Attention is a computation applied to competing environmental information to bias the selection of one option and avoid distraction from alternative inputs. Studying the development of visual attention in children can provide information on attention processes in adults.
We propose a framework that embeds the development of visual attention into the emerging functionality of the hierarchical architectural organization of visual pathways, extending from the primary visual cortex to the prefrontal cortex. The cumulative development of visual areas feeding forward into higher-level regions may function as the catalyst for top-down attentional modulation of these same visual pathways.
Separable visual attention mechanisms are involved in encoding visual short-term memory, maintenance of working memory and long-term recognition memory. These effects of developing attention on distinct memory processes can be dissociated at different developmental time points.
Attention deficit hyperactivity disorder, fragile X syndrome and autism spectrum disorder are among the many neurodevelopmental disorders associated with disruptions to visual attention. Identification of the causative mechanisms of these abnormalities, a critical step to intervention and prevention, can come only from longitudinal developmental studies.
Studies have shown that genetic variability influences basic cortical organization and connections that underlie the development of visual attention, rather than predetermining attentional control itself. This insight is important for understanding why attention disruptions do not occur in isolation in neurodevelopmental disorders and are often comorbid with other disruptions to cognition and perceptual operations.
The goal of attention training is the transfer of improved attentional control skills from the narrow realm of the training task to other related cognitive processes or educational outcomes. This goal is best served through a mechanistic developmental understanding of the links between visual processing, attention, memory and learning.
Visual attention functions as a filter to select environmental information for learning and memory, making it the first step in the eventual cascade of thought and action systems. Here, we review studies of typical and atypical visual attention development and explain how they offer insights into the mechanisms of adult visual attention. We detail interactions between visual processing and visual attention, as well as the contribution of visual attention to memory. Finally, we discuss genetic mechanisms underlying attention disorders and how attention may be modified by training.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
SN Computer Science Open Access 01 February 2022
Scientific Reports Open Access 27 October 2021
Journal of Neurodevelopmental Disorders Open Access 15 October 2021
Subscribe to Journal
Get full journal access for 1 year
only $8.25 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
Fan, J., McCandliss, B. D., Sommer, T., Raz, A. & Posner, M. I. Testing the efficiency and independence of attentional networks. J. Cogn. Neurosci. 14, 340–347 (2002).
Petersen, S. E. & Posner, M. I. The attention system of the human brain: 20 years after. Annu. Rev. Neurosci. 35, 73–89 (2012).
Posner, M. I. & Petersen, S. E. The attention system of the human brain. Annu. Rev. Neurosci. 13, 25–42 (1990).
Desimone, R. & Duncan, J. Neural mechanisms of selective visual-attention. Annu. Rev. Neurosci. 18, 193–222 (1995).
Miller, E. K. & Cohen, J. D. An integrative theory of prefrontal cortex function. Annu. Rev. Neurosci. 24, 167–202 (2001).
Matusz, P. J. et al. Multi-modal distraction: insights from children's limited attention. Cognition 136, 156–165 (2015).
Rizzolatti, G., Riggio, L., Dascola, I. & Umilta, C. Reorienting attention across the horizontal and vertical meridians — evidence in favor of a premotor theory of attention. Neuropsychologia 25, 31–40 (1987).
Pineda, J. A. Sensorimotor cortex as a critical component of an 'extended' mirror neuron system: does it solve the development, correspondence, and control problems in mirroring? Behav. Brain Funct. 4, 47 (2008).
Corbetta, M. & Shulman, G. L. Control of goal-directed and stimulus-driven attention in the brain. Nat. Rev. Neurosci. 3, 201–215 (2002).
Fan, J. et al. Testing the behavioral interaction and integration of attentional networks. Brain Cogn. 70, 209–220 (2009).
Green, A. E. et al. Using genetic data in cognitive neuroscience: from growing pains to genuine insights. Nat. Rev. Neurosci. 9, 710–720 (2008).
Rueda, M. R. et al. Development of attentional networks in childhood. Neuropsychologia 42, 1029–1040 (2004).
Scerif, G. Attention trajectories, mechanisms and outcomes: at the interface between developing cognition and environment. Dev. Sci. 13, 805–812 (2010).
Amso, D. & Johnson, S. P. Learning by selection: visual search and object perception in young infants. Dev. Psychol. 42, 1236–1245 (2006).
Amso, D. & Johnson, S. P. Development of visual selection in 3- to 9-month-olds: evidence from saccades to previously ignored locations. Infancy 13, 675–686 (2008).
Butcher, P. R., Kalverboer, A. F. & Geuze, R. H. Infants' shifts of gaze from a central to a peripheral stimulus: a longitudinal study of development between 6 and 26 weeks. Infant Behav. Dev. 23, 3–21 (2000).
Hood, B. M. Inhibition of return produced by covert shifts of visual-attention in 6-month-old infants. Infant Behav. Dev. 16, 245–254 (1993).
Johnson, M. H. in Attention and Performance XV: Conscious and Nonconscious Information Processing (eds Umiltà, C. & Moscovitch, M.) 291–310 (MIT Press, 1994).
Johnson, M. H. & Tucker, L. A. The development and temporal dynamics of spatial orienting in infants. J. Exp. Child Psychol. 63, 171–188 (1996).
