Independent Development of Imagination and Perception of …...Data demonstrating Fitts’ law in action imagination and perception suggests that these processes share a common mechanism

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Independent Development of Imagination

and Perception of Fitts’ Law in Late Childhood and Adolescence

Emma Yoxon and Timothy N. Welsh

Version Post-print/accepted manuscript

Citation

(published version)

Yoxon, E., & Welsh, T. N. (2018). Independent Development of

Imagination and Perception of Fitts' Law in Late Childhood and Adolescence. Journal of motor behavior, 50(2), 166-176.

Publisher’s Statement This is an Accepted Manuscript of an article published by Taylor

& Francis in Journal of Motor Behavior on 6-23-2017, available online: http://www.tandfonline.com/

10.1080/00222895.2017.1327408

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This is an Author’s Original Manuscript of an article published by Taylor & Francis in The

Journal of Motor Behaviour on 23/06/2017, available online:

http://www.tandfonline.com/10.1080/00222895.2017.1327408

Independent Development of Imagination and Perception of Fitts’ Law in Late Childhood

and Adolescence

Emma Yoxon and Timothy N. Welsh

Faculty of Kinesiology & Physical Education, Centre for Motor Control

University of Toronto, Toronto, ON, Canada

RUNNING HEAD: Imagination and Perception Development

KEY WORDS: development, mental chronometry, motor imagery, perception

http://www.tandfonline.com/10.1080/00222895.2017.1327408

Imagination and Perception Development 2

Abstract

Data demonstrating Fitts’ law in action imagination and perception suggests that these processes

share a common mechanism. Research has revealed that children demonstrate Fitts’ speed-

accuracy trade-off in imagined actions, and that imagined movement time (MT) becomes more

similar to actual MT as age increases. The relationship between execution, imagination and

perception has yet to be evaluated in children. The current study assessed how imagined and

perceived MT related to actual MT in children and adolescents. It was found that imagined MT

was longer than execution MT across all the age ranges. Perception MT was lower than

execution MT for children and was more consistent with execution MT for adolescents. These

results reflect potential mechanistic differences in action imagination and perception.


Introduction

When humans attempt a motor skill, they may first imagine the action and its outcome.

Accordingly, it is thought that the human brain can simulate the internal components of actions

without actually producing any external movements. For instance, many athletes use

visualization or mental practice as a training tool to mentally take themselves through a routine

or a game without the need for actual physical practice. It is thought that such simulation and

mental practice is an effective training tool because it has been demonstrated that when an

individual imagines themselves performing an action, they activate a neural network that is

similar to the one that is involved in actual movement execution (Hétu et al., 2013; Jeannerod,

2001; Munzert, Lorey, & Zentgraf, 2009). In this sense, it is thought that the brain is effectively

simulating the internal components of movement by activating the neural codes that generate

action offline without leading to overt movement.

This concept of action code-based simulation has been extended beyond the explicit

imagination of action and has been implicated as a more implicit mechanism that unifies the

processes that help us perceive and understand our own actions and the actions of others

(Jeannerod, 2001). In action observation and possibility judgements, for example, it is thought

that the neural simulation of action allows an individual to map an observed movement onto their

own motor system and capabilities, allowing the individual to make accurate judgements about

how possible a given movement is to complete (Chandrasekharan, Binsted, Ayres, Higgins, &

Welsh, 2012; Grosjean, Shiffrar, & Knoblich, 2007; Welsh, Wong, & Chandrasekharan, 2013).

These processes are often referred to as “action perception”. Furthermore, because of the


important role that motor simulations play in perceiving and understanding actions, there has

been growing research in how simulation processes emerge in childhood. Importantly, the

mirroring of others’ actions via motor simulations is thought to play an important role in how

young children learn to link gestures and movements with their meanings and effects (Paulus,

Hunnius, & Bekkering, 2013; Paulus, Hunnius, Vissers, & Bekkering, 2011a, 2011b).

Neural simulation of action

Evidence for neural motor simulation in action imagination and perception has been

broadly drawn from neurophysiological and behavioural studies that have reported that the motor

system is active in and constrains action imagination and perception. First, neuroimaging studies

have clearly demonstrated that when participants imagine themselves performing movements,

there is activation of a neural network that overlaps with that of actual execution (e.g., Stippich,

Ochmann, & Sartor, 2002; see Hétu et al., 2013 for a meta-analysis and comprehensive review).

