Developmental Cognitive Neuroscience E-Book

Mark H. Johnson

• Herausgeber: John Wiley & Sons
• Kategorie: Geisteswissenschaft
• Sprache: Englisch

Seitenzahl: 714

Veröffentlichungsjahr: 2015

Beschreibung

Developmental Cognitive Neuroscience, 4th Edition, is a revised and updated edition of the landmark text focusing on the development of brain and behaviour during infancy, childhood, and adolescence.

• Offers a comprehensive introduction to all issues relating to the nature of brain-behaviour relationships and development
• New or greatly expanded coverage of topics such as epigenetics and gene expression, cell migration and stem cells, sleep and learning/memory, socioeconomic status and development of prefrontal cortex function
• Includes a new chapter on educational neuroscience, featuring the latest findings on the application of cognitive neuroscience methods in school-age educational contexts
• Includes a variety of student-friendly features such as chapter-end discussion, practical applications of basic research, and material on recent technological breakthroughs

Leseprobe

CONTENTS

Cover

Title page

List of Figures

List of Tables

Preface to the First Edition

Preface to the Fourth Edition

Acknowledgements

Abbreviations

About the Companion Website

1 The Biology of Change

1.1 Viewpoints on Development

1.2 Analyzing Development

1.3 Why Take a Cognitive Neuroscience Approach to Development?

1.4 Why Take a Developmental Approach to Cognitive Neuroscience?

1.5 The Cause of Developmental Change

1.6 Three Viewpoints on Human Functional Brain Development

1.7 Looking Forward

2 Methods and Populations

2.1 Introduction

2.2 Behavioral and Cognitive Tasks

2.3 Assessing Brain Function in Development

2.4 Observing Brain Structure in Development

2.5 Animal Studies and Genetics

2.6 Developmental Disorders

2.7 Atypically Developing Brains

2.8 Sensory and Environmental Perturbations

2.9 Familial Risk Populations

3 From Gene to Brain

3.1 The History of the Gene

3.2 Principles of Gene Function

3.3 Genetics and Developmental Cognitive Neuroscience

3.4 The Epigenome

3.5 The FOXP2 Gene

4 Building a Brain

4.1 An Overview of Primate Brain Anatomy

4.2 Prenatal Brain Development

4.3 Postnatal Brain Development

4.4 The Development of Cortical Areas: Protomap or Protocortex?

4.5 Cortical Plasticity

4.6 Differential Development of Human Cortex

4.7 Postnatal Brain Development: Adolescence

4.8 Postnatal Brain Development: The Hippocampus and Subcortical Structures

4.9 Neurotransmitters and Neuromodulators

4.10 What Makes a Brain Human?

4.11 General Summary and Conclusions

5 Vision, Orienting, and Attention

5.1 The Development of Vision

5.2 The Development of Visual Orienting

5.3 Saccade Planning

5.4 Visual Attention

5.5 General Summary and Conclusions

6 Perceiving and Acting in a World of Objects

6.1 The Dorsal and Ventral Visual Pathways

6.2 Hidden Objects

6.3 Neural Oscillations and Object Processing

6.4 General Summary and Conclusions

7 Perceiving and Acting in the Social World

7.1 The Social Brain

7.2 Face Recognition

7.3 Filial Imprinting in Chicks

7.4 Brain Development and Face Recognition

7.5 Perceiving and Acting on the Eyes

7.6 Understanding and Predicting the Behavior of Others

7.7 The Atypical Social Brain

7.8 General Summary and Conclusions

8 Learning and Long-Term Memory

8.1 Development of Explicit Memory

8.2 Implicit Memory

8.3 General Summary and Conclusions

9 Language

9.1 Introduction

9.2 Are Some Parts of Cortex Critical for Language Acquisition?

9.3 Neural Basis of Speech Processing in Infants

9.4 Influence of Experience on Brain Language Processing

9.5 Neural Correlates of Typical and Atypical Language Acquisition

9.6 General Summary and Conclusions

10 Prefrontal Cortex, Working Memory, and Decision-Making

10.1 Introduction

10.2 Prefrontal Cortex, Object Permanence, and Working Memory

10.3 Prefrontal Cortex, Social Decision-Making, and Adolescence

10.4 Prefrontal Cortex, Skill Learning, and Interactive Specialization

10.5 General Summary and Conclusions

11 Cerebral Lateralization

11.1 General Summary and Conclusions

12 Educational Neuroscience

12.1 Numeracy

12.2 Literacy

12.3 Domain-General Skills: Executive Functions and Processing Speed

12.4 Dyscalculia and Dyslexia

12.5 General Summary and Conclusions

13 Interactive Specialization

13.1 Three Viewpoints on Human Functional Brain Development

13.2 Interactive Specialization

13.3 Selective Pruning

13.4 Parcellation and Emergent Modularity

13.5 Emerging Networks

13.6 General Summary and Conclusions

14 Toward an Integrated Developmental Cognitive Neuroscience

14.1 Introduction

14.2 Genes and Cognitive Development

14.3 Relations Between Brain Structure and Function in Development

14.4 Neuroconstructivism

14.5 Criticisms of Developmental Cognitive Neuroscience

14.6 Applications of Developmental Cognitive Neuroscience

14.7 Concluding Remarks

References

Supplemental Images

Index

Access to Companion Site

End User License Agreement

List of Tables

Chapter 01

Table 1.1 Levels of interaction between genes and their environment © Mark H. Johnson

Chapter 02

Table 2.1 A thumbnail sketch of some of the major developmental disorders. For the sake of comparison, the table focuses on contrastive aspects of different disorders © Mark H. Johnson

Chapter 05

Table 5.1 Summary of the relation between developing oculomotor pathways and behavior © Mark H. Johnson

Table 5.2 Marker tasks for the development of visual orienting and attention © Mark H. Johnson

Chapter 07

Table 7.1 Stimulus-dependent effects of neurophysiological manipulations

Chapter 13

Table 13.1 Three viewpoints on human functional brain development © Mark H. Johnson

List of Illustrations

Chapter 01

Figure 1.1 Drawings such as this influenced a seventeenth-century school of thought, the “spermists,” who believed that there was a complete preformed person in each male sperm and that development merely consisted of increasing size.

Figure 1.2 A simple three-layered connectionist neural network in which groups of nodes are joined by links. Changes in the strength of links as a result of training are determined by a learning rule.

Figure 1.3 The epigenetic landscape of Waddington (1975).

Chapter 03

Figure 3.1 (A) The basic double helix structure of DNA in which two nucleotide strands coil around each other. (B) Detail showing how the two strands are linked by chemical bonds between the bases of nucleotides. The four bases are thymine, adenine, cytosine, and guanine. Reprinted by permission of the publisher from

The Fundamentals of Brain Development: Integrating Nature and Nurture

by Joan Stiles, pp. 45, 290, Cambridge, Mass.: Harvard University Press, Copyright © 2008 by the President and Fellows of Harvard College.