Johnson, M. H., Posner, M. I. & Rothbart, M. K. Components of visual orienting in early infancy — contingency learning, anticipatory looking, and disengaging. J. Cogn. Neurosci. 3, 335–344 (1991).
Atkinson, J., Hood, B., Wattambell, J. & Braddick, O. Changes in infants ability to switch visual-attention in the first 3 months of life. Perception 21, 643–653 (1992).
Atkinson, J. & Braddick, O. From genes to brain development to phenotypic behavior: “dorsal-stream vulnerability” in relation to spatial cognition, attention, and planning of actions in Williams syndrome (WS) and other developmental disorders. Prog. Brain Res. 189, 261–283 (2011).
Corbetta, M. et al. A common network of functional areas for attention and eye movements. Neuron 21, 761–773 (1998).
Nobre, A. C., Gitelman, D. R., Dias, E. C. & Mesulam, M. M. Covert visual spatial orienting and saccades: overlapping neural systems. Neuroimage 11, 210–216 (2000).
Konrad, K. et al. Development of attentional networks: an fMRI study with children and adults. Neuroimage 28, 429–439 (2005).
Johnson, M. H. The inhibition of automatic saccades in early infancy. Dev. Psychobiol. 28, 281–291 (1995).
Guitton, D., Buchtel, H. A. & Douglas, R. M. Frontal-lobe lesions in man cause difficulties in suppressing reflexive glances and in generating goal-directed saccades. Exp. Brain Res. 58, 455–472 (1985).
Scerif, G. et al. To look or not to look? Typical and atypical development of oculomotor control. J. Cogn. Neurosci. 17, 591–604 (2005).
Luna, B., Garver, K. E., Urban, T. A., Lazar, N. A. & Sweeney, J. A. Maturation of cognitive processes from late childhood to adulthood. Child Dev. 75, 1357–1372 (2004).
Davidson, M. C., Amso, D., Cruess Anderson, L. & Diamond, A. Development of cognitive control and executive functions from 4 to 13 years: evidence from manipulations of memory, inhibition, and task switching. Neuropsychologia 44, 2037–2078 (2006).
Crone, E. A. Executive functions in adolescence: inferences from brain and behavior. Dev. Sci. 12, 825–830 (2009).
Hwang, K., Velanova, K. & Luna, B. Strengthening of top-down frontal cognitive control networks underlying the development of inhibitory control: a functional magnetic resonance imaging effective connectivity study. J. Neurosci. 30, 15535–15545 (2010). Describes a study using Granger causality analysis to test developmental changes in effective connectivity underlying inhibitory control (using an antisaccade task) compared with reflexive responses (using a prosaccade task). In early childhood, few top-down connectivities were evident with increased parietal interconnectivity; however, by adolescence, connections from the PFC were increased and parietal interconnectivity was decreased.
Miyake, A. & Friedman, N. P. The nature and organization of individual differences in executive functions: four general conclusions. Curr. Direct. Psychol. Sci. 21, 8–14 (2012).
Chun, M. M., Golomb, J. D. & Turk-Browne, N. B. A taxonomy of external and internal attention. Annu. Rev. Psychol. 62, 73–101 (2011).
Robertson, I. H., Mattingley, J. B., Rorden, C. & Driver, J. Phasic alerting of neglect patients overcomes their spatial deficit in visual awareness. Nature 395, 169–172 (1998).
Gogtay, N. et al. Dynamic mapping of human cortical development during childhood through early adulthood. Proc. Natl Acad. Sci. USA 101, 8174–8179 (2004).
Sowell, E. R. et al. Longitudinal mapping of cortical thickness and brain growth in normal children. J. Neurosci. 24, 8223–8231 (2004).
Raznahan, A. et al. Longitudinal four-dimensional mapping of subcortical anatomy in human development. Proc. Natl Acad. Sci. USA 111, 1592–1597 (2014).
Vandekar, S. N. et al. Topologically dissociable patterns of development of the human cerebral cortex. J. Neurosci. 35, 599–609 (2015).
Raznahan, A. et al. Patterns of coordinated anatomical change in human cortical development: a longitudinal neuroimaging study of maturational coupling. Neuron 72, 873–884 (2011).
Schmitt, J. E. et al. The dynamic role of genetics on cortical patterning during childhood and adolescence. Proc. Natl Acad. Sci. USA 111, 6774–6779 (2014).
Fair, D. A. et al. Development of distinct control networks through segregation and integration. Proc. Natl Acad. Sci. USA 104, 13507–13512 (2007). Uses resting-state fMRI to show that development of adult control networks involves both segregation (that is, decreased short-range connections) and integration (that is, increased long-range connections) of the brain regions that comprise them.
Luna, B. & Sweeney, J. A. in Adolescent Brain Development: Vulnerabilities and Opportunities (eds Dahl, R. E. & Spear, L. P.) 296–309 (New York Academy of Sciences, 2004).
Maunsell, J. H. R. & Vanessen, D. C. The connections of the middle temporal visual area (mt) and their relationship to a cortical hierarchy in the macaque monkey. J. Neurosci. 3, 2563–2586 (1983).
Vanessen, D. C. & Maunsell, J. H. R. Hierarchical organization and functional streams in the visual-cortex. Trends Neurosci. 6, 370–375 (1983).
Gilbert, C. D. & Li, W. Top-down influences on visual processing. Nat. Rev. Neurosci. 14, 350–363 (2013).