Additionally, many studies have employed transcranial magnetic stimulation (TMS) to study

changes in the excitability of the corticospinal tract while individuals imagine themselves

performing actions. These studies find that when individuals imagine themselves performing an

action, there is a quantifiable increase in excitability of the corticospinal tract, which suggests

that the motor system is active during action imagination (e.g., Clark, Tremblay, & Ste-Marie,

2004; see Munzert et al., 2009 for review). Similar neurophysiological results have been found

for action perception, wherein previous research has demonstrated that various aspects of the

motor system are active when an individual is making judgements about a movement (Eskenazi,

Rotshtein, Grosjean, & Knoblich, 2012). Importantly, it has also been demonstrated that there is

considerable overlap in the cortical areas shown to be active in action imagination, perception


and execution, suggesting that these processes are in fact part of a singular representational

domain (Grèzes & Decety, 2001).

Behaviourally, evidence for common neural substrates underlying action execution,

imagination and perception (and therefore motor simulation) has been drawn from

demonstrations of the temporal similarities between these three processes. Specifically, many

previous studies have demonstrated the presence of Fitts’ law (Fitts, 1954) in imagined and

perceived movement times (MTs) of cyclical aiming movements. Fitts’ law is a mathematical

equation that describes the relationship between the lowest MTs possible to maintain accuracy

and the difficulty of the manual aiming movements. It can be described by the formal equation:

MT = a + b(ID), where “a” and “b” are constants relating to an individual’s base MT and their

unit increase in MT as a function of the index of difficulty (ID), respectively. The ID component

of this equation can be further broken down as ID = log 2(2A/W), where A is the movement

amplitude between a pair of targets (centre-to-centre distance between the targets) and W is the

width of the targets. Essentially, in a task that requires an individual to touch back and forth

between a target pair, the shortest possible MT in which the person can maintain accuracy will

increase as a function of both movement amplitude and target width (the index of movement

difficulty - ID).

Decety and Jeannerod (1995) first demonstrated the relationship between speed-accuracy

trade-offs in imagination in a study of the walking paths of their participants. The researchers

asked individuals to imagine themselves walking towards door openings that varied in their

distance to the participant and in the width of the opening. Critically, participants demonstrated

Fitts’ law in their imagined walking paths. Specifically, participants’ imagined walking paths

were slower when the door opening was narrower and the walking path was longer. Since this


original experiment, this result has been corroborated and extended to manual movements

similar to Fitts’ original aiming task (Sirigu et al., 1995; Wong, Manson, Tremblay, & Welsh,

2013; Young, Pratt, & Chau, 2009; Yoxon, Tremblay, & Welsh, 2015).

This principle of speed-accuracy trade-offs has also been demonstrated in action

possibility judgements. In these studies, individuals are asked to decide on the possibility of an

aiming movement being executed accurately when it is shown at a given speed. It has been

repeatedly shown that participants choose MTs that are consistent with Fitts’ law; that is, the

lowest MT they judge as being possible to accurately complete the movement increases as the ID

of the observed movement increases (Chandrasekharan et al., 2012; Grosjean et al., 2007; Welsh

et al., 2013; Wong et al., 2013). Together, the results of these studies demonstrate that there is

congruency in the temporal aspects of executed, imagined, and perceived action which suggests

again that these processes share a common representational network and involve similar neural

mechanisms.

Action simulation in children

In recent years, there has also been interest in how action simulation emerges in

childhood. Developmental motor imagery research is similar to research that has been done in

adults in that it has focused on the increasing congruence of actual and imagined MTs across

childhood development (Gabbard, 2009). The results of this research suggest that simulation

processes are likely formed in early childhood as evidenced by the presence of Fitts’ law in the

imagined movements of young children aged approximately seven years; although this

relationship is more evident in older children and adolescents(Caeyenberghs, Tsoupas, Wilson,

& Smits-Engelsman, 2009; Caeyenberghs, Wilson, van Roon, Swinnen, & Smits-Engelsman,


