Developmental improvements in reaching correction efficiency are associated with an increased ability to represent action mentally

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Highlights

  • Motor imagery and online control follow parallel developmental paths in childhood.

  • Developmental improvements in motor imagery predict faster online control of reaching.

  • Action representations appear critical to the development of important motor skills.

Abstract

We investigated the purported association between developmental changes in the efficiency of online reaching corrections and improved action representation. Younger children (6–7 years), older children (8–12 years), adolescents (13–17 years), and young adults (18–24 years) completed a double-step reaching paradigm and a motor imagery task. Results showed similar nonlinear performance improvements across both tasks, typified by substantial changes in efficiency after 6 or 7 years followed by incremental improvements. Regression showed that imagery ability significantly predicted reaching efficiency and that this association stayed constant across age. Findings provide the first empirical evidence that more efficient online control through development is predicted, partly, by improved action representation.

Introduction

The ability to correct one’s movement mid-flight in response to unexpected environmental changes has received much attention in the literature of late (e.g., King et al., 2012, Ruddock et al., 2014, Wilson and Hyde, 2013). This so-called online control of movement is seen as a marker of the nervous system’s capacity to interact effectively with an unpredictable and fluid environment, a critical feature of a mature motor system. Despite this, little is known about its development through childhood and beyond. The limited evidence that is available, however, suggests a nonlinear maturation. Specifically, during early childhood (prior to 8 years) mid-movement corrections to reaching following unexpected target perturbation are slow and highly variable. However, by mid-childhood (around 8 years), the efficiency with which a child can engage in these types of online corrective actions improves substantially. This period is followed by more subtle performance improvements into adolescence and beyond (King et al., 2012, Ruddock et al., 2014, Wilson and Hyde, 2013).

For example, Wilson and Hyde (2013) recently compared the performance of younger (6–7 years), mid-aged (8–9 years), and older (10–12 years) children, as well as young healthy adults, on the well-validated double-step reaching task (DSRT). Participants were required to reach for one of three targets presented on a touch screen. For most trials the target remained stationary for the duration of movement, whereas for the remaining trials the target jumped at movement onset (jump trials). During this task, non-jump trials are thought to place few demands on online corrective systems because the target remains stationary throughout movement. That is, presuming that the initially programmed motor command is accurate, it can unfold unchanged. Conversely, the unexpected target perturbation that occurs during jump trials renders the initial motor command inaccurate. As such, successful trial completion is dependent on how efficiently an individual can update the motor command in real time and, hence, facilitate the timely redirection of the limb toward the newly cued target. Consequently, immature or impaired online control manifests as poor performance on jump trials relative to non-jump, a profile shown by patient groups where deficits in the online control of movement are core symptoms, including parietal lesion patients (Blangero et al., 2008, Gréa et al., 2002, Ochipa et al., 1997). Interestingly, Wilson and Hyde (2013) showed that whereas jump trial reaching speed (relative to non-jump) was relatively slow during early childhood (6–7 years), it decreased substantially by middle childhood (8–9 years) and remained relatively stable into older childhood. This age-related improvement in accounting for target perturbation was confirmed by kinematic analyses that showed a significant reduction in time to reach trajectory correction values between younger and middle childhood, where they then stabilized into later childhood (i.e., 10–12 years). Importantly, this developmental profile of online control is a consistent feature of the small number of developmental studies into the online control of reaching (King et al., 2012, Ruddock et al., 2014).