Itti, L. & Koch, C. Computational modelling of visual attention. Nat. Rev. Neurosci. 2, 194–203 (2001).
Serre, T., Oliva, A. & Poggio, T. A feedforward architecture accounts for rapid categorization. Proc. Natl Acad. Sci. USA 104, 6424–6429 (2007).
Zhang, Y. et al. Object decoding with attention in inferior temporal cortex. Proc. Natl Acad. Sci. USA 108, 8850–8855 (2011).
Maunsell, J. H. R. & Newsome, W. T. Visual processing in monkey extrastriate cortex. Annu. Rev. Neurosci. 10, 363–401 (1987).
Carrasco, M., Ling, S. & Read, S. Attention alters appearance. Nat. Neurosci. 7, 308–313 (2004).
Gilbert, C. D. & Sigman, M. Brain states: top-down influences in sensory processing. Neuron 54, 677–696 (2007).
Batardiere, A. et al. Early specification of the hierarchical organization of visual cortical areas in the macaque monkey. Cereb. Cortex 12, 453–465 (2002).
Moore, C. I., Carlen, M., Knoblich, U. & Cardin, J. A. Neocortical interneurons: from diversity, strength. Cell 142, 184–188 (2010).
Summerfield, C. & de Lange, F. P. Expectation in perceptual decision making: neural and computational mechanisms. Nat. Rev. Neurosci. 15, 745–756 (2014).
Amso, D., Haas, S. & Markant, J. An eye tracking investigation of developmental change in bottom-up attention orienting to faces in cluttered natural scenes. PLoS ONE 9, e85701 (2014).
Brown, A. M. & Yamamoto, M. Visual-acuity in newborn and preterm infants measured with grating acuity cards. Am. J. Ophthalmol. 102, 245–253 (1986).
Atkinson, J., Braddick, O. & Moar, K. Development of contrast sensitivity over first 3 months of life in human infant. Vision Res. 17, 1037–1044 (1977).
Banton, T. & Bertenthal, B. I. Multiple developmental pathways for motion processing. Optom. Vision Sci. 74, 751–760 (1997).
Braddick, O. J., Wattambell, J. & Atkinson, J. Orientation-specific cortical responses develop in early infancy. Nature 320, 617–619 (1986).
Amso, D. & Johnson, S. P. Selection and inhibition in infancy: evidence from the spatial negative priming paradigm. Cognition 95, B27–B36 (2005).
Finlay, B. L. & Uchiyama, R. Developmental mechanisms channeling cortical evolution. Trends Neurosci. 38, 69–76 (2015).
Badre, D. & D'Esposito, M. Functional magnetic resonance imaging evidence for a hierarchical organization of the prefrontal cortex. J. Cogn. Neurosci. 19, 2082–2099 (2007).
Sporns, O. The human connectome: origins and challenges. Neuroimage 80, 53–61 (2013).
Fransson, P. et al. Resting-state networks in the infant brain. Proc. Natl Acad. Sci. USA 104, 15531–15536 (2007).
Pruett, J. R. Jr et al. Accurate age classification of 6 and 12 month-old infants based on resting-state functional connectivity magnetic resonance imaging data. Dev. Cogn. Neurosci. 12, 123–133 (2015).
Fair, D. A. et al. Functional brain networks develop from a “local to distributed” organization. PLoS Comput. Biol. 5, e1000381 (2009).
Supekar, K., Musen, M. & Menon, V. Development of large-scale functional brain networks in children. PloS Biol. 7, e1000157 (2009).
Dosenbach, N. U. F. et al. Prediction of individual brain maturity using fMRI. Science 329, 1358–1361 (2010).
Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L. & Petersen, S. E. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59, 2142–2154 (2012).
Luck, S. J. & Vogel, E. K. The capacity of visual working memory for features and conjunctions. Nature 390, 279–281 (1997).
Sperling, G. The information available in brief visual presentations. Psychol. Monographs 74, 1–29 (1960).
Hollingworth, A., Williams, C. C. & Henderson, J. M. To see and remember: visually specific information is retained in memory from previously attended objects in natural scenes. Psychonom. Bull. Rev. 8, 761–768 (2001).
Golomb, J. D., Chun, M. M. & Mazer, J. A. The native coordinate system of spatial attention is retinotopic. J. Neurosci. 28, 10654–10662 (2008).
Ross-Sheehy, S., Oakes, L. M. & Luck, S. J. Exogenous attention influences visual short-term memory in infants. Dev. Sci. 14, 490–501 (2011). Shows that infants as young as 5 months of age can encode information in VSTM from multiple-object arrays, and that attention-directing cues influence both perceptual-memory and VSTM encoding of stimuli in infants, as they do in adults.
Markant, J. & Amso, D. Selective memories: infants' encoding is enhanced in selection via suppression. Dev. Sci. 16, 926–940 (2013). This study showed that 9-month-old infants have better recognition memory for category exemplars encoded in the context of an attention-orienting mechanism involving suppression of distactor information in contrast with a condition in which such suppression is not engaged.
Shimi, A., Nobre, A. C., Astle, D. & Scerif, G. Orienting attention within visual short-term memory: development and mechanisms. Child Dev. 85, 578–592 (2014).