2009). Cross-sectional studies have also revealed that imagined MTs become closer to actual

execution MTs as a child ages, becoming closer to the congruence between these measures seen

in adults as children approach adolescence, suggesting that there is ongoing refinement of action

simulation processes in childhood (approximately six-to-eleven years) (Caeyenberghs, Wilson, et

al., 2009; Smits-Engelsman & Wilson, 2013). Some studies have also demonstrated

developmental differences in the temporal consistency of action imagination between

adolescents and adults (Choudhury, Charman, Bird, & Blakemore, 2007a, 2007b) although

studies with larger age spans of six to nineteen years (Caeyenberghs, Wilson, et al., 2009; Smits-

Engelsman & Wilson, 2013) suggest that these changes are more subtle compared to the changes

seen from early to late childhood and just prior to adolescence.

Interestingly, while developmental aspects of motor imagery have been relatively well

characterized, there has been little focus in developmental changes in the perception and

possibility judgment of human movement. There is some evidence that the motor system is likely

engaged in the imitative processes that are central to motor learning in infancy and toddlerhood

(Paulus et al., 2011b) as well as evidence of action co-representation (the representation of an

observed action in one’s own motor system) in early childhood (Marshall, Bouquet, Thomas, &

Shipley, 2010; Saby, Marshall, Smythe, Bouquet, & Comalli, 2011). Although the results of this

previous research again demonstrate that motor simulation processes are likely intact at a young

age, it remains largely unknown how perceptions of action change as a child’s own motor

repertoire changes.

Therefore, the purpose of the current experiment was to evaluate changes in the temporal

similarity of action possibility judgements with actual MT as a function of age and to assess

differences or similarities in the developmental trajectories of action possibility judgements and


motor imagery. To address this purpose, an experimental paradigm employing action possibility

judgements similar to that of Grosjean, Shiffrar and Knoblich (2007) and a mental chronometry

paradigm similar to those of Wong et al. (2013) and Yoxon et al. (2015) was used. Children

between the ages of seven and sixteen, as well as a control group of adults executed aiming

movements to target pairs with varying accuracy demands. Imagination and perception tasks

employed the same target pairs. It was hypothesized that MTs in action perception, imagination

and execution would conform to Fitts’ law, as action simulation processes should be initially

developed before the age of seven. Of greater theoretical relevance, specific predictions involved

comparisons across the different tasks. If action perception and imagination share a common

representational domain and action simulation is an underlying mechanism for both action

possibility judgements and imagination, then these processes should develop in similar ways.

Specifically, MTs in both action imagination and action possibility judgements should approach

actual execution MTs with age. If this is not the case, it is possible that these processes may have

different underlying mechanisms that develop independently of each other.

Methods

Participants

Thirty-four children between the ages of seven and sixteen (24 Male, 10 Female) and 11

young adults (2 Male, 9 Female, Mean Age = 22.4) were recruited for the study. Two male

children participants were removed because it was disclosed to the experimenter subsequent to

testing that they were diagnosed with unspecified developmental and learning disabilities that

may have impacted motor skills and comprehension of task instructions. All other participants

were reported to be typically developing and had normal or corrected-to-normal vision.


Handedness was collected using a modified version of the Edinburgh Handedness Questionnaire.

All participants, with the exception of two children, were right handed. One of these participants

was left-handed and the other reported no distinct preference for activities of daily living. All

participants provided informed assent and their parents or guardians provided informed consent

prior to testing. All procedures were approved by the University of Toronto Research Ethics

Board.

Study Design and Tasks

The design of the present study was based on previous studies (Chandrasekharan et al.,

2012; Grosjean et al., 2007; Wong et al., 2013). Participants completed action execution,

imagination and perception tasks. The execution task was always performed first, followed by

the remaining two tasks. This order was specifically chosen because previous research has

demonstrated the effect of experience on perceived and imagined MTs (Chandrasekharan et al.,

2012; Wong et al., 2013; Yoxon et al., 2015). Specifically, it has been found that perceived and

imagined MTs more accurately reflect actual execution MTs after task-specific experience. This

increased consistency may reflect experience-based refinement of internal action simulations and

occurs because participants are able to link the perceptual effects of an action with the actual

motor experience during practice. These more closely linked representations of action and effect

can then facilitate imaginations that are more temporally similar to actual execution MTs.