Figure 3.2 An illustration of the complex causal pathway between a genetic level defect and its consequences for behavior from Fragile-X syndrome.

Chapter 04

Figure 4.1 A simplified schematic diagram which illustrates that, despite its convoluted surface appearance (top), the cerebral cortex is a thin sheet (middle) composed of six layers (bottom). The convolutions in the cortex arise from a combination of growth patterns and the restricted space inside the skull. In general, differences between mammals involve the total area of the cortical sheet, and not its layered structure. Each of the layers possesses certain neuron types and characteristic input and projection patterns (see text).

Figure 4.2 A typical cortical pyramidal cell. The apical dendrite is the long process that extends to the upper layers and may allow the cell to be influenced by other neurons. An axon projects to subcortical regions.

Figure 4.3 A sequence of drawings of the embryonic and fetal development of the human brain. The drawings of brains beneath those of 25–100 days are the same images but drawn to the same scale as those in the row below. The forebrain, midbrain, and hindbrain originate as swellings at the head end of the neural tube. In primates, the convoluted cortex grows to cover the midbrain, hindbrain, and parts of the cerebellum. Prior to birth, neurons are generated in the developing brain at a rate of more than 250,000 per minute.

Figure 4.4 MRI structural scans of a 4-month-old infant (top) and a 12-year-old adolescent (below).

Figure 4.5 A drawing of the cellular structure of the human visual cortex based on Golgi stain preparations from Conel (1939–1967). Reprinted by permission of the publisher from The Postnatal Development of The Human Cerebral Cortex, Volumes I–VIII, by Jesse LeRoy Conel, Cambridge, Mass.: Harvard University Press, Copyright © 1939, 1941, 1947, 1951, 1955, 1959, 1963, 1967 by the President and Fellows of Harvard College. Copyright © renewed 1967, 1969, 1975, 1979, 1983, 1987, 1991.

Figure 4.6 The sequence of axon myelination by an oligodendrocyte. (A–D) show the sequence of initial contact, then engulfing and surrounding the axon, followed by spiraling around the axon to form the final myelin sheath. Reprinted by permission of the publisher from

The Fundamentals of Brain Development: Integrating Nature and Nurture

by Joan Stiles, pp. 45, 290, Cambridge, Mass.: Harvard University Press, Copyright © 2008 by the President and Fellows of Harvard College.

Figure 4.9 Graph showing the development of density of synapses in human primary visual cortex (dotted line: data taken from Huttenlocher, 1990), and resting glucose uptake in the occipital cortex as measured by PET (solid line: data taken from Chugani et al., 1987). ICMRGlc is a measure of the local cerebral metabolic rates for glucose.

Figure 4.12 Cytoarchitectural map of the cerebral cortex. Some of the most important specific areas are as follows. Motor cortex: motor strip, area 4; pre-motor area, area 6; frontal eye fields, area 8. Somatosensory cortex: areas 3, 1, 2. Visual cortex: areas 17, 18, 19. Auditory cortex: areas 41 and 42. Wernicke’s speech area: approximately area 22. Broca’s speech area: approximately area 44 (in the left hemisphere).

Figure 4.13 The radial unit model of Rakic (1987). Radial glial fibers span from the ventricular zone (VZ) to the cortical plate (CP) via a number of regions: the intermediate zone (IZ) and the subplate zone (SP). RG indicates a radial glial fiber, and MN a migrating neuron. Each MN traverses the IZ and SP zones that contain waiting terminals from the thalamic radiation (TR) and corticocortico afferents (CC). As described in the text, after entering the cortical plate, the neurons migrate past their predecessors to the marginal zone (MZ).

Figure 4.14 Patterning of areal units in somatosensory cortex. The pattern of “barrels” in the somatosensory cortex of rodents is an isomorphic representation of the geometric arrangement of vibrissae found on the animal’s face. Similar patterns are present in the brain stem and thalamic nuclei that relay inputs from the face to the barrel cortex.

Figure 4.15 PET images illustrating developmental changes in local cerebral metabolic rates for glucose (ICMRGlc) in the normal human infant with increasing age. Level 1 is a superior section, at the level of the cingulate gyrus. Level 2 is more inferior, at the level of caudate, putamen, and thalamus. Level 3 is an inferior section of the brain, at the level of cerebellum and inferior position of the temporal lobes. Gray scale is proportional to ICMRGlc, with black being highest. Images from all subjects are not shown on the same absolute gray scale of ICMRGlc; instead, images of each subject are shown with the full gray scale to maximize gray scale display of ICMRGlc at each age. (A) In the 5-day-old, ICMRGlc is highest in sensorimotor cortex, thalamus, cerebellar vermis (arrows), and brain stem (not shown). (B, C, D) ICMRGlc gradually increases in parietal, temporal, and calcarine cortices; basal ganglia; and cerebellar cortex (arrows), particularly during the second and third months. (E) In the frontal cortex, ICMRGlc increases first in the lateral prefrontal regions by approximately 6 months. (F) By approximately 8 months, ICMRGlc also increases in the medial aspects of the frontal cortex (arrows), as well as the dorsolateral occipital cortex. (G) By 1 year, the ICMRGlc pattern resembles that of adults (H).

Chapter 05

Figure 5.1 Diagram of the developmental sequence of visual behavior (left of vertical line) and ventral- and dorsal-stream neural systems contributing to this (right of vertical line).

Figure 5.2 Simplified schematic diagram illustrating how projections from the two eyes form ocular dominance columns in the visual cortex. LGN, lateral geniculate nucleus.

Figure 5.3 (A) Afferents from both eyes synapse on the same cells in layer 4, thereby losing information about the eye of origin. (B) Afferents are segregated on the basis of eye origin (R and L), and consequently recipient cells in layer 4 may send their axons to cells outside of that layer so as to synapse on cells that may be disparity-selective.

Figure 5.4 Diagram representing some of the main neural pathways and structures involved in visual orienting and attention. BS, brain stem; LGN, lateral geniculate nucleus; V1, V2, and V4, visual cortical areas; MT, middle temporal area; SC, superior colliculus; SN, substantia nigra; BG, basal ganglia.

Figure 5.5 The oculomotor delayed response task as designed for use with infants. Infant subjects face three computer screens on which brightly colored moving stimuli appear. At the start of each trial, a fixation stimulus appears on the central screen. Once the infant is looking at this stimulus, a cue is briefly flashed up on one of the two side screens. Following the briefly flashed cue, the central stimulus stays on for between 1 and 5 seconds, before presentation of two targets on the side screens. By measuring delayed looks to the cued location prior to the target onset, Gilmore and Johnson (1995) established that infants can retain information about the cued location for several seconds.