Markant, J. & Amso, D. Leveling the playing field: attention mitigates the effects of intelligence on memory. Cognition 131, 195–204 (2014).
Dixon, M. L., Zelazo, P. D. & De Rosa, E. Evidence for intact memory-guided attention in school-aged children. Dev. Sci. 13, 161–169 (2010).
Hollingworth, A., Richard, A. M. & Luck, S. J. Understanding the function of visual short-term memory: transsaccadic memory, object correspondence, and gaze correction. J. Exp. Psychol. General 137, 163–181 (2008).
Ross-Sheehy, S., Oakes, L. M. & Luck, S. J. The development of visual short-term memory capacity in infants. Child Dev. 74, 1807–1822 (2003).
Kaldy, Z. & Leslie, A. M. A memory span of one? Object identification in 6.5-month-old infants. Cognition 97, 153–177 (2005).
Wu, R. & Kirkham, N. Z. No two cues are alike: depth of learning during infancy is dependent on what orients attention. J. Exp. Child Psychol. 107, 118–136 (2010). Demonstrates that attention-directing social cues have powerful influences on young infants' ability to learn about features of their visual world.
Richards, J. E. & Casey, B. J. Heart-rate-variability during attention phases in young infants. Psychophysiology 28, 43–53 (1991).
Markant, J., Worden, M. S. & Amso, D. Not all attention orienting is created equal: recognition memory is enhanced when attention orienting involves distractor suppression. Neurobiol. Learn. Memory 120, 28–40 (2015).
Crone, E. A., Wendelken, C., Donohue, S., van Leijenhorst, L. & Bunge, S. A. Neurocognitive development of the ability to manipulate information in working memory. Proc. Natl Acad. Sci. USA 103, 9315–9320 (2006).
Wendelken, C., Baym, C. L., Gazzaley, A. & Bunge, S. A. Neural indices of improved attentional modulation over middle childhood. Dev. Cogn. Neurosci. 1, 175–186 (2011). Shows that children's reduced ability to maintain items in working memory, especially in the presence of distraction, is driven by weaker top-down modulation of activity in areas involved in stimulus processing.
Olesen, P. J., Macoveanu, J., Tegner, J. & Klingberg, T. Brain activity related to working memory and distraction in children and adults. Cereb. Cortex 17, 1047–1054 (2007).
Astle, D. E., Nobre, A. C. & Scerif, G. Attentional control constrains visual short-term memory: insights from developmental and individual differences. Q. J. Exp. Psychol. (Hove) 65, 277–294 (2012).
Astle, D. et al. The neural dynamics of fronto-parietal networks in childhood revealed using magnetoencephalography. Cereb. Cortex http://dx.doi.org/10.1093/cercor/bhu271 (2014). Uses magnetoencephalography to show that, in children, slow frequency-theta (4–7 Hz) activity within a right-lateralized frontoparietal network in anticipation of memoranda being encoded into VSTM predicts the accuracy with which those memory items were subsequently retrieved, as well as activity associated with early visual processing of the memoranda.
Shimi, A., Kuo, B.-C., Astle, D. E., Nobre, A. C. & Scerif, G. Age group and individual differences in attentional orienting dissociate neural mechanisms of encoding and maintenance in visual STM. J. Cogn. Neurosci. 26, 864–877 (2014). Uses electroencephalography to show that adults, but not children, elicit a set of neural markers that are broadly similar in preparation for encoding and during maintenance in VSTM.
Best, J. R., Miller, P. H. & Naglieri, J. A. Relations between executive function and academic achievement from ages 5 to 17 in a large, representative national sample. Learn. Individ. Differ. 21, 327–336 (2011).
Bull, R. & Scerif, G. Executive functioning as a predictor of children's mathematics ability: inhibition, switching, and working memory. Dev. Neuropsychol. 19, 273–293 (2001).
Chun, M. M. & Jian, Y. H. Contextual cueing: implicit learning and memory of visual context guides spatial attention. Cogn. Psychol. 36, 28–71 (1998).
Summerfield, J. J., Lepsien, J., Gitelman, D. R., Mesulam, M. M. & Nobre, A. C. Orienting attention based on long-term memory experience. Neuron 49, 905–916 (2006).
Wu, R. et al. Searching for something familiar or novel: top-down attentional selection of specific items or object categories. J. Cogn. Neurosci. 25, 719–729 (2013).
Chun, M. M. & Jiang, Y. H. Top-down attentional guidance based on implicit learning of visual covariation. Psychol. Sci. 10, 360–365 (1999).
Shimi, A. & Scerif, G. The interplay of spatial attentional biases and mnemonic codes in VSTM: developmentally informed hypotheses. Dev. Psychol. 51, 731–743 (2015).
Casey, B. J. et al. Implication of right frontostriatal circuitry in response inhibition and attention-deficit/hyperactivity disorder. J. Am. Acad. Child Adolesc. Psychiatry 36, 374–383 (1997).
Durston, S. et al. Differential effects of DRD4 and DAT1 genotype on fronto-striatal gray matter volumes in a sample of subjects with attention deficit hyperactivity disorder, their unaffected siblings, and controls. Mol. Psychiatry 10, 678–685 (2005).
Volkow, N. D., Wang, G. J., Fowler, J. S. & Ding, Y. S. Imaging the effect of methylphenidate on brain dopamine: new model on its therapeutic actions for attention-deficit/hyperactivity disorder. Biol. Psychiatry 57, 1410–1415 (2005).
