connectivity during 4 weeks motor learning. Brain Res 1318:64–76
     [Google Scholar]
  107. Mackey AP, Miller Singley AT, Bunge SA 2013. Intensive reasoning training alters patterns of brain connectivity at rest. J. Neurosci. 33:4796–803
     [Google Scholar]
  108. Makino H, Hwang EJ, Hedrick NG, Komiyama T 2016. Circuit mechanisms of sensorimotor learning. Neuron 92:705–21
     [Google Scholar]
  109. McGee AW, Yang Y, Fischer QS, Daw NW, Strittmatter SM 2005. Experience-driven plasticity of visual cortex limited by myelin and Nogo receptor. Science 309:2222–26
     [Google Scholar]
  110. McNab F, Varrone A, Farde L, Jucaite A, Bystritsky P et al. 2009. Changes in cortical dopamine D1 receptor binding associated with cognitive training. Science 323:800–2
     [Google Scholar]
  111. Medaglia JD, Huang W, Karuza EA, Kelkar A, Thompson-Schill SL et al. 2018. Functional alignment with anatomical networks is associated with cognitive flexibility. Nat. Hum. Behav. 2:156–64
     [Google Scholar]
  112. Melby-Lervåg M, Hulme C. 2016. There is no convincing evidence that working memory training is effective: a reply to Au et al. 2014 and Karbach and Verhaeghen 2014. Psychol. Bull. Rev. 23:324–30
     [Google Scholar]
  113. Melby-Lervåg M, Redick TS, Hulme C 2016. Working memory training does not improve performance on measures of intelligence or other measures of “far transfer”: evidence from a meta-analytic review. Perspect. Psychol. Sci. 11:512–34
     [Google Scholar]
  114. Mery F, Kawecki TJ. 2003. A fitness cost of learning ability in Drosophila melanogaster. . Proc. R. Soc B 270:2465–69
     [Google Scholar]
  115. Meyer D, Bonhoeffer T, Scheuss V 2014. Balance and stability of synaptic structures during synaptic plasticity. Neuron 82:430–43
     [Google Scholar]
  116. Molina-Luna K, Hertler B, Buitrago MM, Luft AR 2008. Motor learning transiently changes cortical somatotopy. NeuroImage 40:1748–54
     [Google Scholar]
  117. Moore JJ, Ravassard PM, Ho D, Acharya L, Kees AL et al. 2017. Dynamics of cortical dendritic membrane potential and spikes in freely behaving rats. Science 355: eaaj1497
    [Google Scholar]
  118. Mosing MA, Madison G, Pedersen NL, Kuja-Halkola R, Ullén F 2014. Practice does not make perfect: no causal effect of music practice on music ability. Psychol. Sci. 25:1795–803
     [Google Scholar]
  119. Newcombe NS. 2011. What is neoconstructivism?. Child Dev. Perspect. 5:157–60
     [Google Scholar]
  120. Newell A, Rosenbloom PS. 1981. Mechanisms of skill acquisition and the law of practice. Cognitive Skills and Their Acquisition JR Anderson 1–56 Hillsdale, NJ: Erlbaum
     [Google Scholar]
  121. Noack H, Lövdén M, Schmiedek F 2014. On the validity and generality of transfer effects in cognitive training research. Psychol. Res. 78:773–89
     [Google Scholar]
  122. Nyberg L, Lindenberger U. 2020. Brain maintenance and cognition in old age. The Cognitive Neurosciences D Poeppel, G Mangun, MS Gazzaniga Cambridge, MA: MIT Press, 6th ed.. In press
    [Google Scholar]
  123. Nyberg L, Lövden M, Riklund K, Lindenberger U, Bäckman L 2012. Memory aging and brain maintenance. Trends Cogn. Sci. 16:292–305
     [Google Scholar]
  124. Oby ER, Golub MD, Hennig JA, Degenhart AD, Tyler-Kabara EC et al. 2019. New neural activity patterns emerge with long-term learning. PNAS 116:15210–15
     [Google Scholar]
  125. Oschwald J, Guye S, Liem F, Rast P, Willis S et al. 2019. Brain structure and cognitive ability in healthy aging: a review on longitudinal correlated change. Rev. Neurosci. In press. https://doi.org/10.1515/revneuro-2018-0096
    [Crossref] [Google Scholar]
