Chapter 9

Cranking Up the New Machinery…
The acquisition of skills and ability in early life

  1. General reference books and review articles on the critical period and on child development listed in the notes on the previous chapter again apply here, for the initial conclusions drawn in this Chapter (using my grand-daughters as a platform for reminding the reader of the dimensions of change from infancy to older childhood).
  2. Re my reference to a child raised hearing the language of wolves: There are about a hundred interesting descriptions of feral children raised by monkeys or dogs or wolves, or raised under conditions of profound neglect. A handful of these cases are acceptably well documented. An example for which there is some video that on other grounds appears to have been confirmed is a girl raised with dogs.See or in video, Or for something a little different, see
  3. For a perspective about brain development across the critical period from a non-visual-system brain plasticity perspective, see Merzenich MM (2001) Cortical plasticity contributing to child development. IN: McClelland J, Siegler R, eds. Mechanisms in Cognitive Development, L Ehrlbaum Assoc, Mahway, NJ.
  4. Anatomical studies demonstrating that the “major trunk lines” interconnecting different brain areas are genetically determined were conducted in rich abundance over the decades of the 70’s through 90’s. At the same time, brain imaging studies documenting interconnected cortical zones have shown that strongly connected systems can EMERGE as we learn new skills or abilities, even as adults. (For example, a middle-aged adult like John Corcoran described earlier can acquire a “reading brain”.) Such emergent systems partly expressed by dramatically greater connectional strengths between otherwise-only-weakly or modestly interconnected zones are recorded, for example, in every child or adult who masters reading; learns to juggle; establishes perfect pitch as a music performer; learns to drive a car; masters golf; etc., etc.
  5. An equally large body of studies beginning in earnest more than 50 years ago and extending to the present day have shown that the detailed wiring of the brain expressed by dendritic branching or axonal arbors is elaborated in brains of all ages, by training. These studies were stimulated by studies initiated in the early 1950’s led by the late Mark Rosenzweig and conducted with his (then) students Marian Diamond, David Krech and Edward Bennett first demonstrating that the cortex was thickened by putting a rodent in a “richer” living environment. Later studies (in which the inimitable Dr. Diamond played a key role—but for which there were numerous other contributors) showed that the primary changes in thickness resulted from an elaboration of local cortical wiring; that the areas that were thickened could be manipulated by just what stimulation was provided to the animal; and that these striking physical changes could be recorded in animals of any age. For a more contemporary review, see Mohammed AH et al (2002) Environmental enrichment and the brain. Prog Brain Res 138:109. Many, many studies showing that training elaborated dendrites, axonal arbors and their interconnections in the cortex and elsewhere were conducted from that time forward to the present day (although the predominant technical methods documenting change have continuously evolved).
  6. Thousands of studies have documented different aspects of the progressive development of neurobehavioral abilities in the older fetus, and in the infant and toddler; for an entrée into this literature, see, for example, Moon C et al (2013) Language experienced in utero affect vowel perception after birth: a two-country study. Acta Paediatr 102:156; or, for after birth, see Kuhl P et al. (2000) A Scientist In The Crib: What Early Learning Tells Us About The Mind; or Gopnik A (2010) The Philosophical Baby: What Children’s Mind’s Tell Us About The Meaning Of Life. If you want to develop a perspective about “what can go wrong” in language development before and soon after birth, see (as an entre) Heim S et al (2011) Reduced sensory oscillatory activity during rapid auditory processing as a correlate of language-learning impairment. J Neurolinguistics 24:539. The principle scientist in this study, April Benasich, is one of a handful of investigators who carefully document what is happening in the brains and behaviors of babies—then track these children continuously forward in life, right up to the time they succeed, or fail, in developing (not developing) effective language and reading abilities, or at being a general success (or failure) in school!
  7. For an introduction to the argument that maturation of local cortical networks expressed by growing local response coordination provides the basis for the changes associated with critical period closure (including the up-regulation of myelination), see, for example Hench TK (2004) Critical period regulation Ann Rev Neurosci 27:549; Wake H et al. (2011) Control of local protein synthesis and initial events in myelination by action potentials. Science 333:1647; de Villers-Sidani E et al (2008) Manipulating critical period closure across different sectors of the auditory cortex. Nat Neurosci 11:957; or Maffei L (2002) Plasticity in the visual system: role of neurotrophins and electrical activity. Arch Ital Biol 140:341. Note that the release of neurotrophins, up-regulated as local response coordination grows, induces myelination (see references below).
