After Piaget

Eduardo Marti

• Publisher: Routledge
• Year: 2017
• ISBN:
  9781351533461
• Edition: 1
• Topic: Psychology
• Subtopic: History & Theory in Psychology
• Index: Psychology

After Piaget proves that Jean Piaget's work is critical for understanding some of the most current proposals in the study of psychological development. It analyzes Piaget's legacy, moving beyond the harsh critiques that have circulated since he lost prominence. It also brings together new developments and research practices that have grown out of Jean Piaget's tradition, while providing a retrospective glance into the intellectual atmospheres of different periods at which the contributors encountered Piaget.This book reveals the richness and coherence of the School of Geneva's research during the last decades before Piaget's death. Contributions from scholars who formed part of the School of Geneva during the 1970s and '80s demonstrate Piaget's influence on such diverse fields as infant development, ethnology, neuropsychology, semiotic development, and epistemology. After Piaget is part of Transaction's History and Theory of Psychology series.

1
From Constructivism to Neuroconstructivism: The Activity-Dependent Structuring of the Human Brain

Annette Karmiloff-Smith

Introduction

In philosophy, psychology, and linguistics, the pendulum regularly swings from nativist claims regarding hardwired domain-specificity to empiricist claims regarding domain-general processes that enable learning. Although Piaget was by no means an empiricist, his constructivist theory does embody domain-general mechanisms of change (assimilation, accommodation, and equilibration), purported to apply to all cognitive domains such as number, space, physical causality, social cognition, and language (Piaget, 1936). Several computational modeling approaches to child development are also domain-general in nature. For example, production system modeling (Klahr et al., 1987) has been used to account for children’s problem-solving across a wide variety of domains (Klahr, 1992, 2000; Klahr and Dunbar, 1989).

While strongly supporting Piaget’s view that infants are active participants in their own learning and that cognitive structures are emergent and not innately specified (Piaget, 1936, 1966), I hereby propose a different view from the domain-general or domain-specific approaches—a domain-relevant view of progressive change—which argues that the brain starts out with a number of basic-level biases, each of which is somewhat more relevant to the processing of certain kinds of input over others and which become domain-specific over time through neuronal competition and a process of gradual modularization (Elman et al., 1996; Karmiloff-Smith, 1992, 1998). But first, let us briefly examine one of the most influential approaches to infant cognition in the literature over recent decades.

Nativist Approaches to Cognitive and Neural Development

As the popularity of Piagetian approaches to infant development began to wane, the nativist approach arguing for innately specified, cognitive-level core knowledge became particularly influential. At least four arguments are used to support nativist claims. First, they drew on the field of adult neuropsychology in which patients whose brains had previously developed normally subsequently suffer a brain trauma and end up with a pattern of relatively dissociated impairments, for example, cases of agrammatism, prosopagnosia, or agnosia. This, theorists argue, indicates that the brain is composed of independently functioning, domain-specific modules (Baron-Cohen, 1998; Butterworth, 2005; Duchaine, 2000; Gopnik, 1997; Temple, 1997; Van der Lely, 2005). The second argument emanated from a version of evolutionary psychology, which maintains that the human brain has evolved into the equivalent of a Swiss army knife in which each innately-specified module in the newborn brain is exquisitely adapted for a specific, independent function (Barkow et al., 1992; Duchaine et al., 2001). (The analogy ignores the fact that most users of the Swiss army knife actually employ for all purposes only a few of the numerous special-purpose tools their knife possesses!) The third argument was based on the capacities of what is known as the “competent infant,” that is, claims that young infants possess innately specified core knowledge or core principles (e.g., Butterworth, 2005; Carey, 2009; Kinzler and Spelke, 2007; Pinker, 1999; Spelke, 2000; Spelke and Kinzler, 2007, 2009). In nativist accounts, learning was banished from having any explanatory role (Piatelli-Palmerini, 2001). Finally, children with genetic disorders presenting with uneven cognitive profiles and displaying a juxtaposition of scores “in the normal range” in one or more domains alongside serious deficits in others were argued to illustrate the dissociation of general intelligence from independently functioning domains like grammar, number, face processing, and the like. So why should we consider these arguments to be less compelling than they initially seem?

First and foremost, they are all static. They ignore what Piaget deemed to be essential: the developmental history of the organism. Indeed, a crucial component of Piaget’s epistemology focused on the growth of knowledge over ontogenetic time, not a snapshot of knowledge at one specific point in time like birth. Second, those of nativist persuasion tend to disregard everything we now know about the progressive development of the infant brain.