Castellanos, F. X. et al. Quantitative brain magnetic resonance imaging in attention-deficit hyperactivity disorder. Arch. Gen. Psychiatry 53, 607–616 (1996).
Castellanos, F. X., Sonuga-Barke, E. J. S., Milham, M. P. & Tannock, R. Characterizing cognition in ADHD: beyond executive dysfunction. Trends Cogn. Sci. 10, 117–123 (2006).
Castellanos, F. X. & Proal, E. Large-scale brain systems in ADHD: beyond the prefrontal-striatal model. Trends Cogn. Sci. 16, 17–26 (2012).
Neale, B. M. et al. Meta-analysis of genome-wide association studies of attention-deficit/hyperactivity disorder. J. Am. Acad. Child Adolesc. Psychiatry 49, 884–897 (2010).
Shaw, P. et al. Longitudinal mapping of cortical thickness and clinical outcome in children and adolescents with attention-deficit/hyperactivity disorder. Arch. Gen Psychiatry 63, 540–549 (2006).
Batty, M. J. et al. Cortical gray matter in attention-deficit/hyperactivity disorder: a structural magnetic resonance imaging study. J. Am. Acad. Child Adolesc. Psychiatry 49, 229–238 (2010).
Poldrack, R. A. Is “efficiency” a useful concept in cognitive neuroscience? Dev. Cogn. Neurosci. 11, 12–17 (2015).
Wang, L. et al. Altered small-world brain functional networks in children with attention-deficit/hyperactivity disorder. Hum. Brain Mapp. 30, 638–649 (2009).
Cortese, S. et al. Toward systems neuroscience of ADHD: a meta-analysis of 55 fMRI Studies. Am. J. Psychiatry 169, 1038–1055 (2012).
Liddle, E. B. et al. Task-related default mode network modulation and inhibitory control in ADHD: effects of motivation and methylphenidate. J. Child Psychol. Psychiatry 52, 761–771 (2011).
Fair, D. A. et al. Atypical default network connectivity in youth with attention-deficit/hyperactivity disorder. Biol. Psychiatry 68, 1084–1091 (2010).
Hahamy, A., Behrmann, M. & Malach, R. The idiosyncratic brain: distortion of spontaneous connectivity patterns in autism spectrum disorder. Nat. Neurosci. 18, 302–309 (2015).
Ray, S. et al. Structural and functional connectivity of the human brain in autism spectrum disorders and attention-deficit/hyperactivity disorder: a rich club-organization study. Hum. Brain Mapp. 35, 6032–6048 (2014).
Di Martino, A. et al. Unraveling the miswired connectome: a developmental perspective. Neuron 83, 1335–1353 (2014). This review highlights the excitement and caveats associated with the new field of developmental connectomics and their implications for understanding neurodevelopmental disorders.
Braddick, O., Atkinson, J. & Wattam-Bell, J. Normal and anomalous development of visual motion processing: motion coherence and 'dorsal-stream vulnerability'. Neuropsychologia 41, 1769–1784 (2003). Summarizes the wealth of evidence for dorsal stream vulnerability across a large number of neurodevelopmental disorders.
Mottron, L., Dawson, M., Soulieres, I., Hubert, B. & Burack, J. Enhanced perceptual functioning in autism: an update, and eight principles of autistic perception. J. Autism Dev. Disord. 36, 27–43 (2006).
O'Riordan, M. & Plaisted, K. Enhanced discrimination in autism. Q. J. Exp. Psychol. A 54, 961–979 (2001).
Davis, G. & Plaisted-Grant, K. Low endogenous neural noise in autism. Autism 19, 351–362 (2015).
Pellicano, E. & Burr, D. When the world becomes 'too real': a Bayesian explanation of autistic perception. Trends Cogn. Sci. 16, 504–510 (2012).
Shen, M. D. et al. Early brain enlargement and elevated extra-axial fluid in infants who develop autism spectrum disorder. Brain 136, 2825–2835 (2013).
Nordahl, C. W. et al. Brain enlargement is associated with regression in preschool-age boys with autism spectrum disorders. Proc. Natl Acad. Sci. USA 108, 20195–20200 (2011).
Keehn, B., Mueller, R.-A. & Townsend, J. Atypical attentional networks and the emergence of autism. Neurosci. Biobehav. Rev. 37, 164–183 (2013).
Jones, W. & Klin, A. Attention to eyes is present but in decline in 2–6-month-old infants later diagnosed with autism. Nature 504, 427–431 (2013).
Elsabbagh, M. et al. Disengagement of visual attention in infancy is associated with emerging autism in toddlerhood. Biol. Psychiatry 74, 189–194 (2013).
Posner, M. I., Rothbart, M. K. & Sheese, B. E. Attention genes. Dev. Sci. 10, 24–29 (2007).
Fossella, J. et al. Assessing the molecular genetics of attention networks. BMC Neurosci. 3, 14 (2002).
Brookes, K. et al. The analysis of 51 genes in DSM-IV combined type attention deficit hyperactivity disorder: association signals in DRD4, DAT1 and 16 other genes. Mol. Psychiatry 11, 934–953 (2006).