  8. For an earlier view that has had a powerful influence on studies of child development, and is substantially compatible with a neuroscience-informed perspective about developmental progression, see Jean Piaget’s The Origins of Intelligence in Children (1952) or Equilibration of Cognitive Structures: The Central Problem of Intellectual Development (1985). I personally also enjoyed Conversations with Jean Piaget (1989), in which Piaget was thrown a nice set of softball questions by Jean-Claude Bringuier. Its fun to reflect on the fact that Piaget’s understanding originated in large part from the careful observation of the development of his own children.My neuro-scientific perspective on child development is generally compatible with Piaget’s overall outlook. You can see his perspective reflected by my description, arrived at independently on neurological bases, of the “staged” development of brain systems (which is an established scientific reality), by which the output of cortical areas are not fully empowered before they reach developmental maturity (strong local resolution of inputs, generating strongly correlated local activities which are inherently highly reliable; Piaget’s practice-dependent “consolidation”), or by the obvious progression from more egocentric to more extrocentric (what Piaget called “social-centric”) social cognition operations.You might note that most cognitive psychologists studying these issues have not fully understood the neuroscience of perinatal and postnatal brain development. Partly because of that difference in background, the perspective presented in this book is not completely compatible with views held by the majority of authorities in that field (or by pediatric medical authorities). A marriage of these perspectives shall happen over the next few years. It would be a very good subject for a later book.
  9. One objective and relatively straightforward way to track fundamental cellular and molecular aspects of brain maturation is via the study of gene expression as a function of age. While this field is still in development, it has already generated important insights into the complex series of events that mark normal and abnormal perinatal and post-natal “maturation.” See Sassone-Corsi P and Christen Y (eds.) Epigenetics, Brain and Behavior (2012) for an introduction to this rapidly growing sub-discipline. As we shall relate later, gene expression data are one important basis of our understanding of the specific neurological distortions that require correction in the effective plasticity-based treatment of neurological and psychiatric illness. See, for example,
  10. In animal models, we can dramatically specialize the processing machinery in young brains by manipulating the statistics of environment inputs that the baby is exposed to in the “critical period”. Our own predominant model has been the auditory system, where we and others have created very different functional auditory systems that grow into adulthood as, variably, highly-facile and highly specialized processors of specific statistical forms of early-exposed acoustic inputs, all emerging, through large-scale plasticity, as a simple function of that early exposure to specific sounds—none of which bear any meaning to the baby animal. For example, see (e.g., see Zhang LI et al, 2001, Persistent and specific influences of early acoustic environments on primary auditory cortex. Nat Neuroci 4:1123; Zhang LI et al., 2002, Disruption of primary auditory cortex by synchronous auditory inputs during a critical period. PNAS 99:2309; Chang EF, Merzenich MM, 2003, Environmental noise retards auditory cortical development. Science 300:498; or Nakahara H et al., 2004, Specialization of primary auditory cortex processing by sound exposure in the “critical period”. PNAS 202:7070. In perhaps the most compelling example in this series, we exposed rat pups to the sounds of 40 mammals and birds using a tape loop collected by scientists at a zoo in Zurich. On that tape, there were at least 5 different calls of each animal/bird (e.g., 5 different roars or snorts of lions; 5 calls of a macaw; etc.). With exposure in infancy, the auditory cortex in these rats was magnificently specialized to physiologically represent specific animal vocalizations in a categorical way. A manuscript summarizing these findings is now in press (Bao S, et al (2013) Emergent categorical representation of natural, complex sounds in the early post-natal sound environment. J. Neurosci. Related studies were conducted earlier, using behavioral endpoints, by Patricia Kuhl and colleagues, who showed that monkeys, guinea pigs and quail (to cite examples from distant branches of the evolutionary tree) categorize human speech sounds just as we humans do—presumably because we all share the same exposure-based competitive processes—as did your and my brain.We have also exposed infant rats to a variety of early environmental challenges (simulating repeated ultrasound treatment, exposure to organic environmental toxins, perinatal hypoxia associated with alternative or difficult birthing, heavy metal exposure, raising an animal in the continuous presence of garbled noises, exposure to anti-depressant medicine taken by the mother, etc., etc.) invariably substantially distorting the progression of the brain’s “maturation” across childhood, with those distortions still strongly in place in the brain when the animal grew into adulthood (e.g. see Kenet T et al, 2007, Perinatal exposure to a noncoplanar polychlorinated biphenyl alters tonotopy, receptive fields, and plasticity in rat primary auditory cortex. PNAS 104:7646; Strata F et al, 2010, Perinatal asphyxia affects rat auditory processing: implications for auditory perceptual impairments in neurodevelopmental disorders. PLoS One e15326.doi: 10.1371/journal.pone.0015326; or for an example in a different great sensory system, see Strata F et al, 2004, Effects of sensorimotor restriction and anoxia on gait and motor cortex organization: implications for a rodent model of cerebral palsy. Neuroscience 129:141.