  126. Panchanathan K, Frankenhuis WE. 2016. The evolution of sensitive periods in a model of incremental development. Proc. R. Soc. B Biol. Sci. 283:20152439
     [Google Scholar]
  127. Patel Y, Shin J, Gowland PA, Pausova Z, Paus T 2019. Maturation of the human cerebral cortex during adolescence: myelin or dendritic arbor?. Cereb. Cortex 29:3351–62
     [Google Scholar]
  128. Peters AJ, Liu H, Komiyama T 2017. Learning in the rodent motor cortex. Annu. Rev. Neurosci. 40:77–97
     [Google Scholar]
  129. Piaget J. 1980. Introduction. In Les Formes élémentaires de la dialectique J Piaget 9–13 Paris: Gallimard
     [Google Scholar]
  130. Plomin R, DeFries JC, Knopik VS, Neiderhiser JM 2016. Top 10 replicated findings from behavioral genetics. Perspect. Psychol. Sci. 11:3–23
     [Google Scholar]
  131. Poo MM, Pignatelli M, Ryan TJ, Tonegawa S, Bonhoeffer T et al. 2016. What is memory? The present state of the engram. BMC Biol 14:40
     [Google Scholar]
  132. Pruitt DT, Schmid AN, Danaphongse TT, Flanagan KE, Morrison RA et al. 2016. Forelimb training drives transient map reorganization in ipsilateral motor cortex. Behav. Brain Res. 313:10–16
     [Google Scholar]
  133. Putignano E, Lonetti G, Cancedda L, Ratto G, Costa M et al. 2007. Developmental downregulation of histone posttranslational modifications regulates visual cortical plasticity. Neuron 53:747–59
     [Google Scholar]
  134. Quallo MM, Price CJ, Ueno K, Asamizuya T, Cheng K et al. 2009. Gray and white matter changes associated with tool-use learning in macaque monkeys. PNAS 106:18379–84
     [Google Scholar]
  135. Queenan BN, Ryan TJ, Gazzaniga MS, Gallistel CR 2017. On the research of time past: the hunt for the substrate of memory. Ann. N. Y. Acad. Sci. 1396:108–25
     [Google Scholar]
  136. Raz N. 2007. Comment on Greenwood (2007): Which side of plasticity?. Neuropsychology 21:676–77
     [Google Scholar]
  137. Raz N, Daugherty AM. 2018. Pathways to brain aging and their modifiers: Free-Radical-Induced Energetic and Neural Decline in Senescence (FRIENDS) Model—a mini-review. Gerontology 64:49–57
     [Google Scholar]
  138. Raz N, Schmiedek F, Rodrigue KM, Kennedy KM, Lindenberger U, Lövdén M 2013. Differential brain shrinkage over 6 months shows limited association with cognitive practice. Brain Cogn 82:171–80
     [Google Scholar]
  139. Reed A, Riley J, Carraway R, Carrasco A, Perez C et al. 2011. Cortical map plasticity improves learning but is not necessary for improved performance. Neuron 70:121–31
     [Google Scholar]
  140. Rowley CD, Bazin P-L, Tardiff CL, Sehmbi M, Hashim E et al. 2015. Assessing intracortical myelin in the living human brain using myelinated cortical thickness. Front. Neurosci. 9:396
     [Google Scholar]
  141. Scarr S, McCartney K. 1983. How people make their own environment: a theory of genotype → environment effects. Child Dev 54:424–35
     [Google Scholar]
  142. Schaie KW. 1962. A field-theory approach to age changes in cognitive behavior. Vita Humana 5:129–41
     [Google Scholar]
  143. Schmiedek F, Lövdén M, Lindenberger U 2010. Hundred days of cognitive training enhance broad cognitive abilities in adulthood: findings from the COGITO study. Front. Aging Neurosci. 2:27
     [Google Scholar]
  144. Schmiedek F, Lövdén M, Lindenberger U 2014. Younger adults show long-term effects of cognitive training on broad cognitive abilities over two years. Dev. Psychol. 50:2304–10
     [Google Scholar]
  145. Scholz J, Allemang-Grand R, Dazai J, Lerch JP 2015a. Environmental enrichment is associated with rapid volumetric brain changes in adult mice. NeuroImage 109:190–98
     [Google Scholar]
  146. Scholz J, Niibori Y, Frankland PW, Lerch JP 2015b. Rotarod training in mice is associated with changes in brain structure observable with multimodal MRI. NeuroImage 107:182–89