Faraone, S. V., Doyle, A. E., Mick, E. & Biederman, J. Meta-analysis of the association between the 7-repeat allele of the dopamine D4 receptor gene and attention deficit hyperactivity disorder. Am. J. Psychiatry 158, 1052–1057 (2001).
Dumontheil, I. et al. Influence of the COMT genotype on working memory and brain activity changes during development. Biol. Psychiatry 70, 222–229 (2011).
Franke, B. et al. Multicenter analysis of the SLC6A3/DAT1 VNTR haplotype in persistent ADHD suggests differential involvement of the gene in childhood and persistent ADHD. Neuropsychopharmacology 35, 656–664 (2010).
Stergiakouli, E. et al. Investigating the contribution of common genetic variants to the risk and pathogenesis of ADHD. Am. J. Psychiatry 169, 186–194 (2012).
Yang, L. et al. Polygenic transmission and complex neuro developmental network for attention deficit hyperactivity disorder: genome-wide association study of both common and rare variants. Am. J. Med. Genet. B Neuropsychiatr. Genet. 162B, 419–430 (2013).
Hamshere, M. L. et al. Shared polygenic contribution between childhood attention-deficit hyperactivity disorder and adult schizophrenia. Br. J. Psychiatry 203, 107–111 (2013).
Martin, J., Hamshere, M. L., Stergiakouli, E., O'Donovan, M. C. & Thapar, A. Genetic risk for attention-deficit/hyperactivity disorder contributes to neurodevelopmental traits in the general population. Biol. Psychiatry 76, 664–671 (2014).
Poelmans, G., Pauls, D. L., Buitelaar, J. K. & Franke, B. Integrated genome-wide association study findings: identification of a neurodevelopmental network for attention deficit hyperactivity disorder. Am. J. Psychiatry 168, 365–377 (2011). One of the first studies to discuss the failures of genome-wide studies of ADHD risk to identify single-candidate variants and argue that such studies should be replaced with analyses that instead focus on neurodevelopmental pathways.
Scerif, G. & Karmiloff-Smith, A. The dawn of cognitive genetics? Crucial developmental caveats. Trends Cogn. Sci. 9, 126–135 (2005).
Rice, F. et al. The links between prenatal stress and offspring development and psychopathology: disentangling environmental and inherited influences. Psychol. Med. 40, 335–345 (2010).
Hall, J., Trent, S., Thomas, K. L., O'Donovan, M. C. & Owen, M. J. Genetic risk for schizophrenia: convergence on synaptic pathways involved in plasticity. Biol. Psychiatry 77, 52–58 (2015).
Pinto, D. et al. Convergence of genes and cellular pathways dysregulated in autism spectrum disorders. Am. J. Hum. Genet. 94, 677–694 (2014).
Lee, S. H. et al. Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat. Genet. 45, 984–994 (2013).
Wahlstrom, D., Collins, P., White, T. & Luciana, M. Developmental changes in dopamine neurotransmission in adolescence: Behavioral implications and issues in assessment. Brain Cogn. 72, 146–159 (2010).
Scerif, G. & Baker, K. Annual Research Review: rare genotypes and childhood psychopathology — uncovering diverse developmental mechanisms of ADHD risk. J. Child Psychol. Psychiatry 56, 251–273 (2015).
Verkerk, A. et al. Identification of a gene (FMR-1) containing a CGG repeat coincident with a breakpoint cluster region exhibiting length variation in fragile-X syndrome. Cell 65, 905–914 (1991).
Bear, M. F., Huber, K. M. & Warren, S. T. The mGIuR theory of fragile X mental retardation. Trends Neurosci. 27, 370–377 (2004).
D'Hulst, C. et al. Decreased expression of the GABAA receptor in fragile X syndrome. Brain Res. 1121, 238–245 (2006).
Bagni, C. & Greenough, W. T. From mRNP trafficking to spine dysmorphogenesis: The roots of fragile X syndrome. Nat. Rev. Neurosci. 6, 376–387 (2005).
Huber, K. M., Gallagher, S. M., Warren, S. T. & Bear, M. F. Altered synaptic plasticity in a mouse model of fragile X mental retardation. Proc. Natl Acad. Sci. USA 99, 7746–7750 (2002).
Zhang, Y. Q. et al. Protein expression profiling of the Drosophila fragile X mutant brain reveals up-regulation of monoamine synthesis. Mol. Cell. Proteom. 4, 278–290 (2005).
Elston, G. N., Oga, T., Okamoto, T. & Fujita, I. Spinogenesis and pruning from early visual onset to adulthood: an intracellular injection study of layer III pyramidal cells in the ventral visual cortical pathway of the macaque monkey. Cereb. Cortex 20, 1398–1408 (2010).
Elia, J. et al. Rare structural variants found in attention-deficit hyperactivity disorder are preferentially associated with neurodevelopmental genes. Mol. Psychiatry 15, 637–646 (2010).
Darnell, J. C. et al. FMRP stalls ribosomal translocation on mRNAs linked to synaptic function and autism. Cell 146, 247–261 (2011).
Auerbach, B. D., Osterweil, E. K. & Bear, M. F. Mutations causing syndromic autism define an axis of synaptic pathophysiology. Nature 480, 63–68 (2011).
Baker, K., Scerif, G., Astle, D. E., Fletcher, P. C. & Raymond, F. L. Psychopathology and cognitive performance in individuals with membrane-associated guanylate kinase mutations: a functional network phenotyping study. J. Neurodev. Disord. 7, 8 (2015).