  11. Many studies document different aspects of the maturation in the processes controlling the transition to adult plasticity, but almost all reviews on this subject are (in my view) incomplete because they do not adequately consider the impacts of large-scale changes in the expressions of neuromodulatory receptor sub-types, or changes in the maturation of inhibitory and other processes in modulatory control nuclei (among other factors that I’ll flesh out a little more, later in this narrative).
  12. At “highest” brain levels, the “plasticity switch” that I describe is arguably never turned completely “OFF”. Thus, for example, scientists have shown a capacity for plastic change at these levels and demonstrate “priming” effects for stimuli that a human subject is exposed to, even when they never (or minimally) rise to the level of conscious perception. At the same time, hundreds of thousands of non-attended, behaviorally-irrelevant stimuli can be delivered to a non-attending brain, without showing ANY impact at the lower cortical levels of our great brain systems. (For an entry into this literature, I liked the description of the way that your brain works to use all kinds of information that it’s recording without you even knowing about it, to push our thoughts and conclusions around within our skulls every which way, provided by my former research fellow Dean Buonomano in Brain Bugs: How the Brain’s Flaws Shape Our Lives (2011)). On the other hand, when I AM in a “learning mode,” it is very easy to remodel brain representations at EVERY system level—in the right (a strong) behavioral context, with only a small number of the same stimuli. For review, e.g., see Merzenich MM and DeCharms RC (1996) Neural representations, experience and change. In: The Mind-Brain Continuum, MIT Press, Boston; or Merzenich MM, Jenkins WM (1993) Cortical representations of learned behaviors. In: Memory Concepts, Elsevier, Amsterdam.
  13. One way that we have shown the selective power of brain plasticity processes in adults was to train an animal to make a distinction about either a) the loudness or b) the pitch of sound stimuli, with the same stimuli presented in training independent of whether or not the animal was behaviorally seeking a sound with a specific pitch, or a specific loudness. If an animal was listening for and rewarded for marking the occurrence of the presentation of a sound of a specific pitch, dramatic remodeling of their neurological representation of sound frequency was recorded in their brain; the cortical representation of loudness was unaltered. If an animal was listening for and rewarded for detecting the occurrence of stimuli of a specific loudness, again under challenging conditions, dramatic remodeling of sound loudness representation were recorded In their brain. Pitch representations are unaltered. Again, in both experiments, delivered stimuli at the end stages of training were identical in pitch and loudness, for both series of animals. Thus, even down to the level of the representation of one of the most elemental parametric details of listening (loudness, vs pitch), the attending, adult brain can be SELECTIVELY plastic. See Polley DB et al (2006) Perceptual learning directs auditory cortical map reorganization through top-down influences. J Neurosci 26:4970.Note that designing tasks that engage these top-down working memory processes in ways that assure that the trainee sustain a clear mental construct of the stimulus goals and performance requirements of the task at hand is a key for efficiently driving selective plasticity (see Chapter 31, and
  14. We’ll further consider the processes controlling “adult” plasticity in the next Chapter. There, we’ll provide references that begin to describe (alas, in a limited way) the actions of dopamine, acetylcholine & noradrenaline.
  15. “Teach a monkey….”: see Miyashita Y (1988) Neuronal correlate of visual associative long-term memory in the primate temporal cortex. Nature 335:817; or more contemporarily, Susuki W, Tanaka K (2011) Monotonic neuronal tuning in the monkey inferotemporal cortex through long-term learning of fine shape discrimination. Eur J Neurosci 33:748.