Because of the observed benefit of experience on perception and imagination, the execution task

was performed first to: 1) avoid any variance in the data due to differences in task experience

that a random or completely counterbalanced order would have provided; and 2) give each

individual experience with the execution of the task thereby the best capability of demonstrating

accurate imagination and perception. Essentially, the execution task was performed prior to the


other two tasks to equate all individuals on task-specific experience. Any observed

developmental differences in perception and imagination tasks can therefore be more closely

equated to differences in age and not differences in previous experience with similar tasks.

Although the execution was always performed first, the imagination and perception tasks

were counterbalanced to ensure there were no effects of execution experience just prior to

completing either of these tasks. To confirm that this was not the case prior to the main analysis,

the effect of order in these two tasks was assessed by carrying out a 2 (task: imagination,

perception) by 2 (order: imagination first, perception first) mixed ANOVA with repeated

measures on the task variable on imagined and perceived MTs. Although there was a main effect

of task, F(1,41) = 96.097, p < .01, there was no significant effect of order, F(1,41) = .048, p =

.827, or task by order interaction, F(1,41) = .551, p = .462. These results indicate that there was

no significant effect of task order in relation to imagined or perceived MTs.

Participants were tested individually, under the direct supervision of the experimenter. In

each of the tasks, data collection only proceeded when the experimenter had confirmed that the

participant understood the task. The overall time in testing was between 30 and 45 minutes.

All tasks were performed using a touch screen monitor (3M™ MicroTouch™ Display,

473.8mm (W) x 296.1 mm (H)). In all of the tasks, six sets of two targets varying in target width

and movement amplitude were used. The targets were one of two widths: 2.5 cm or 3.5 cm. The

centre-to-centre measurement (movement amplitude) for a given movement context was one of

7.5 cm, 15 cm or 30 cm for the 2.5 cm target. For the 3.5 cm target, the centre-to-centre

measurement was one of 10.5 cm, 21 cm or 42 cm. These combinations generated two target


pairs for each of the IDs: 2.6, 3.6 and 4.6. The combination of target width and movement

amplitude remained consistent throughout a specific trial.

Execution Task. Participants were seated comfortably in front of a table upon which the

touch screen monitor rested. In a given trial, they were presented with one of the six target pairs.

Beginning with the index finger of their dominant hand on the right side target, participants were

asked to perform ten continuous pointing movements as quickly and accurately as possible

between the two targets. One movement was from the right to the left target and the next from

the left to the right target. They were told that they must move as quickly as possible but that

they must also try to always land “on the line” (i.e., within the target). This sequence was

repeated three consecutive times for each target pair for a total of 30 movements per target

condition. Prior to the experimental trials, the experimenter gave the task instructions and

participants experienced three practice trials, during which they had the opportunity to ask the

experimenter questions about the task and the experimenter could confirm their understanding of

the task demands.

The order of the target combinations was randomized. The accuracy (spatial coordinates

of screen contact) and the time to complete the movements were recorded, by the custom

program, which also displayed the stimuli for analysis offline. A single mean MT was calculated

for each of the six combinations of target width and movement amplitude. Erroneous trials where

there was clearly either computer or human error (a touch recorded on the same side of space

two times in a row or where the touch screen failed to record a touch) were removed prior to

calculation of the mean MT for each target pair. Additionally, touches that fell beyond 35 px

(approximately 1 cm) of the target were considered errors and were also removed at this point.


These procedures resulted in the removal of approximately 4.3% (erroneous trials) and 0.7%

(error) of the individual touches in the execution data overall.

Imagination Task. The experimental set up was consistent with that of the execution

task. In a given trial, participants were presented with one of the six target pairs. They began

with the index finger of their dominant hand on the right side target and imagined executing ten

pointing movements between the targets as quickly and accurately as they could execute the

movements in real time. Similar to the execution task, participants were asked to imagine their

finger moving as quickly as it did in “real life” and to imagine themselves always landing “on

the line” (i.e., on the target). Participants were asked to perform the imagination from an internal

(first person) perspective. They were instructed to lift their finger off the monitor (a maximum of

about 5 cm) for the time required to imagine the movement and then place their finger back onto

the monitor after imagining the movements. They were also instructed that the finger lift should

occur when they imagined their finger lifting off for the first time and the placing of the finger

back onto the monitor should occur when they imagined their finger returning to the right side

target on the tenth movement. This sequence was repeated three times for each target pair for a

total of 30 imagined movements.