Bavelier, D. & Neville, H. J. Cross-modal plasticity: where and how? Nat. Rev. Neurosci. 3, 443–452 (2002).
Bavelier, D. et al. Visual attention to the periphery is enhanced in congenitally deaf individuals. J. Neurosci. 20, RC93 (2000).
Mezzacappa, E. Alerting, orienting, and executive attention: developmental properties and sociodemographic correlates in an epidemiological sample of young, urban children. Child Dev. 75, 1373–1386 (2004).
Hackman, D. A., Farah, M. J. & Meaney, M. J. Socioeconomic status and the brain: mechanistic insights from human and animal research. Nat. Rev. Neurosci. 11, 651–659 (2010).
Amso, D., Markant, J. & Haas, S. Agents of developmental change in orienting to faces in cluttered natural scenes. PLoS ONE 9, e85701 (2014).
Bejjanki, V. R. et al. Action video game play facilitates the development of better perceptual templates. Proc. Natl Acad. Sci. USA 111, 16961–16966 (2014).
Green, C. S. & Bavelier, D. Action video game modifies visual selective attention. Nature 423, 534–537 (2003).
Green, C. S. & Bavelier, D. Effect of action video games on the spatial distribution of visuospatial attention. J. Exp. Psychol. Hum. Percept. Perform. 32, 1465–1478 (2006).
Rueda, M. R., Rothbart, M. K., McCandliss, B. D., Saccomanno, L. & Posner, M. I. Training, maturation, and genetic influences on the development of executive attention. Proc. Natl Acad. Sci. USA 102, 14931–14936 (2005).
Klingberg, T. Training and plasticity of working memory. Trends Cogn. Sci. 14, 317–324 (2010).
Diamond, A. & Lee, K. Interventions shown to aid executive function development in children 4 to 12 years old. Science 333, 959–964 (2011).
Thorell, L. B., Lindqvist, S., Nutley, S. B., Bohlin, G. & Klingberg, T. Training and transfer effects of executive functions in preschool children. Dev. Sci. 12, 106–113 (2009).
Klingberg, T. et al. Computerized training of working memory in children with ADHD — a randomized, controlled trial. J. Am. Acad. Child Adolesc. Psychiatry 44, 177–186 (2005).
Chacko, A. et al. A randomized clinical trial of Cogmed Working Memory Training in school-age children with ADHD: a replication in a diverse sample using a control condition. J. Child Psychol. Psychiatry 55, 247–255 (2014).
Shipstead, Z., Redick, T. S. & Engle, R. W. Is working memory training effective? Psychol. Bull. 138, 628–654 (2012).
Melby-Lervag, M. & Hulme, C. Is working memory training effective? A meta-analytic review. Dev. Psychol. 49, 270–291 (2013).
Wass, S. V., Scerif, G. & Johnson, M. H. Training attentional control and working memory — is younger, better? Dev. Rev. 32, 360–387 (2012).
Anguera, J. A. et al. Video game training enhances cognitive control in older adults. Nature 501, 97–101 (2013).
Astle, D. E. et al. Cognitive training enhances intrinsic brain connectivity in childhood. J. Neurosci. 35, 6277–6283 (2015).
Schiller, P. H. in Models of the Visual Cortex (eds Rose, D. & Dobson, V. G.) 62–70 (Wiley, 1985).
Johnson, M. H. Cortical maturation and the development of visual attention in early infancy. J. Cogn. Neurosci. 2, 81–95 (1990).
Canfield, R. L. & Haith, M. M. Young infants visual expectations for symmetrical and asymmetric stimulus sequences. Dev. Psychol. 27, 198–208 (1991).
Haith, M. M. & McCarty, M. E. Stability of visual expectations at 3.0 months of age. Dev. Psychol. 26, 68–74 (1990).
Johnson, S. P., Amso, D. & Slemmer, J. A. Development of object concepts in infancy: evidence for early learning in an eye-tracking paradigm. Proc. Natl Acad. Sci. USA 100, 10568–10573 (2003).
Richards, J. E. & Holley, F. B. Infant attention and the development of smooth pursuit tracking. Dev. Psychol. 35, 856–867 (1999).
Luna, B., Velanova, K. & Geier, C. F. Development of eye-movement control. Brain Cogn. 68, 293–308 (2008).
Csibra, G., Tucker, L. A. & Johnson, M. H. Neural correlates of saccade planning in infants: a high-density ERP study. Int. J. Psychophysiol. 29, 201–215 (1998).
Luna, B. et al. Maturation of widely distributed brain function subserves cognitive development. Neuroimage 13, 786–793 (2001).
Ordaz, S. J., Foran, W., Velanova, K. & Luna, B. Longitudinal growth curves of brain function underlying inhibitory control through adolescence. J. Neurosci. 33, 18109–18124 (2013).
Bull, R., Espy, K. A. & Wiebe, S. A. Short-term memory, working memory, and executive functioning in preschoolers: longitudinal predictors of mathematical achievement at age 7 years. Dev. Neuropsychol. 33, 205–228 (2008).
Steele, A., Karmiloff-Smith, A., Cornish, K. & Scerif, G. The multiple subfunctions of attention: differential developmental gateways to literacy and numeracy. Child Dev. 83, 2028–2041 (2012).