From a computational perspective, the ability to engage in online reaching corrections is thought to depend heavily on an individual’s ability to represent action at an internal neural level. Specifically, the nervous system is thought to use an efferent copy of the impending motor command to anticipate the limb trajectory should the movement unfold as anticipated. On movement initiation, actual visual and proprioceptive in-flow becomes available and is compared with the predicted sensory information (as per the action representation) in real time. In case of a mismatch (e.g., following unexpected target perturbation), an error signal is generated which must then be integrated seamlessly with the unfolding motor command, affording fluent and efficient correction to the moving limb (Desmurget & Grafton, 2000). By anticipating the sensory consequences of movement, this predictive modeling system allows the nervous system to circumvent sensory processing delays (which can exceed 250 ms; Frith, Blakemore, & Wolpert, 2000) with minimum lag (Desmurget and Grafton, 2000, Shadmehr et al., 2010). In neural terms, this system appears to be supported by a functional loop between connections across motor and frontal cortices and parietal and cerebellar networks (Andersen and Cui, 2009, Izawa and Shadmehr, 2011, Mulliken et al., 2008). This neurocomputational modeling is supported by a strong body of indirect (Hyde and Wilson, 2011a, Hyde and Wilson, 2011b, King et al., 2012, Ruddock et al., 2014, Wilson and Hyde, 2013) and direct (Hyde, Wilmut, Fuelscher, & Williams, 2013) empirical evidence demonstrating that the efficiency with which individuals are able to implement online control is dependent on their capacity to generate and integrate internal “neural” action representations with incoming sensory information. Accordingly, it is generally argued that the nonlinear improvement in online control observed between the critical years spanning ages 6 to 12 is subserved by an improved ability to generate and/or use internal “action” representations (King et al., 2012, Wilson and Hyde, 2013). Interestingly, this developmental progression coincides with the protracted maturation of the parietal cortices and their projections to frontal structures (see Casey, Tottenham, Liston, & Durston, 2005, for a review) and central nervous system more broadly (Kail, 1991). These neural systems are known to be critical to visually guided reaching in adults (Ferraina et al., 2009, Pisella et al., 2006), with the posterior parietal cortex in particular thought to be critical to integrating sensory (especially visual) inputs with predictive estimates of limb locations (Macuga & Frey, 2014) and possibly involved in generating and processing the error signals that arise following mismatches between the two (Reichenbach, Bresciani, Peer, Bulthoff, & Thielscher, 2011).

Empirical support for the argument that improvements in the proficiency of online control through development are supported, at least partly, by a greater capacity to generate internal representations of action can be found from evidence that the development of motor imagery (MI), an experimental protocol thought to elucidate the integrity of the action representation that ordinarily precedes movement (Decety et al., 1989, Parsons, 1994, Sirigu et al., 1996), follows a similar maturational timeline to that of online control. MI requires participants to mentally represent an action without overt movement taking place (Decety & Grèzes, 1999). Performance of typically developing children and adults has been shown to be subject to the same temporal and biomechanical constraints as actual movements. Indeed, the time taken to imagine a movement correlates closely with subsequent execution times (Decety et al., 1989, Sirigu et al., 1996), with awkward and more physically demanding actions taking longer to imagine (Butson et al., 2014, de Lange et al., 2008, Munzert et al., 2009). This relative functional equivalence is coupled with corresponding neurophysiological similarities, with neuroimaging studies indicating that imagined movements activate similar neural networks to those activated in actual movement (Jeannerod, 2001, Munzert et al., 2009) and corticospinal pathways (e.g., Williams, Pearce, Loporto, Morris, & Holmes, 2012). Consequently, it is largely assumed that MI provides insight into one’s ability to accurately form and monitor the kinds of internal motor representations that support purposive action (de Lange et al., 2008, Jeannerod, 2001, Munzert et al., 2009).

One of the more commonly adopted tasks of MI, the mental limb rotation task, requires participants to judge the laterality of a limb (usually a hand) presented at different rotation angles. Although participants often report imagining rotating their own hand to respond (de Lange et al., 2006, Kosslyn et al., 1998, Parsons and Fox, 1998), the use of an embodied MI strategy to complete the task is corroborated by neuroimaging evidence indicating that participants enlist fronto–parieto (dorsal) circuitry specific to MI (cf. visual imagery that typically enlists more ventral structures; see Kosslyn, Ganis, & Thompson, 2001, for a review). Behaviorally, performance of typically developing children (and adults) has been shown to conform to the kinesthetic and biomechanical constraints of action during MI; that is, children display faster response times when performing physically comfortable rotations compared with impossible or awkward rotations. Indeed, children as young as 5 years who are able to make “left/right” distinctions have been shown to display this profile (Butson et al., 2014). Consequently, it is widely argued that participants engage in an embodied (i.e., MI) strategy to perform the task (Gabbard, 2009, Munzert et al., 2009, ter Horst et al., 2010), although this position is not without critique (see Grafton & Viswanathan, 2014).