     [Google Scholar]
  147. Sharpe MJ, Stalnaker T, Schuck NW, Killcross S, Schoenbaum G, Niv Y 2019. An integrated model of action selection: distinct modes of cortical control of striatal decision making. Annu. Rev. Psychol. 70:53–76
     [Google Scholar]
  148. Sigal YM, Bae H, Bogart LJ, Hensch TK, Zhuang X 2019. Structural maturation of cortical perineuronal nets and their perforating synapses revealed by superresolution imaging. PNAS 116:7071–76
     [Google Scholar]
  149. Simons DJ, Boot WR, Charness N, Gathercole SE, Chabris CF et al. 2016. Do “brain-training” programs work?. Psychol. Sci. Public Interest 17:103–86
     [Google Scholar]
  150. Skinner BF. 1981. Selection by consequences. Science 213:501–4
     [Google Scholar]
  151. Smarr BL, Jennings KJ, Driscoll JR, Kriegsfeld LJ 2014. A time to remember: the role of circadian clocks in learning and memory. Behav. Neurosci. 128:283–303
     [Google Scholar]
  152. Snyder JS. 2019. Recalibrating the relevance of adult neurogenesis. Trends Neurosci 42:164–78
     [Google Scholar]
  153. Stanley JA, Raz N. 2018. Functional magnetic resonance spectroscopy: the “new” MRS for cognitive neuroscience and psychiatry research. Front. Psychiatry 9:76
     [Google Scholar]
  154. Steele CJ, Zatorre RJ. 2018. Practice makes plasticity. Nat. Neurosci. 21:1645–46
     [Google Scholar]
  155. Syken J, Grandpre T, Kanold PO, Shatz CJ 2006. PirB restricts ocular-dominance plasticity in visual cortex. Science 313:1795–800
     [Google Scholar]
  156. Takahashi H, Funamizu A, Mitsumori Y, Kose H, Kanzaki R 2010. Progressive plasticity of auditory cortex during appetitive operant conditioning. Biosystems 101:37–41
     [Google Scholar]
  157. Takesian AE, Hensch TK. 2013. Balancing plasticity/stability across brain development. Prog. Brain Res. 207:3–34
     [Google Scholar]
  158. Tang E, Giusti C, Baum GL, Gu S, Pollock E et al. 2017. Developmental increases in white matter network controllability support a growing diversity of brain dynamics. Nat. Commun. 8:1252
     [Google Scholar]
  159. Tau GZ, Peterson BS. 2010. Normal development of brain circuits. Neuropsychopharmacology 35:147–68
     [Google Scholar]
  160. Taubert M, Mehnert J, Pleger B, Villringer A 2016. Rapid and specific gray matter changes in M1 induced by balance training. NeuroImage 133:399–407
     [Google Scholar]
  161. Thorndike EL. 1898. Animal Intelligence: An Experimental Study of the Associative Processes in Animals New York: Macmillan
  162. Tonegawa S, Pignatelli M, Roy DS, Ryan TJ 2015. Memory engram storage and retrieval. Curr. Opin. Neurobiol. 35:101–9
     [Google Scholar]
  163. Tucker-Drob EM. 2017. How do individual experiences aggregate to shape personality development?. Eur. J. Personal. 31:529–95
     [Google Scholar]
  164. Uhlhaas PJ, Roux F, Rodriguez E, Rotarska-Jagiela A, Singer W 2009. Neural synchrony and the development of cortical networks. Trends Cogn. Sci. 14:72–80
     [Google Scholar]
  165. Ullén F, Hambrick DZ, Mosing MA 2016. Rethinking expertise: a multifactorial gene–environment interaction model of expert performance. Psychol. Bull. 142:427–46
     [Google Scholar]
  166. Vendetti MS, Bunge S. 2014. Evolutionary and developmental changes in the lateral frontoparetial network: A little goes a long way for higher-level cognition. Neuron 84:906–17
     [Google Scholar]
  167. von Bastian CC, Oberauer K 2014. Effects and mechanisms of working memory training: a review. Psychol. Res. 78:803–20
     [Google Scholar]
  168. Voss MW, Vivar C, Kramer AF, van Praag H 2013. Bridging animal and human models of exercise-induced brain plasticity. Trends Cogn. Sci. 17:525–44