Figure 5.6 Grand-average saccade-locked potentials at the midline, parietal electrode in (A) 6-month-old infants, (B) 12-month-old infants, and (C) adults. The vertical bar marks the saccade onset, a spike potential (SP) is evident in adults and 12-month-olds, but not at 6 months.

Figure 5.7 Three types of saccades made by young infants in response to two targets briefly flashed as shown. (A) A “vector summation” saccade in which eye movement is directed between the two targets. (B) A “retinocentric” saccade in which the second saccade is directed to the location corresponding to the retinal error when the flash occurred. (C) An “egocentric” saccade which corresponds to the use of extra-retinal information to plan the second saccade. Between birth and 6 months, infants shift from the first two types of response to the third.

Figure 5.8 Heart-rate-defined phases of sustained attention.

Chapter 06

Figure 6.1 Major routes whereby retinal input reaches the dorsal and ventral streams. The diagram of the brain on the right of the figure shows the approximate routes of the projections from primary visual cortex to posterior parietal and the inferotemporal cortex, respectively. LGNd, lateral geniculate nucleus, pars dorsalis; Pulv, pulvinar; SC, superior colliculus.

Figure 6.2 A diagram showing the object processing model of Mareschal et al. (1999).

Chapter 07

Figure 7.1 Some of the regions involved in the human social brain network.

Figure 7.2 Data showing the extent of newborns’ head and eye turns in following a schematic face, a scrambled face, and a blank (unpatterned) stimulus.

Figure 7.3 A summary of human newborn and model responses to schematic images. The top row represents some of the schematic patterns presented to both newborns and the “retina” of the neural network model. The next two rows illustrate the lateral geniculate nucleus and visual cortex stages of the models processing. The bottom row indicates the output of the model, with the preferred stimuli being b, c, and d. The preferences of the model correspond well to the result obtained with newborn infants.

Figure 7.4 Outline sagittal view of the chick brain showing the main visual pathway to IMM (formerly known as IMHV; HA, hyperstriatum accessorium). There are other routes of visual input to IMM which are not shown in this figure (see Horn, 1985). The brain of a 2-day-old chick is approximately 2 cm long.

Figure 7.5 Schematic illustration of the stimuli that might be optimal for eliciting a face-related preference in human newborns. These hypothetical representations were created by integrating the results from several experiments with newborns.

Figure 7.7 Example of the edited video image illustrating the stimulus for experiment 1 in Farroni et al. (2000). In this trial the stimulus target (the duck) appears on the side incongruent with the direction of gaze.

Figure 7.8 Results of the Farroni et al. 2002preferential looking study with newborns. (A) Mean looking times (and standard error) spent at the two stimulus types. Newborns spent significantly more time looking at the face with direct gaze than looking at the face with averted gaze. (B) Mean number of orientations toward each type of stimulus. (C) Filled triangles indicate reference scores for the direct gaze over the averted gaze for each individual newborn. Open triangles indicate average preference scores.

Chapter 08

Figure 8.3 Memory performance for Beth, Jon, and Kate on the Rey-Osterrieh Complex Figure. Left column shows their normal copying of the figure, while the right column shows how much less they were able to recall after a 40-minute delay compared to control participants.

Chapter 09

Figure 9.1 Some of the key structures involved in language processing. Top left: Schematic view of information flow from posterior sensory areas to frontal response areas through the inferior temporofrontal loop. The shaded regions show where brain damage causes fluent (Wernicke’s) and nonfluent (Broca’s) aphasia. These regions are conceptual rather than anatomical. Bottom left: The Sylvian fissure has been pulled out and down in the direction of the arrows to reveal the insula (I) and the auditory cortex (H, P) on the superior surface of the temporal lobe. The region of the frontal operculum indicated as F5 contains mirror neurons in the monkey. It is thought that these neurons play a crucial role in imitation learning. Right: Enlarged view of Heschl’s gyrus and planum temporal.

Figure 9.2 Cortical areas showing increases in blood oxygenation on fMRI when normal hearing adults read English sentences (top), when congenitally deaf native signers read English sentences (middle), and when congenitally deaf native signers view sentences in their native sign language (American Sign Language).

Chapter 10

Figure 10.3 Summary of the sequence and anatomical distribution of the coherence patterns reported by Thatcher (1992). Lines connecting electrode locations indicate a measure of strong coherence. A microcycle is a developmental sequence that involves a lateral-medial rotation that cycles from the left hemisphere to bilateral to right hemisphere in approximately 4 years. Note the hypothesized involvement of the frontal cortex in the “bilateral” subcycle.

Figure 10.4 (A) Rate of growth of EEG coherence between frontal and posterior lobes during middle childhood (F 7–P 3) (Thatcher, 1992). (B) Rate of growth of working memory (counting span and spatial span) during the same age range.

Chapter 11

Figure 11.1 A summary diagram of the model proposed by Geschwind, Behan, and Galaburda. Arrows between boxes indicate direct causal link.

Chapter 12

Figure 12.1 Examples of experimental tests used to assess (A) nonsymbolic numerical representations, (B) symbolic representations, and (C) number line tasks.

Figure 12.2 A summary over 52 fMRI studies of healthy children and adolescents showing (A) the locations of brain areas involved in numerical abilities (left), (B) reading (left), and (C, D) executive function (left). On the right, the distributions show the number of studies per year of age.

Chapter 13

Figure 13.1 The formation of representations in the cortical matrix model under two different architectural conditions. The left upper panel shows the starting state and the left lower the final state. In the final state, “structured” representations emerge in which stimuli that have features in common tend to be clustered together (spatially aligned). With just a minor change in the architecture of the network (changing the relevant average lengths of intrinsic excitatory and inhibitory links: the right-hand side), nodes in the network fail to form structured clustered representations.

Figure 13.2 An illustration of different kinds of brain connectivity. The right-hand panel shows dense local connectivity without long-range connections. The left panel shows the more optimal arrangement that balances local connectivity with some long-range connections (a “small world” network).

Supplemental Images

Figure 2.1 An illustration of the relative strengths and weaknesses of different functional brain imaging methods used with infants and children. Lloyd-Fox, Blasi and Elwell, 2010.

Figure 2.2 An infant wearing a high-density ERP/EEG system (EGI Geodesic Sensor Net) during a study on the “mirror neuron system”. The sensor net consists of damp sponge contacts that rest gently on the scalp.

Figure 2.3 An infant engaged in an optical imaging (NIRS) study. Light emitters and detectors are incorporated into a cloth head cap.

Figure 2.4 The expansion of myelinated fibers over early postnatal development as revealed by a new structural MRI technique.

Figure 4.7 Resting state networks in a single representative infant. Rows A to E each show one resting state network at three axial sections. Fransson et al, 2007.