Prior to experimental trials, participants experienced three practice trials during which

they had the opportunity to ask the experimenter questions about the task and the experimenter

could confirm the participant’s understanding. The order of the target pairs was randomized. The

total time required to complete the imagination (from finger lift to contact with the touch screen)

was recorded for analysis. A mean MT was calculated for each of the target pairs and this mean

time was derived by dividing the total imagination time by ten to provide a mean MT for each

target pair in a given trial. Trials where there was either computer or human error (where the


touch screen failed to register a touch or the participant admitted to improperly performing the

task) were flagged throughout testing and were removed prior to analysis. This process resulted

in the removal of approximately 2% of the imagination data.

Perception Task. Participants were seated as in the other two tasks. In the perception

task, the touch screen monitor presented two digital photographs of the hand of a young adult

woman performing the execution task from an internal or first-person perspective: the first

picture was of a person with their right index finger on the right side target and the second

picture was of the finger on the left side target (see Figure 1). These photos were presented

alternately to create the apparent motion of the model in the photographs moving between the

two targets. In a given trial, the photos were presented at one of eleven different stimulus onset

asynchronies (SOAs) ranging between 120 ms and 520 ms. The SOA remained constant within a

trial and the trial ended when the participant’s response was recorded. Participants were asked to

judge if it is possible for them to maintain accuracy while moving at the shown speed.

Specifically, they were told that the task was to decide if it was possible or impossible to move

as fast as the hand is moving and still be able to land “on the line” (i.e., to land accurately on the

targets). They were told to pick the best answer (possible or impossible) for their own

performance. Participants would verbally tell the experimenter, “yes” or “no”, if they thought the

movement was possible or impossible to move at the given MT, respectively.

Prior to testing, participants were shown 3 practice trials, two of which represented the

extremes of the SOA/target pair combinations (a high difficulty with the fastest possible

movement and a low difficulty with the slowest possible movement) and the other was a target

pair with ID = 2.6 at SOA = 200 ms to represent a trial that was at neither of the previously

presented extremes. During these practice trials, the experimenter asked the participants


questions to confirm their understanding. For example, the experimenter would ask the

participant why they thought a particular movement was possible or impossible. During this

time, the experimenter also answered any questions the participants had. After confirming their

understanding of the task, participants completed one block of perceptual judgments consisting

of 66 trials (6 target combinations x 11 SOAs). For each of the 6 target pairs, the point at which

participants changed their responses from impossible to possible (this point was the point along

the spectrum of SOAs where the participant answered “yes” twice in a row) was determined and

the SOA at this point was considered to be the minimum MT perceived to be possible for a given

combination of target width and amplitude (or ID). This process generated one data point for

each of the 6 target pairs. A similar type of task has been used several times in adult studies to

assess how individuals perceive an observed action (Chandrasekharan et al., 2012; Eskenazi et

al., 2012; Grosjean et al., 2007; Welsh et al., 2013). Therefore, the current method is consistent

with previous work, but was modified for use with children. Specifically, a single block of trials

(as opposed to multiple blocks of trials) was performed to maintain a relatively short time in

testing, to balance task demands boredom and fatigue, particularly in younger children.

Results

Prior to statistical analysis, child participants were initially divided into two experimental

groups to facilitate analysis of differences between younger children (seven to eleven, n = 18)

and adolescents (12 to 16, n = 14). This division was chosen at this age because it has been

shown to be a critical point for corticospinal maturation (Yeo, Jang, & Son, 2014), motor skill

acquisition (Sugden & Wade, 2013) as well as motor imagery development (Caeyenberghs,

Wilson, et al., 2009; Smits-Engelsman & Wilson, 2013). Specifically, this age seems to delineate

all three of these processes in that there is more rapid development up until approximately eleven


or twelve years, followed by more subtle developmental changes in corticospinal maturation,

motor skill acquisition and motor imagery ability from this age onward.

Fitts’ Law

To determine if MTs in each group and task conformed to Fitts’ speed accuracy trade-off,

a linear regression was calculated between group mean MT for each of the six combinations of

target width and amplitude and ID. For all groups and all tasks, MT was significantly correlated

with ID, confirming the presence of Fitts’ law in all groups and tasks (Figure 2). For equations

and statistics, see Table 1.