The Dynamics of Infant Brain Development

Just as Piaget stressed the ways in which children build their own cognitive structures, so neuroscientists are increasingly focusing on how the child’s activities sculpt the resulting structure of the brain. The brain is rather like a large, very wrinkled walnut! Yet if its wrinkled layers and folds were to be flattened out to a smooth surface, it would cover a full-sized football pitch. Within all those complicated folds are nearly a billion neurons. In fact, the newborn’s brain contains most of the neurons that it will use throughout life, although research has recently shown that even in adulthood some areas of the brain continue to generate new neurons. By about eight months of age, the infant brain will have about one thousand trillion connections between neurons, which is roughly twice the amount of connections found in adult brains. But this is a temporary difference. With time, the connections that have proven useful will get increasingly strengthened, whereas those which haven’t been active often will be “pruned” or weakened. So very gradually over time, the connections in the brain become increasingly specialized, fine tuned, and more adultlike.

While some macrostructures of the brain, like the overall six-layer structure of cortex, may be under general genetic constraints, most of the microcircuitry of the brain turns out to be the result of complex multilevel interactions over developmental time. Indeed, fine-tuning of functional brain organization is a progressive, activity-dependent process (Kandel et al., 2000). According to Huttenlocher (2002), plasticity itself changes over developmental time, with some mechanisms available throughout the lifetime (increase in synaptic strength, decrease in local inhibition, dendritic sprouting, formation of new synapses, and formation of new neurons), whereas others are only available to the early developing brain (utilization of unspecified labile synapses, competition for synaptic sites, persistence of normally transient connections, and myelination). Moreover, changes in plasticity turn out to be region specific, not general across the brain, suggesting (Thomas, 2003) that there is no such overarching thing as “the brain’s plasticity.” Many structures (e.g., dendrites, axons, and synapses) initially undergo exuberant growth, followed by a period of pruning in which the processing of environmental input gradually moulds the way in which the microstructure of the brain emerges.

In young infants, neural processing tends initially to be diffuse across several regions in both hemispheres, but over developmental time with the continuous processing of inputs, brain activity becomes increasingly restricted to more specific networks in the left (LH) or right hemispheres (RH) (Durston et al., 2006; Johnson, 2001). And this gradual process of modularization over developmental time (Karmiloff-Smith, 1992), as opposed to the notion of built-in modules, improves processing efficiency. A recent study by Minagawa-Kawai et al. (2007) examined language-specific phonemic contrasts in infants from three months to twenty-eight months and found that the onset of activation in different areas of cortex was age-specific. Another study suggests that comprehension of single words moves from bilateral processing between thirteen and seventeen months to left lateralized processing at twenty months (Mills et al., 1997). Like vocabulary development, processing of human faces starts out with bilateral activity, with the brain displaying similar signatures for other stimuli like cars or monkey faces (de Haan et al., 2002; Pascalis et al., 2001). But by the end of the first year, the brain becomes increasingly fine-tuned for processing human faces, with other stimuli displaying different neural signatures, as well as increasing localization for human faces to specific networks in the RH (de Haan et al., 2002; Peelen et al., 2009).

Many questions about the developing brain of course remain to be answered. For example, what explains individual differences across different brain regions? Even the brains of monozygotic twins end up rather different, highlighting the role of gene–environment interactions. Further, we need to know more about how hemispheric differences influence neural change over developmental time. In adults, the RH seems to be implicated in more parallel, coarse-grained, integrative processing, whereas the LH is involved in more serial, fine-grained, predictive processing. How does this develop in children? Is information passage through the corpus callosum always faster from RH to LH than from LH to RH, or does this alter over developmental time? Certainly the thickness of the corpus callosum fibers changes developmentally over a long period of time between infancy and adolescence (Keshavan et al., 2002). Finally, short-range gray matter connectivity is greater in children, while long-range white matter connectivity develops considerably more slowly over time (Huttenlocher, 2002). All of these and other developmental changes in the brain must be taken into account when, for instance, analyzing neuroimaging data over time, because the brain continues to undergo quite major changes even at puberty (Blakemore, 2010; Crone et al., 2008).

Importance of the Brain’s Resting State Functional Connectivity

Since self-organizing processes are a necessary part of the explanation of how the brain changes over developmental time, it is critical to understand the spontaneous neural activity that occurs without external stimuli. In adult neuroscience, a resting state circuit has been identified, comprising a large network of brain regions, associated with task-irrelevant mental processes: precuneus/posterior cingulate cortex, medial prefrontal cortex, and medial, lateral, and inferior parietal cortex. It turns out that more of the brain’s energy is spent on intrinsic rather than evoked activity; in fact that brain is never at rest. Studies point to a high degree of functional connectivity during rest, that is, interregional temporal synchrony (Raichle et al., 2001), indicating that this spontaneous neural activity is not merely random activation.