     [Google Scholar]
  169. Waehnert MD, Dinse J, Weiss M, Streicher MN, Waehnert P et al. 2014. Anatomically motivated modeling of cortical laminae. NeuroImage 93:210–20
     [Google Scholar]
  170. Walhovd KB, Lövdén M. 2020. A lifespan perspective on human neurocognitive plasticity. The Cognitive Neurosciences D Poeppel, G Mangun, MS Gazzaniga Cambridge, MA: MIT Press, 6th ed.. In press
    [Google Scholar]
  171. Wenger E, Brozzoli C, Lindenberger U, Lövdén M 2017a. Expansion and renormalization of human brain structure during skill acquisition. Trends Cogn. Sci. 21:930–39
     [Google Scholar]
  172. Wenger E, Kuhn S, Verrel J, Martensson J, Bodammer NC et al. 2017b. Repeated structural imaging reveals nonlinear progression of experience-dependent volume changes in human motor cortex. Cereb. Cortex 27:2911–25
     [Google Scholar]
  173. Werker JF, Hensch TK. 2015. Critical periods in speech perception: new directions. Annu. Rev. Psychol. 66:173–96
     [Google Scholar]
  174. Wiesel TN, Hubel DH. 1963. Single-cell responses in striate cortex of kittens deprived of vision in one eye. J. Neurophysiol. 26:1003–17
     [Google Scholar]
  175. Wiesel TN, Hubel DH. 1965. Extent of recovery from the effects of visual deprivation in kittens. J. Neurophysiol. 28:1060–72
     [Google Scholar]
  176. Wiestler T, Diedrichsen J. 2013. Skill learning strengthens cortical representations of motor sequences. eLife 2:e00801
     [Google Scholar]
  177. Wymbs NF, Grafton ST. 2015. The human motor system supports sequence-specific representations over multiple training-dependent timescales. Cereb. Cortex 25:4213–25
     [Google Scholar]
  178. Xu T, Yu X, Perlik AJ, Tobin WF, Zweig JA et al. 2009. Rapid formation and selective stabilization of synapses for enduring motor memories. Nature 462:915–19
     [Google Scholar]
  179. Yang G, Pan F, Gan WB 2009. Stably maintained dendritic spines are associated with lifelong memories. Nature 462:920–24
     [Google Scholar]
  180. Yotsumoto Y, Watanabe T, Sasaki Y 2008. Different dynamics of performance and brain activation in the time course of perceptual learning. Neuron 57:827–33
     [Google Scholar]
  181. Yuan P, Raz N. 2014. Prefrontal cortex and executive functions in healthy adults: a meta-analysis of structural neuroimaging studies. Neurosci. Biobehav. Rev. 42:180–92
     [Google Scholar]
  182. Zatorre RJ. 2013. Predispositions and plasticity in music and speech learning: neural correlates and implications. Science 342:585–89
     [Google Scholar]
  183. Zatorre RJ, Fields RD, Johansen-Berg H 2012. Plasticity in gray and white: neuroimaging changes in brain structure during learning. Nat. Neurosci. 15:528–36
     [Google Scholar]
  184. Ziegler G, Hauser TU, Moutoussis M, Bullmore ET, Goodyer IM et al. 2019. Compulsivity and impulsivity traits linked to attenuated developmental frontostriatal myelination trajectories. Nat. Neurosci. 22:992–99
     [Google Scholar]
  185. Zinke K, Zeintl M, Rose NS, Putzmann J, Pydde A, Kliegel M 2014. Working memory training and transfer in older adults: effects of age, baseline performance, and training gains. Dev. Psychol. 50:304–15
     [Google Scholar]
/content/journals/10.1146/annurev-devpsych-121318-085229
Loading
Brain Plasticity in Human Lifespan Development: The Exploration–Selection–Refinement Model
Annual Review of Developmental Psychology 1, 197 (2019); https://doi.org/10.1146/annurev-devpsych-121318-085229
/content/journals/10.1146/annurev-devpsych-121318-085229
/content/journals/10.1146/annurev-devpsych-121318-085229
Loading

Data & Media loading...

  • Article Type: Review Article

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error