Although numerous developmental studies have explored MI performance in children aged 6 to 12 years using the hand rotation task (see Gabbard, 2009, and Butson et al., 2014, for reviews), few studies (e.g., Butson et al., 2014) have compared performance of younger children (i.e., 6–7 years) with that of older children (i.e., 8–12 years). Indeed, much of the research is drawn from comparisons of typically and atypically age-matched developing children (e.g., Deconinck et al., 2009, Williams et al., 2011, Williams et al., 2011), thereby limiting the degree to which inferences can be drawn about the development of MI during the critical 7- and 8-year junction. Still, where available, developmental evidence from the hand rotation task points toward a critical developmental period for MI ability between 6 and 12 years. For example, Caeyenberghs, Tsoupas, Wilson, and Smits-Engelsman (2009) reported nonlinear improvements in MI proficiency in typically developing children aged 7 to 12 years. Specifically, 7- and 8-year-olds were slower and less accurate than 9- to 12-year-olds on the hand rotation task, with no significant differences observed between 9- and 10-year-olds and 11- and 12-year-olds. This evidence was taken to suggest that imagery efficiency improves rapidly during early childhood, with only subtle improvements henceforth. It should be noted, however, that the authors did not analyze whether mental rotation performance was influenced by the biomechanical or postural constraints of real action (as would be expected if participants were using an MI strategy). Therefore, it is difficult to infer whether participants were indeed engaging in a specific MI strategy or an alternative nonmotoric imagery strategy to complete the task.

In a more recent study, Butson et al. (2014) highlighted a similar developmental trajectory of MI capacity showing that 11-year-old children were significantly more accurate, but not significantly faster, than 7- and 8-year-olds on the hand rotation task. When accuracy was accounted for in response time, however, age differences appeared, with the 11-year-olds being significantly more efficient than the 8-year-olds. Notably, the authors tested a group of 5- and 6-year-olds but were unable to include this group in their main analysis because only five of the children met the minimum accuracy requirement. The descriptive data for these five children, however, indicated that performance was constrained by the biomechanical constraints of actual movement. This suggests that children under 7 years of age can perform MI, although with limited accuracy. At age 7, however, there was a considerable jump in accuracy levels, suggesting substantially improved MI performance.

Taken together, this body of research points toward a critical period for the development of MI between 6 and 12 years of age. Given that MI is largely assumed to provide insight into one’s ability to accurately form and monitor mental representations of action, there is compelling evidence that children’s ability to represent action mentally develops in a strikingly similar nonlinear manner to online control of reaching. This hand rotation performance profile across development is unlikely to reflect age-dependent learning effects considering that no feedback on response accuracy was given and the different trial types (i.e., angular rotation, hand orientation, and direction of rotation) were randomized.

Interestingly, the neural networks that support MI overlap considerably with those underpinning the online control of reaching, including the fronto–posterior–cerebellar structures such as the posterior parietal cortex (PPC), premotor cortex, and cerebellum. The cerebellum and PPC in particular are thought to play a critical role for MI (see Macuga & Frey, 2014). As noted, these neural structures, in particular those more posterior structures (e.g., the parietal cortices), undergo rapid maturation between 6 and 10 years of age, the product of a complex reciprocal interaction between endogenous (i.e., neurophysiological and genetics) and exogenous (i.e., environmental) factors (Butson et al., 2014, Caeyenberghs et al., 2009, Casey et al., 2005, Munakata et al., 2004).

To summarize, there is a growing body of theoretical and empirical evidence that online control of reaching shows a nonlinear developmental progression characterized by rapid improvement during the critical 6- to 12-year period. Based on neurocomputational modeling, it is widely assumed that this development is subserved, at least in part, by an improved capacity to generate and/or use action representations. This suggestion is supported by evidence from MI studies showing a similar developmental profile. Despite a strong body of theoretical and indirect empirical evidence suggesting the importance of accurate action representation to individuals’ ability to efficiently correct their reaching online, no developmental study has measured both online control of reaching and MI ability in the same child at any age group. Accordingly, it is difficult to verify the degree to which the well-established trend of improved online control from 6 to 12 years and beyond is in fact associated with a greater capacity to generate and/or engage action representation. Clarifying this issue is critical to our understanding of the development of online control and the neurocognitive mechanisms that support it and to developing appropriate interventions when it develops atypically.

To this end, the aim of this study was to test the purported association between the development of online control reaching efficiency throughout the critical 6- to 12-year period and the capacity to generate internal “neural” action representations; a group of adolescents and adults were included to ensure that child development was considered in the broader context of neuromotor maturation. Online control of reaching was measured using the well-validated DSRT; the ability to generate internal movement representations was measured using a traditional MI task, the hand rotation task. As per earlier investigations, it was predicted that the ability to correct reaching in response to unexpected target perturbation would improve substantially from early childhood (6–7 years) across late childhood (8–12 years) and begin to plateau into adolescence (13–17 years) and early adulthood (18–24 years). Based on neurocomputational modeling suggesting that accurate and efficient internal action representation (i.e., MI) is fundamental to these types of corrective movements, it was expected that MI performance would show a similar developmental profile, indicated by faster response times and higher accuracy levels. For the same reason, it was expected that MI ability would positively predict the efficiency of online control throughout development.