Figure 4.8 Figure illustrating the approximate timeline for some of the most important changes in human brain development, including the characteristic rise and fall of synaptic density.

Figure 4.10 A color-coded map of changes in cortical gray matter with development. The maps illustrate regional variations in decreases in gray matter density between the ages of 5 and 20 years. Toga et al, 2006.

Figure 4.11 The brain maps (center panel) show prominent clusters where “superior” and “average” intelligence groups differ significantly in the trajectories of cortical development. The graphs show the developmental trajectories for these regions. The age of peak cortical thickness is arrowed for each of the three groups in each region.

Figure 6.3 Gamma-band EEG activity recorded from infants in the Kaufman, Csibra, and Johnson (2003) experiment. (a) Time-frequency analysis of the average EEG at three electrodes over the right temporal cortex (around T4) during the phase in which the tunnel was lifted showed higher activations when the object should have been below the tunnel. Black asterisks below the maps indicate a significant difference from baseline; red asterisks indicate a significant difference between conditions in the average gamma activity in 200 ms-long bins. (B) A topographical map of the between-condition difference of gamma-band (20–60 Hz) activity during the occlusion-related peak gamma activity (from –400 to –200 ms) revealed a right-temporal focus. Circles signify right-temporal electrode sites.

Figure 7.6 Differential activation for each stimulus category mapped onto an inflated brain: (A) ventral view and (B) a lateral view of the right hemisphere for all three age groups. In contrast to older groups, young children showed no face-selective activation in face-related areas. However, objects and buildings or navigation yielded similar patterns of selective activation at all ages. Abbreviations: FFA, fusiform face area; LO, Lateral occipital object area; OFA, occipital face area; PPA, parahippocampal place area; STS, superior temporal sulcus. Scherf et al, 2007.

Figure 8.1 The medial temporal lobe memory system.

Figure 8.2 (A) Example of a sequence to be imitated. (B) The number of actions and ordered pairs of actions produced by 20-month-old infants in a baseline period, at immediate recall after the sequence is demonstrated, and after a two-week delay. Bauer, 2006.

Figure 9.3 Covert language task: group average fMRI activation in the unaffected and affected members of the KE family. Activated regions are projected onto the surface rendering of a typical 3D individual brain, displayed at a statistical threshold of p < .05, corrected for multiple comparisons. L, left hemisphere; R, right hemisphere. Liégeois et al, 2003.

Figure 10.1 A summary of the superior frontal-intraparietal network involved in the development of visuo-spatial working memory. Regions in red show a correlation between brain activity and development of working memory capacity, and regions in white show a correlation between white matter maturation and development. Klingberg, 2006.

Figure 10.2 Interaction between wakefulness and the linguistic nature of the stimuli. This comparison isolated a right dorsolateral prefrontal region that showed greater activation by forward speech than by backward speech in awake infants, but not in sleeping infants. Dehaene-Lambertz, 2002.

Figure 13.3 Developmental changes in interregional functional connectivity. A graphical representation of developmental changes in functional connectivity along the posterior-anterior and ventral-dorsal axes of the brain highlighting higher sub-cortical connectivity and lower paralimbic connectivity in children compared to young adults. Supekar, 2009.

Guide

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Developmental Cognitive Neuroscience

An Introduction

Fourth Edition

Mark H. Johnson and Michelle de Haan

This edition first published 2015© 2015 John Wiley & Sons, Ltd

Blackwell Publishers Ltd (1e, 1997); Blackwell Publishing Ltd (2e, 2005); John Wiley & Sons, Ltd (3e, 2011)

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Library of Congress Cataloging-in-Publication Data applied for

HB ISBN: 9781118938072PB ISBN: 9781118938089

A catalogue record for this book is available from the British Library.

Cover image: Saloni Krishnan and Fred Dick, Birkbeck, University of London

To our parents, who provided both our nature and our nurture.

List of Figures

Figures listed below without a page number appear in the color plate section. The color plate section appears between pages 138 and 139.

1.1

Seventeenth-century “spermist” drawing

1.2

Simple three-layered connectionist neural network

1.3

The epigenetic landscape of Waddington (1975)

2.1

An illustration of the relative strengths and weaknesses of different functional brain-imaging methods used with infants and children (color plate)

2.2

An infant wearing a high-density ERP/EEG system (EGI Geodesic Sensor Net) during a study on the “mirror neuron system” (color plate)

2.3

An infant engaged in an optical imaging (NIRS) study (color plate)

2.4

The expansion of myelinated fibers over early postnatal development as revealed by a new structural MRI technique (color plate)

3.1

(A) The basic double helix structure of DNA. (B) Detail showing how the two strands are linked by chemical bonds between the bases of nucleotides

3.2

The complex causal pathway between a genetic level defect and its consequences for behavior from Fragile-X syndrome

4.1

Simplified schematic diagram showing cerebral cortex as a thin sheet composed of six layers

4.2

A typical cortical pyramidal cell

4.3

Embryonic and fetal development of the human brain

4.4

MRI structural scans of a 4 month old infant (top) and a 12 year old adolescent (below)

4.5

Cellular structure of the human visual cortex based on Golgi stain preparations

4.6

The sequence of axon myelination by an oligodendrocyte

4.7

Resting state networks in a single representative infant (color plate)

4.8

An approximate timeline for some of the most important changes in human brain development (color plate)

4.9

The development of density of synapses in human primary visual cortex

4.10

A color-coded map of changes in cortical gray matter with development (color plate)

4.11

Trajectories of cortical development in “superior” and “average” intelligence groups (color plate)

4.12

Cytoarchitectural map of the cerebral cortex

4.13

The radial unit model of Rakic (1987)

4.14

Patterning of areal units in the somatosensory cortex

4.15

Developmental changes in local cerebral metabolic rates for glucose (ICMRGlc) in the normal human infant

5.1

Diagram of the developmental sequence of visual behavior and ventral- and dorsal-stream neural systems contributing to this

5.2

Simplified schematic diagram illustrating how projections from the two eyes form ocular dominance columns in the visual cortex

5.3

(A) Afferents from both eyes synapse on the same cells in layer 4. (B) Afferents are segregated on the basis of eye origin

5.4

Diagram representing some of the main neural pathways and structures involved in visual orienting and attention

5.5

The oculomotor delayed response task as designed for use with infants

5.6

Grand-average saccade-locked potentials at Pz in 6-month-old infants, 12-month-old infants, and adults

5.7

Three types of saccades made by young infants in response to two targets briefly flashed as shown

5.8

Heart-rate-defined phases of sustained attention

6.1

Major routes whereby retinal input reaches the dorsal and ventral streams

6.2

A diagram showing the object processing model of Mareschal et al. (1999)

6.3

Gamma-band EEG activity recorded from infants in the Kaufman et al. (2003) experiment (color plate)