Spontaneous brain activity during sleep, for instance, plays a critical role in the consolidation of memory, involving redistribution of memory representations from temporary hippocampal storage to neocortical long-term storage sites. The dialogue between neocortex and hippocampus generates sharp-wave ripples and is orchestrated by the <1 Hz EEG slow oscillation during slow-wave sleep. Unlike adults whose sleep patterns involve cycles of slow-wave sleep to rapid eye movement (REM) sleep, young infants fall directly into REM sleep, with the proportion of slow-wave sleep increasing only very progressively over the first years of life (Hill et al., 2007). How this affects the resting state neural processes involved in sleep-related consolidation of learning in children remains to be more fully elucidated.

The importance of developmental changes in resting state brain activity will, I believe, become very prominent in the next few years. Years ago (Karmiloff-Smith, 1986, 1992), I put forward a cognitive-level developmental hypothesis—the representational redescription (RR) hypothesis—postulating that what is specifically human to human intelligence is a process by which task-specific representations stored as procedures in the brain become, via an internally-generated process of RR, domain-general knowledge to the brain. This internal self-organizing process, I argued, is generated by behavioral mastery, not by negative feedback, and allows knowledge relevant to one domain to become transportable to other domains without the need to process new external input. In other words, RR was argued to be an internally-generated process occurring outside the processing of external stimuli. With the current advances in developmental brain imaging, it should be possible to assess the hypothesis by detecting specific networks in cerebral resting state underlying RR.

Gradual Developmental Process of Modularization

Neuroconstructivism, with many epistemological overlaps with Piagetian constructivism but incorporating knowledge of brain structure and function, argues that if the adult brain is in any way modular, it is the product of an emergent developmental process of modularization, not its starting point (Karmiloff-Smith, 1992, 1998, 2006, 2007; Elman et al., 1996; Johnson et al., 2002; Westermann et al., 2007). A crucial error is to conflate the specialized brains of adults, which have developed normally prior to damage in later life, with those of infants and children, which are still in the process of developing (Karmiloff-Smith et al., 2002). To date, there is no evidence to suggest functional specificity of gene expression in the brain, that is, no evidence that individual genes which are expressed in the brain target discrete cortical regions. Rather, gene expression seems to be widespread showing diffuse, large-scale gradients across cortex (Kingsbury and Finlay, 2001). Moreover, genetic mutations contributing to developmental disorders in infants are likely to affect widespread systems within the brain (Karmiloff-Smith, 1998). This does not preclude that the outcome of the dynamic developmental process could end up with some areas being more impaired than others, but this would not be a pattern necessarily apparent at the outset but due to the result of processing demands of certain kinds of inputs to those areas and to differences in synaptogenesis across various cerebral regions (Huttenlocher and Dabholkar, 1997). By contrast, the nativist modular view underestimates the dynamics of the changing patterns of connectivity within and across different brain areas during development. Indeed, the same overt behavior may be subserved by different underlying neural substrates at different ages during development (Karmiloff-Smith, 1998).

In studies of typically developing infants and of those with developmental disorders, researchers have shown how different cortical pathways become increasingly specialized and localized as a result of being recruited for specific tasks over developmental time (Elman et al., 1996; Johnson, 2001). Various areas of the brain start out by competing to process different inputs (Karmiloff-Smith, 1998), because cortical regions initially respond to a wide variety of different stimuli and task situations. In other words, the infant brain displays more widespread activity than the older child or adult brain when processing specific kinds of inputs. With time, however, the developing brain starts to show increasing specialization and localization of function as certain areas win out in the competitive processing. How does neuroconstructivism explain this?

It is important to stress that the neuroconstructivist approach does not imply that the neonate brain is a blank slate with no structure, as empiricists would claim. Nor does it entertain the possibility that just any part of the brain can process any and all inputs. On the contrary, neurconstructivism maintains that the neonate cortex has some regional differentiation in terms of types of neuron, de...

Table of contents

• Cover Page
• Title Page
• Copyright Page
• Contents
• Series Editor’s Preface
• Preface
• Introduction
• Part 1. Beyond Piaget’s Constructivism
• Part 2. From Animal to Infant and Child Development
• Part 3. Semiotic Challenges along Development
• Part 4. Development through Education
• List of Contributors
• Index