Barnes, J. J. M., Woolrich, M. W., Baker, K., Colclough, G. L. & Astle, D. E. Electrophysiological measures of resting state functional connectivity and their relationship with working memory capacity in childhood. Dev. Sci. http://dx.doi.org/10.1111/desc.12297 (2015).
Hagerman, P. J. The fragile X prevalence paradox. J. Med. Genet. 45, 498–499 (2008).
Hoeft, F. et al. Fronto-striatal dysfunction and potential compensatory mechanisms in male adolescents with fragile X syndrome. Hum. Brain Mapp. 28, 543–554 (2007).
Kwon, H. et al. Functional neuroanatomy of visuospatial working memory in fragile X syndrome: relation to behavioral and molecular measures. Am. J. Psychiatry 158, 1040–1051 (2001).
Hoeft, F. et al. Region-specific alterations in brain development in one- to three-year-old boys with fragile X syndrome. Proc. Natl Acad. Sci. USA 107, 9335–9339 (2010).
La Fata, G. et al. FMRP regulates multipolar to bipolar transition affecting neuronal migration and cortical circuitry. Nat. Neurosci. 17, 1693–1700 (2014).
Goncalves, J. T., Anstey, J. E., Golshani, P. & Portera-Cailliau, C. Circuit level defects in the developing neocortex of Fragile X mice. Nat. Neurosci. 16, 903–909 (2013).
Scerif, G., Cornish, K., Wilding, J., Driver, J. & Karmiloff-Smith, A. Visual search in typically developing toddlers and toddlers with Fragile X or Williams syndrome. Dev. Sci. 7, 116–130 (2004).
Scerif, G., Longhi, E., Cole, V., Karmiloff-Smith, A. & Cornish, K. Attention across modalities as a longitudinal predictor of early outcomes: the case of fragile X syndrome. J. Child Psychol. Psychiatry 53, 641–650 (2012).
Cornish, K., Cole, V., Longhi, E., Karmiloff-Smith, A. & Scerif, G. Mapping developmental trajectories of attention and working memory in fragile X syndrome: developmental freeze or developmental change? Dev. Psychopathol. 25, 365–376 (2013).
Farzin, F., Rivera, S. M. & Whitney, D. Resolution of spatial and temporal visual attention in infants with fragile X syndrome. Brain 134, 3355–3368 (2011).
Gilbert, C. B. in Principles of Neural Science 5th edn (eds Kandel, E. R., Schwartz, J., Jessel, T., Siegelbaum, S. A. & Hudspeth, A. J.) 623 (McGraw-Hill Education, 2013)
The authors thank their team members and collaborators for all discussions and ideas informing the points raised here. In particular, D. Astle and K. Baker were instrumental in developing the authors' thinking on functional connectivity development and functional gene networks. The overview of the research and models presented herewith were funded by two ongoing James S. McDonnell Foundation Scholar Awards (to D.A. and G.S.), US National Institutes of Health grants P20GM103645 and R01 MH099078 (to D.A.), and past project grants by the Wellcome Trust, Oxford University Press Fell Fund and Newlife Foundation (to G.S.).
The authors declare no competing financial interests.
- Working memory
A cognitive operation that involves manipulating the contents of short-term memory to direct goal-relevant action.
- Attentional network task
(ANT). An attentional cueing paradigm designed to provide separable indices of alerting, orienting and executive attention.
- Executive control functions
Functions deployed across modalities to implement task goals, including maintenance of working memory (also known as updating), inhibition of responses (also known as inhibitory control) and cognitive flexibility (also known as shifting).
- Attentional biases
Processes by which rich sensory, motor or internally held information is modified by attention to enhance the processing of aspects that are relevant to the task at hand and to inhibit task-irrelevant dimensions.
Efferent flow of information away from a lower cortical region to a higher cortical region.
Afferent flow of information from a higher cortical region to a lower cortical area.
An emerging field that identifies functional coupling of brain regions to form networks by assessing correlated activity using functional magnetic resonance imaging analyses.
- Contextual cueing
A visual search paradigm designed to improve attention selection of targets that appear repeatedly in the same scene (context) compared with attention directed towards targets that appear in novel contexts.
- Polygenic risk
Genetic risk for a particular phenotype (for example, the likelihood of attention deficit hyperactivity disorder diagnosis) captured as the cumulative effect of differences at multiple genetic loci.
- Functional gene networks
Genes operating in concert to regulate particular neural or developmental functions (for example, dendritic dynamics and receptor clustering, intracellular transport and regulation of gene transcription).
The outcome of a cognitive or neural training regime that may improve untrained tasks that use the specific skill being trained (such as attention), improve closely related functions (referred to as narrow transfer) or improve more-distally related system functions (referred to as wide or far transfer; for example, mathematical achievement or intelligence improving after attention training).
About this article
Cite this article
Amso, D., Scerif, G. The attentive brain: insights from developmental cognitive neuroscience. Nat Rev Neurosci 16, 606–619 (2015). https://doi.org/10.1038/nrn4025
Nature Reviews Neuroscience (2022)
Physical and Engineering Sciences in Medicine (2022)
SN Computer Science (2022)
Early Childhood Education Journal (2022)
Journal of Neurodevelopmental Disorders (2021)