7.1

Some of the regions involved in the human social brain network

7.2

Data showing the extent of newborns’ head and eye turns in following a schematic face, a scrambled face, and a blank (unpatterned) stimulus

7.3

Human newborn and model response to schematic images

7.4

Outline sagittal view of the chick brain showing the main visual pathway to IMM

7.5

Schematic illustration of the stimuli that might be optimal for eliciting a face-related preference in human newborns

7.6

Selective activation to face, object, and building stimuli across different age groups (color plate)

7.7

Example of the edited video image illustrating the stimulus for experiment 1 in Farroni et al. (2000)

7.8

Results of the preferential looking study with newborns

8.1

The medial temporal lobe memory system (color plate)

8.2

(A) Example of a sequence to be imitated. (B) Production of actions by 20-month-old infants in a baseline period, at immediate recall after the sequence is demonstrated, and after a 2-week delay (color plate)

8.3

Memory performance for Beth, Jon, and Kate on the Rey-Osterrieh Complex Figure

9.1

Some of the key structures involved in language processing

9.2

Cortical areas showing increases in blood oxygenation on fMRI when normal hearing adults read English sentences, when congenitally deaf native signers read English sentences, and when congenitally deaf native signers view sentences in their native sign language (American Sign Language)

9.3

Covert language task: group average fMRI activation in the unaffected and affected members of the KE family (color plate)

10.1

A summary of the superior frontal-intraparietal network involved in the development of visuo-spatial working memory (color plate)

10.2

Interaction between wakefulness and the linguistic nature of the stimuli (color plate)

10.3

Summary of the sequence and anatomical distribution of the coherence patterns reported by Thatcher (1992)

10.4

(A) Rate of growth of EEG coherence between frontal and posterior lobes during middle childhood (F 7–P 3; Thatcher, 1992). (B) Rate of growth of working memory (counting span and spatial span) during the same age range

11.1

A summary diagram of the model proposed by Geschwind, Behan, and Galaburda

12.1

Tasks used to assess numerical processing in children

12.2

Brain areas involved in numerical abilities, reading, and executive function in children

13.1

The formation of representations in the cortical matrix model under two different architectural conditions

13.2

An illustration of different kinds of brain connectivity

13.3

Developmental changes in interregional functional connectivity (color plate)

List of Tables

1.1

Levels of interaction between genes and their environment

2.1

A thumbnail sketch of some of the major developmental disorders

5.1

Summary of the relation between developing oculomotor pathways and behavior

5.2

Marker tasks for the development of visual orienting and attention

7.1

Stimulus-dependent effects of neurophysiological manipulations

13.1

Three viewpoints on human functional brain development

Preface to the First Edition

In the first chapter of this book I describe some of the factors responsible for the recent emergence of a subdiscipline at the interface between developmental psychology and cognitive neuroscience. I have chosen to refer to this new field as “developmental cognitive neuroscience,” though it has been known under a number of other terms such as “developmental neurocognition” (de Boysson-Bardies, de Schonen, Jusczyck, McNeilage, & Morton, 1993). Though a series of edited volumes on the topic has recently appeared, like most newly emerging disciplines there is a time lag before the first books suitable for teaching appear. This book and the Reader which I edited in 1993 (Johnson, 1993) are initial attempts to fill the gap. While some may believe these efforts to be premature, my own view is that the lifeblood of any new discipline is in the students and postdocs recruited to the cause. And the sooner they are recruited, the better.

Is developmental cognitive neuroscience really significantly different from other fields that have a more extended history, such as developmental neuropsychology or cognitive development? Clearly, it would be unwise to rigidly demarcate developmental cognitive neuroscience from related, and mutually informative, fields. However, it is my belief that the emerging field has a number of characteristics that makes it distinctive. First, while there is some disagreement about exact definitions, the fields of developmental neuropsychology and developmental psychopathology focus on atypical development, while commonly comparing them to normal developmental trajectories. In contrast, cognitive neuroscience (including the developmental variant outlined in this book) focuses on normal cognitive functioning, but uses information from deviant functioning and development as “nature’s experiments” which can shed light on the neural basis of normal cognition. This book is therefore not intended as an introduction to the neuropsychology of developmental disorders. For such information the reader is referred to the excellent introductions by Cicchetti and Cohen (1995) and Spreen, Risser, and Edgell (1995).

Second, unlike many in cognitive development, this book adopts the premise that information from brain development is more than just a useful additional source of evidence for supporting particular cognitive theories. Rather, information about brain development is viewed as both changing and originating theories at the cognitive level. Third, developmental cognitive neuroscience restricts itself to issues at the neural, cognitive, and immediate environmental levels. In my view it is a hazard of some interdisciplinary fields that the focus of interest is diffused across many different levels of explanation. This is not to deny the importance of these other levels, but a mechanistic interdisciplinary science needs to restrict both the domains (in this case aspects of cognitive processing) and levels of explanation with which it is concerned. Finally, developmental cognitive neuroscience is specifically concerned with understanding the relation between neural and cognitive phenomena. For this reason, I have not discussed evidence from the related field of developmental behavior genetics. In general, developmental behavior genetics tends to be concerned with correlations between the molecular level (genetics) and gross behavioral measures such as IQ. With some notable exceptions, little effort is made to specifically relate these two levels of explanation via the intermediate neural and cognitive levels. Having pointed out the different focus of developmental cognitive neuroscience, my hope is that this book is written to be both accessible and informative to those in related and overlapping disciplines.

The above comments go some way to explaining the choice of material that I have presented in the book. However, I have no doubt that there is a substantive amount of excellent experimentation and theorizing that could have been included but was not. Since this is intended as a brief introduction to the field, I have chosen to focus on a few particular issues in some detail. Of course, the choice of material also reflects my own biases and knowledge since the book is intended as an introductory survey of the field as viewed from my own perspective. I apologize in advance for the inevitable omissions and errors.

The book is aimed at the advanced-level student and assumes some introductory knowledge of both neuroscience and cognitive development. Students without this background will probably need to refer to more introductory textbooks in the appropriate areas. I also hope that the book will attract developmentalists with an interest in learning more about the brain, and cognitive neuroscientists curious as to how developmental data can help constrain their theories about adult functioning. But most of all I hope that the book inspires readers to find out more about the field, and to consider a developmental cognitive neuroscience approach to their own topic.

Preface to the Fourth Edition

In the nearly two decades since publication of the first edition of this book, the field of developmental cognitive neuroscience (DCN) has continued to expand very rapidly, and the volume of papers published in specialized and generalist journals make the job of reviewing and summarizing this information increasingly formidable. In addition, the range of evidence encompassed within the field now extends to the underlying genetics and epigenetics. Thus, and as in previous editions, the selection of material inevitably involves our biases, but with a focus on topics in which a specifically DCN approach has been taken. This inevitably means that there are topics in the parent disciplines of cognitive development or developmental neuroscience that are not addressed in this book.