  16. “Teach a kid… to play the violin…” see, e.g., Elbert T et al (1995) Increased cortical representation of the fingers of the left hand in string players. Science 270:305; Herholz SC, Zatorre RJ (2012) Musical training as a framework for brain plasticity: behavior, function, and structure. Neuron 76:486. Schlaug G (2001) The brain of musicians. A model for functional and structural adaptation. Ann NY Acad Sci 930:281. After we conducted studies in the mid-1990’s showing that we could induce hand dystonias by training, my research colleague Nancy Byl initiated before-vs-after training imaging studies in professional musicians with and without disabled hands. She found that the hand representations in woodwind player were both enormous—in contradistinction to those in a violinist, violist or cellist, where only the fingering hand was enlarged; and that the anterior face and tongue representations of flutists were massive, and very elegantly elaborated. For you flute players out there, perhaps you did not know this about yourselves! For the rest of you, think about the innumerable ways in which YOUR brain has been altered by how you engage it, as a human specialist!
  17. Scientists have documented the numbers or percentages of synaptic connections that are remodeled within a cortical zone engaged in behavioral training; the magnitude of physical and functional changes is staggering. See, for example, Buchs PA, Muller D (1996) Induction of long-term potentiation is associated with major ultrastructural changes in activated synapses. PNAS 93:8040; Geinesman Y (2000) Remodeling of hippocampal synapses after hippocampus-dependent associative learning. J Comp Neurol 415:49; Kleim JA et al (2002) Motor learning-dependent synaptogenesis is localized to functionally reorganized motor cortex. Neurobiol Learn Mem 77:63; Cheetham CE et al, Pansynaptic enlargement at adult cortical connections strengthened by experience. Cereb Cortex 31 (epub ahead of print); among many others. Note that there were many forerunner studies conducted from the 1950’s onward showing changes in dendritic and axonal arbors and dendritic spines attributable to new experiences and learning. For references that can get you back to this important era first documenting physical changes in learning brains, again see Mohammed AH et al (2002) Environmental enrichment and the brain. Prog Brain Res 138:109.
  18. The engagement of reward systems by hedonic feedback, and by the brain’s evaluation of success in goal achievement is described in hundreds of studies. For example, see reviews by Wolfram Schultz: Multiple dopamine functions at different time courses. Ann Rev Neurosci 30:259, 2007; or Behavioral dopamine signals. Trends Neurosci 30:203, 2007; or Subjective neuronal coding of reward: temporal value discounting and risk. Eur J Neurosci 31:2124. There’s more to the story then this, but here’s a place to start.
  19. On “learning how to learn”, and on differences in child-parent interactions for South and East Asian vs North American or European children, see Kuwabara M, Smith LB (2012) Cross-cultural differences in cognitive development; attention to relations and objects. J Exp Child Psychol 113:20; Spencer-Rogers et al (2010) Differences in expectations of change and tolerance for contradiction: a decade of empirical research. Per Soc Psychol Rev 24:296; or Tang YY, Liu Y (2009) Numbers in the cultural brain. Prog Brain Res 178:151.Many scientists have documented striking individual subject differences between individuals working at the same behavioral tasks, with those differences reflecting very different “learning styles.” For example, in studies documenting brain responses in cued visual or auditory tasks in which stimuli predictably activated brain locations or systems, my UCSF colleague Greg Simpson and colleagues recorded enormous inter-subject neurological variability in different otherwise-high-performing individuals. All were employees (students, faculty, research fellows) at UC San Francisco research laboratories. What otherwise distinguished them? Some were engineering/mathematics oriented; others had prodigious object/fact memories (mostly medicos); still others were more artistic and imaginative; but ALL were succeeding in life, with brains that operated in the same task with very big individual-by-individual attention & learning-strategy performance differences. One size, in attention modulation, does NOT fit all! Such studies suggest that there is NOT an “ideal” neurological problem-solving strategy; there appear (for individuals who develop different brain-using strengths) to be SEVERAL (perhaps many) “ideals”. Of course we also know that there are an equivalent number of less effective—and sometimes almost-worthless—learning styles.]
  20. For a history of cleft palate treatments, and the de-stigmatization of affected children by early treatment, see or from the Cleft Palate Foundation
  21. Since the appreciation that normal language and cognitive abilities can more often be assured if the cleft palate is corrected before a child’s first birthday (some surgeons argue that the surgery should be undertaken before the infant tries to talk), the lives of millions of children in the world have been very dramatically improved, for the better.