Section snippets

Participants

The sample consisted of 115 participants, from which 13 children aged 6 or 7 years, 1 12-year-old, and 1 13-year-old were removed from the analysis because they failed to reach our minimum accuracy criterion on the hand rotation task (see “Design and analysis” section below). The final sample comprised 100 participants consisting of 15 younger children aged 6 or 7 years (5 boys and 10 girls, Mage = 7.04 years, SD = 0.61), 30 mid-aged children of 8 to 12 years (16 boys and 14 girls, Mage = 10.40 years, SD = 

Developmental comparison of double-step reaching performance

The two-way ANOVA on reaction time revealed a significant main effect for group, F(3, 96) = 14.64, p < .001, ηp2 = .31. No interaction effect and no main effect for trial were observed. Averaged across trials, younger children (663 ms) were significantly slower than mid-aged children (481 ms), p < .001, ηp2 = .33. No significant differences were observed between adolescents (465 ms) and adults (421 ms).

The two-way ANOVA on movement time revealed a significant interaction effect, Wilks’ Λ = .68, F(3, 96) = 15.12,

Discussion

Recent research suggests a critical period of development for the online control of reaching between 6 and 12 years of age characterized by nonlinear improvements in efficiency. Neurocomputational modeling suggests that these improvements may arise, at least in part, as a result of an increased ability to generate and/or implement internal action representations. To date, however, no study has measured both action representation and online control in the same sample of children across

Acknowledgments

Our sincere gratitude extends to the students, parents, and staff of those schools that participated in this research. In addition, we thank the Department of Education and Early Childhood Development (DEECD) for its support. Finally, we thank Tim Miles for his valued assistance during data collection.

References (58)

  • C. Gabbard

    Studying action representation in children via motor imagery

    Brain and Cognition

    (2009)
  • R. Geuze et al.

    Clinical and research diagnostic criteria for developmental coordination disorder: A review and discussion

    Human Movement Science

    (2001)
  • H. Gréa et al.

    A lesion of the posterior parietal cortex disrupts online adjustments during aiming movements

    Neuropsychologia

    (2002)
  • C. Hyde et al.

    Dissecting online control in developmental coordination disorder: A kinematic analysis of double-step reaching

    Brain and Cognition

    (2011)
  • C. Lange-Küttner

    Development of size modification of human figure drawings in spatial axes systems of varying complexity

    Journal of Experimental Child Psychology

    (1997)
  • C. Lange-Küttner

    More evidence on size modification in spatial axes systems of varying complexity

    Journal of Experimental Child Psychology

    (2004)
  • K.L. Macuga et al.

    Differential contributions of the superior and inferior parietal cortex to feedback versus feedforward control of tools

    NeuroImage

    (2014)
  • Y. Munakata et al.

    Developmental cognitive neuroscience: Progress and potential

    Trends in Cognitive Sciences

    (2004)
  • J. Munzert et al.

    Cognitive motor processes: The role of motor imagery in the study of motor representations

    Brain Research Reviews

    (2009)
  • T. Paus

    Mapping brain maturation and cognitive development during adolescence

    Trends in Cognitive Science

    (2005)
  • L. Pisella et al.

    No double dissociation between optic ataxia and visual agnosia: Multiple sub-streams for multiple visuo-manual integrations

    Neuropsychologia

    (2006)
  • M. Plumb et al.

    Online corrections in children with and without DCD

    Human Movement Science

    (2008)
  • K. Van Braeckel et al.

    Movement adaptations in 7- to 10-year-old typically developing children: Evidence for a transition in feedback-based motor control

    Human Movement Science

    (2007)
  • J. Williams et al.

    The relationship between corticospinal excitability during motor imagery and motor imagery ability

    Behavioural Brain Research

    (2012)
  • J. Williams et al.

    Motor imagery ability of children with congenital hemiplegia: Effect of lesion side and functional level

    Research in Developmental Disabilities

    (2011)
  • P.H. Wilson et al.

    The development of rapid online control in children aged 6–12 years: Reaching performance

    Human Movement Science

    (2013)
  • L.S. Aiken et al.

    Multiple regression: Testing and interpreting interactions

    (1991)
  • K. Caeyenberghs et al.

    Motor imagery development in primary school children

    Developmental Neuropsychology

    (2009)
  • M. Conson et al.

    Developmental changes of the biomechanical effect in motor imagery

    Experimental Brain Research

    (2014)
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