As the field matures, researchers and funders are increasingly interested in applying the knowledge we have gained to practical real-world problems, such as developing the best brain-based strategies for formal school education. Thus, in this fourth edition we have emphasized new research that underpins application to important clinical, educational, and societal issues. In particular, we have added a new chapter on the emerging topic of educational neuroscience (Chapter 12).

In line with the previous edition, we include “key discussion points” at the end of each chapter, which can be used in association with the teachers’ website associated with the book (www.wiley.com/go/johnson/dcn) that has essay, short answer, and multiple choice test questions as well as downloadable figures. Also, as in previous editions, we provide many pointers to further reading, allowing the book to be used as a springboard for more detailed exploration of the field.

We continue to be indebted to many colleagues and collaborators for educating and informing us on a variety of topics. We also thank our publisher for their continued commitment to the book and Luba Prout for her invaluable contribution to the production of this edition.

Acknowledgements

Figure 1.2 © Mark H. Johnson

Figure 1.3 from Waddington, C.H. (1975). The Evolution of an Evolutionist. New York, USA: Cornell University Press. Copyright © 1975 by C.H. Waddington. Reprinted by permission of the author's estate.

Figure 2.1 reprinted from Neuroscience and Biobehavioural Reviews, 34(3), Lloyd-Fox, S., Blasi, A., & Elwell, C.E., Illuminating the developing brain: The past, present and future of functional near infrared spectroscopy, 269–284, Copyright (2010), with permission from Elsevier.

Figure 2.2 reprinted by permission of Michael Crabtree.

Figure 2.3 reprinted by permission of Sarah Lloyd-Fox.

Figure 2.4 images courtesy of Dr. Sean Deoni, King's College London and Advanced Baby Imaging Lab, Brown University.

Figure 3.1 from Stiles, J. (2008). The fundamentals of brain development: Integrating nature and nurture. Cambridge, MA.: Harvard University Press. Copyright © 2008 by the President and Fellows of Harvard College. Reprinted by permission of the publisher.

Figure 3.2 from Cornish, K. M., Turk, J., Wilding, J., Sudhalter, V., Munir, F., Kooy F., & Hagerman R. (2004). Annotation: Deconstructing the attention in Fragile X syndrome: A developmental neuropsychological approach. Journal of Child Psychology and Psychiatry, 45, 1042–1053. Copyright © 2004, John Wiley and Sons. Reprinted by permission of the publisher.

Figure 4.1 © Mark H. Johnson.

Figure 4.2 © Mark H. Johnson.

Figure 4.3 from Maxwell Cowan, W. (1979). The development of the brain. Reproduced with permission. Copyright © 1979 Scientific American, Inc. All rights reserved.

Figure 4.4 images courtesy of the Centre for NeuroImaging Sciences, King’s College London and the Birkbeck-UCL Centre for NeuroImaging.

Figure 4.5 from LeRoy Conel, J. (1939–1967). The postnatal development of the human cerebral cortex, vols I–VIII. Cambridge, MA: Harvard University Press, Copyright © 1939, 1941, 1947, 1951, 1955, 1959, 1963, 1967 by the President and Fellows of Harvard College. Reprinted by permission of the publisher.

Figure 4.6 from Stiles, J. (2008). The fundamentals of brain development: Integrating nature and nurture. Cambridge, MA: Harvard University Press. Copyright © 2008 by the President and Fellows of Harvard College. Reprinted by permission of the publisher.

Figure 4.7 from Fransson, P., Skiöld, B., Horsch, S., Nordell, A., Blennow, M., Lagercrantz H., and Aden, U. (2007). Resting-state networks in the infant brain. Proceedings of the National Academy of Sciences, USA, 104, 15531–15536. Copyright (2007) National Academy of Sciences, U.S.A. Reprinted by permission of the publisher.

Figure 4.8 reprinted from Trends in Cognitive Sciences, 9, Casey, B. J., Tottenham, N., Liston, C., & Durston, S., Imaging the developing brain: what have we learned about cognitive development?, 104–110, Copyright (2005), with permission from Elsevier. Which is a modified version of a figure from Thompson, R. A. and Nelson, C. A. (2001). Developmental science and the media: Early brain development. American Psychologist, 56, 5–15.

Figure 4.9 © Mark H. Johnson.

Figure 4.10 reprinted from Trends in Neuroscience, 29, Toga, A.W., Thompson, P. M., & Sowell, E. R., Mapping brain maturation, 148–158, Copyright (2006), with permission from Elsevier.

Figure 4.11 reprinted by permission from Macmillan Publishers Ltd: Nature, 440, 676–679, copyright (2006).

Figure 4.12 from Brodmann, K. in Brodal, A. (Eds.) (1981). Neurological Anatomy in Relation to Clinical Medicine, 3rd Ed, Oxford University Press, Figure 12.2 from p. 791. By permission of Oxford University Press, USA.

Figure 4.13 from Rakic, P. (1987). Intrinsic and extrinsic determinants of neocortical parcellation: a radial unit model. In Rakic P. and Singer W. (Eds.) Neurobiology of Neocortex. Report of the Dahelm workshop on Neurobiology of Neocortex, Berlin, 17–22 May 1987. Copyright © 1987, John Wiley and Sons. Reprinted by permission of the publisher.

Figure 4.14 reprinted from Trends in the Neurosciences, 12(10), O'Leary, D.D.M., Do cortical areas emerge from a protocortex?, 400–406, Copyright (1989), with permission from Elsevier.

Figure 4.15 from Chugani, H. T., Phelps M. E., and Mazziotta J. C. (1987). Positron emission tomography study of human brain functional development. Annals of Neurology, 22(4), 487–497. Copyright © 1987 American Neurological Association. Reproduced by permission of John Wiley & Sons.

Figure 5.1 from Atkinson, J. and Braddick, O. (2003). Neurobiological models of normal and abnormal visual development, in de Haan, M. and Johnson, M. H. (Eds.), The Cognitive Neuroscience of Development, Hove, UK: Psychology Press. Copyright © 2003 Psychology Press. Reproduced by permission of the publisher.

Figure 5.2 from Miller, K. D, Keller J. B., and Stryker M. P. (1989). Ocular dominance column development: analysis and simulation, Science, 245, 605–615. Reprinted with permission of the AAAS.

Figure 5.3 from Held, R. (1985). Binocular vision: behavioural and neuronal development. In Mehler, J. and Fox, R. (Eds.), Neonate Cognition: Beyond the Blooming, Buzzing Confusion. Copyright © 1985 Prof. Richard Held. Reproduced by permission of the author.

Figure 5.4 from Schiller, P. H. (1998). The neural control of visually guided eye movements. In Richards, J. E. (Eds.), Cognitive Neuroscience of Attention. Copyright © 1998 Lawrence Erlbaum Associates, Inc., Mahwah, New Jersey. Reproduced by permission of the publisher.

Figure 5.5 reprinted from Journal of Experimental Child Psychology, 59, Gilmore, R. O., & Johnson, M. H., Working memory in infancy: Six-month-olds' performance on two versions of the oculomotor delayed response task, 397–418, Copyright (1995), with permission from Elsevier.

Figure 5.6 from Csibra, G., Tucker, L. A., Volein, A., and Johnson, M.H. (2000). Cortical development and saccade planning: The ontogeny of the spike potential, NeuroReport, 11, 1069–1073. Reproduced by permission of Lippincott Williams & Wilkins.

Figure 5.7 © Mark H. Johnson.

Figure 5.8 from Richards, J. E. and Casey, B. J. (1991). Heart rate variability during attention phases in young infants. Psychophysiology, 28(1), 43–53. Copyright © 2007, John Wiley and Sons. Reprinted by permission of the publisher.

Figure 6.1 from Goodale, M. A., Jacobson, L. S., Milner, A. D., and Perrett, D. I. (1994). The nature and limits of orientation and pattern processing supporting visuomotor control in a visual form agnosic. Journal of Cognitive Neuroscience, 6(1), 43–56. Copyright © 1994, Massachusetts Institute of Technology. Reprinted by permission of MIT Press Journals.

Figure 6.2 from Mareschal, D., Plunkett, K., and Harris, P. (1999). A computational and neuropsychological account of object- oriented behaviours in infancy. Developmental Science, 2, 306–317. Blackwell Publishers Ltd. 1999. Reproduced by permission of the publisher.

Figure 6.3 from Kaufman, J., Csibra, G., & Johnson, M. H. (2003). Representing occluded objects in the human infant brain. Proceedings of the Royal Society B: Biology Letters, 270/S2, 140–143. Reproduced by permission of the Royal Society and the authors.

Figure 7.1 © Mark H. Johnson.

Figure 7.2 from Johnson, M. H. and Morton, J. (1991). Biology and cognitive development: The case of face recognition. Oxford: Blackwell. Reprinted by permission of John Wiley and Sons.

Figure 7.3 from Bednar, J. A., & Miikkulainen, R. (2003). Learning innate face preferences, Neural Computation, 15(7), 1525–1557. Copyright © 2003 by the Massachusetts Institute of Technology. Additional Credit: Goren, C. C., Sarty, M., and Wu, P. Y .K. (1975). Visual following and pattern discrimination of face-like stimuli by newborn infants, Pediatrics, 56, 544–549 and Johnson, M. H. and Morton, J. (1991). Biology and Cognitive Development: The Case of Face Recognition, Oxford: Blackwell Publishing, Figure 2.3, p. 31. Reproduced by permission of John Wiley and Sons.

Figure 7.4 © Mark H. Johnson.

Figure 7.5 from Johnson, M. H. (2005). Sub-cortical face processing. Nature Reviews Neuroscience, 6, 766–774. Reprinted by permission of Nature Publishing Group.

Figure 7.6 from Scherf, K. S., Behrmann, M., Humphreys, K., and Luna, B. (2007). Visual category-selectivity for faces, places and objects emerges along different developmental trajectories. Developmental Science, 10, F15–F30. Copyright © 2007, John Wiley and Sons. Reprinted by permission of the publisher.

Figure 7.7 from Farroni, T., Johnson, M. H., Brockbank, M., and Simion, F. (2000). Infants’ use of gaze direction to cue attention: The importance of perceived motion. Visual Cognition, 7(6), 705–718. Reprinted by permission of the publisher (Taylor & Francis Ltd, http://www.tandf.co.uk/journals).

Figure 7.8 from Farroni, T., Csibra, G., Simion, F., and Johnson, M. H. (2002). Eye contact detection in humans from birth. Proceedings of the National Academy of Sciences of the United States of America, 99, 9602–9605. Copyright (2002) National Academy of Sciences, U.S.A.

Figure 8.1 reprinted by permission of David Amaral.

Figure 8.2 reprinted from Trends in Cognitive Sciences, 10, Bauer, P. J., Constructing a past in infancy: A neuro-developmental account, 175–181, Copyright (2006), with permission from Elsevier.

Figure 8.3 from Vargha-Khadem, F., Gadian, D. G., Watkins, K. E., Connelly, A., van Paesschen, W., and Mishkin, M. (1997). Differential effects of early hippocampal pathology on episodic and semantic memory. Science, 277, 376–380. Reprinted with permission from AAAS.

Figure 9.1 from Leonard, C. M. (2003). Neural substrate of speech and language development. In de Haan, M., and Johnson, M. H. (Eds.), The Cognitive Neuroscience of Development, Hove, UK: Psychology Press, Figure 6.3. Reproduced by permission of Thomson Publishing Services.

Figure 9.2 from Neville, H. J., Bavelier, D., Corina, D., Rauschecker, J. P., Karni, A., Lalwani, A., et al. (1998). Cerebral organization for language in deaf and hearing subjects: Biological constrains and effects of experience. Proceedings of the National Academy of Sciences of the United States of America, 95, 922–929. Copyright (1998) National Academy of Sciences, U.S.A.

Figure 9.3 reprinted by permission from Macmillan Publishers Ltd: Nature Neuroscience, 6(11), 1230–1237, copyright (2003).

Figure 10.1 reprinted from Neuropsychologia, 44, Klingberg, T., Development of a superior frontal-intraparietal network for visuo-spatial working memory, 2171–2177, Copyright (2006), with permission from Elsevier.

Figure 10.2 from Dehaene-Lambertz, G., Dehaene, S., & Hertz-Pannier, L. (2002). Functional neuroimaging of speech perception in infants. Science, 298(5600), 2013–2015. Reprinted with permission from AAAS.

Figure 10.3 reprinted from Brain and Cognition, 20(1), Thatcher, R. W., Cyclic cortical reorganization during early childhood. Special Issue: The role of frontal lobe maturation in cognitive and social development, 24–50, Copyright (1992), with permission from Elsevier.

Figure 10.4 reprinted from Brain and Cognition, 20(1), Case, R., The role of the frontal lobes in the regulation of cognitive development, 51–73, Copyright (1992), with permission from Elsevier.

Figure 11.1 from McManus, I. C., & Bryden, M. P., Geschwind's theory of cerebral lateralization: Developing a formal, causal model, Psychological Bulletin, 110, 237–253, Copyright © 1991 by the American Psychological Association. Reprinted with permission.

Figure 12.1 reproduced from Archives of Disease in Childhood Fetal Neonatal, Simms, V., Cragg, L., Gilmore, C., Marlow, N., & Johnson, S., 98(5), F457–F463, 2013, with permission from BMJ Publishing Group Ltd.

Figure 12.2 from Mapping numerical processing, reading, and executive functions in the developing brain: an fMRI meta-analysis of 52 studies including 842 children, Houde, O., Rossi, S., Lubin, A., & Joliot M (2010), Developmental Science, 13(6), 876–885. Copyright © 2010 Blackwell Publishing Ltd.

Figure 13.1 © Mark H. Johnson.

Figure 13.2 adapted from a figure by Belmonte, M. K., Allen, G., Beckel-Mitchener, A., Boulanger, L. M., Carper, R. A., and Webb, S.J. (2004). Autism and abnormal development of brain connectivity. The Journal of Neuroscience, 24, 9228–9231.

Figure 13.3 from Supekar, K., Musen, M., and Menon, V. (2009). Development of large-scale functional brain networks in children. Public Library of Science, Biology, 7(7), e1000157. Reprinted by permission of the authors and of Public Library of Science, Biology.

Abbreviations

2D

two-dimensional

ADHD

attention deficit/hyperactivity disorder

ANS

approximate number system

ASL

American Sign Language

BOLD

blood oxygen level dependent

CA1

cornu ammonis 1 area of the hippocampus

CA3

cornu ammonis 3 area of the hippocampus

CANTAB

Cambridge Neuropsychological Testing Automated Battery

COMT

catechol-O-methyltransferase gene

CT

computed tomography

DAT1

dopamine active transporter 1 gene

DLPC

dorsolateral prefrontal cortex

DNA

deoxyribonucleic acid

DSP4

N-(2-chloroethyl)-N-ethyl-2-bromobenzylamine neurotoxin

DTI

diffusion tensor imaging

EEG

electroencephalography, electroencephalogram

ERO

event-related oscillations

ERP

event-related potential

FEF

frontal eye fields

FFA

fusiform face area

FG

fusiform gyrus

FMR1

gene Fragile X mental retardation 1 gene

fMRI

functional magnetic resonance imaging

FOXP2

gene forkhead box protein P2 gene

GABA

gamma-aminobutyric acid

GBG

Geschwind, Behan, and Galaburda model of hemispheric differences

HD

high density

HD-ERP

high-density event-related potential

HM

initials of a patient with amnesia

IMM/IMVH

intermediate and medial part of the mesopallium

IPS

intra parietal suclus

IQ

intelligence quotient

IS

interactive specialization

ISI

interstimulus interval

KBCC

knowledge-based cascade correlation

LGN

lateral geniculate nucleus

LTC

lateral temporal complex

MGN

medial geniculate nucleus

MNS

mirror neuron system

MPFC

medial prefrontal cortex

MRI

magnetic resonance imaging

MT

middle temporal visual cortical area

MTL

medial temporal lobes

NIRS

near infrared spectroscopy

PET

positron emission tomography

PFC

prefrontal cortex

PKU

phenylketonuria

RNA

ribonucleic acid

SES

socioeconomic status

SIPN

Social Information Processing Network

SLI

specific language impairment

SOA

stimulus onset asynchrony

SP

spike potential

STS

superior temporal sulcus

TPH2

tryptophan hydroxylase gene 2

TV

television

V1

primary visual cortex

VWFA

visual word form area

WS

Williams syndrome

About the Companion Website

This book is accompanied by a companion website:

www.wiley.com/go/johnson/developmentalcognitiveneuroscience

The website includes:

Multiple choice questions and an answer guide

The material is available freely but you will need an instructor password to access the answers to the multiple choice questions.

1The Biology of Change

In this introductory chapter we discuss a number of background issues for developmental cognitive neuroscience, beginning with historical approaches to the nature–nurture debate. Constructivism, in which biological forms are an emergent produc3t of complex dynamic interactions between genes and environment, is presented as an approach to development that is superior to accounts that seek to identify preexisting information in genes or the external environment. However, if we are to abandon existing ways of analyzing development into “innate” and “acquired” components, this raises the question of how we should best understand developmental processes. One scheme is proposed for taking account of the various levels of interaction between genes and environment. In addition, we introduce the difference between innate representations and architectural constraints on the emergence of representations within neural networks. Following this, a number of factors are discussed that demonstrate the importance of the cognitive neuroscience approach to development, including the increasing availability of brain imaging and molecular approaches. Conversely, the importance of taking a developmental approach to analyzing the relation between brain structure and cognition is reviewed. In examining the ways in which development and cognitive neuroscience can be combined, three different perspectives on human functional brain development are discussed: a maturational view, a skill learning view, and an “interactive specialization” framework. Finally, the contents of the rest of the book are outlined.

1.1 Viewpoints on Development

As every parent knows, the changes we can observe during the growth of children from birth to adolescence are truly amazing. Perhaps the most remarkable aspects of this growth involve the brain and mind. Accompanying the fourfold increase in the volume of the brain during this time are numerous, and sometimes surprising, changes in behavior, thought, and emotion. An understanding of how the developments in brain and mind relate to each other could potentially revolutionize our thinking about education, social policy, and disorders of mental development. It is no surprise, therefore, that there has been increasing interest in this new branch of science from grant-funding agencies, medical charities, and even presidential summits. Since the publication of the first edition of this book in 1997, this field has become known as developmental cognitive neuroscience.

Developmental cognitive neuroscience has emerged at the interface between two of the most fundamental questions that challenge humankind. The first of these questions concerns the relation between mind and body, and specifically between the physical substance of the brain and the mental processes it supports. This issue is fundamental to the scientific discipline of cognitive neuroscience. The second question concerns the origin of organized biological structures, such as the highly complex structure of the adult human brain. This issue is fundamental to the study of development. In this book we will show that light can be shed on these two fundamental questions by tackling them both simultaneously, specifically by focusing on the relation between the postnatal development of the human brain and the emerging cognitive processes it supports.

The second of the two questions above, that of the origins of organized biological structure, can be posed in terms of phylogeny or ontogeny. The phylogenetic (evolutionary) version of this question concerns the origin of species and has been addressed by Charles Darwin and many others since. The ontogenetic version of this question concerns individual development within a life span. The ontogenetic question has been somewhat neglected relative to phylogeny, since some influential scientists have held the view that once a particular set of genes has been selected by evolution, ontogeny is simply a process of executing the “instructions” coded for by those genes. By this view, the ontogenetic question essentially reduces to phylogeny (e.g., so-called evolutionary psychology). In contrast to this view, in this book we argue that ontogenetic development is an active process through which biological structure is constructed afresh in each individual by means of complex and variable interactions between genes and their respective environments. The information is not in the genes, but emerges from the constructive interaction between genes and their environment (see also Oyama, 2000). However, since both ontogeny and phylogeny concern the emergence of biological structure, some of the same mechanisms of change have been invoked in the two cases.