Group Differences

An analysis of variance was conducted to further examine within- and between-group

differences between execution, imagination and perception task MTs. Because MTs were found

to conform to Fitts’ speed-accuracy trade-off, a single mean MT was calculated per participant

for each task. These mean MTs were submitted to a 3 (task: execution, imagination, perception)

by 3 (group: child, adolescent, adult) mixed ANOVA with repeated measures on the first factor.

Mauchly’s test indicated that the assumption of sphericity had been violated (2 (2) = 12.42, p <

.01). Therefore, degrees of freedom were corrected using Greenhouse-Geisser estimates of

sphericity (ε = .79). All post-hoc analyses were conducted using Tukey’s HSD.

The analysis revealed a significant effect of task, F(1.57, 62.85) = 71.30, p < .001, where

imagined MTs (M = 483 ms) were significantly higher than those for perception (M = 302 ms)

and execution (M = 364 ms), and perception MTs were significantly lower than those for

execution and imagination. There was also a significant effect of group, F(2,40) = 5.07, p < .05,

where MTs averaged across all tasks for the child group (M = 416 ms) were significantly higher


than those of the adult group (M = 351 ms). There was no significant difference in overall MT

between the children and adolescents, although there was a trend towards lower overall MTs for

the older group (M = 366 ms). Finally, there was a significant group by task interaction, F(3.14,

62.85) = 3.73, p < .05. Post-hoc analysis of the interaction showed that in the child group, there

were significant differences between the mean MTs of all tasks, with MTs in the perception task

being the lowest and MTs in the imagination task the highest (see Figure 3). In the adolescent

group and adult groups, the mean MT for imagination was significantly higher than the mean

MT for both perception and execution, but there were no significant differences between

perception and execution. Between the groups, there were no significant differences between

MTs for the execution or perception tasks, although the difference in execution times between

the younger child group and the other two groups approached significance. In the imagination

task, the child group had significantly higher MTs than the older group and the adult group.

The relationship between age and simulation congruency

To assess the relationship between congruency of action simulation processes (the degree

to which perception and imagination MTs reflect actual execution MTs) and age (i.e., how these

measures change with age), difference scores between mean imagination and execution and

mean perception and execution MTs were calculated for each participant. This analysis only

included the child participants. Pearson’s correlation coefficient and linear regressions were

calculated for the correlation between difference scores for imagination and age as well as the

correlation between difference scores for perception and age. It was found that difference scores

for perception were significantly and positively correlated with age (r = .71, p < .001, y = 20.16 x

– 299.40) such that perception MTs approach actual MTs as age increased (Figure 4-A). In

contrast, there was no significant correlation between the difference scores for imagination and


age (r = .005, p = .98, y = 0.19 x + 121.60), suggesting that these differences were more stable

across the age range (Figure 4-B).

Discussion

The aim of the current study was to quantify the relationship between action execution,

imagination and perception in children and adolescents and to describe how these relationships

change as a function of age. Typically developing children between the ages of seven and sixteen

and young adults completed three tasks, execution, imagination and perception (action

possibility judgement), which involved continuous aiming movements to the same six target

pairs. Overall, the MTs for each of these tasks for each group of participants increased as a

function of the difficulty (ID) of the aiming movement and therefore conformed to Fitts’ law.

The critical finding, however, was that MTs selected in the action possibility judgements task

became increasingly congruent with actual execution MTs as age increased, whereas imagined

MTs did not exhibit a similar developmental change. These results are discussed over the

following sections as they relate to neural simulation in action imagination and perception.

Fitts’ law in imagination and perception

Overall, the presence of Fitts’ law in action perception and imagination across all age

groups demonstrates that action simulation processes are intact and at least partially developed

by the age of seven. This result is in line with the findings of previous motor imagery research,

where the consensus is that the ability to effectively engage in motor imagery is present by

approximately seven years of age (Gabbard, 2009). This finding is also consistent with previous

action observation and imitation research in infancy which has implicated a motor simulation

mechanism for imitation and observational learning (Paulus et al., 2013, 2011a, 2011b). It is


also consistent with the work of Marshall et al. (2010)and Saby et al. (2011) who demonstrated

that children’s movement trajectories change when observing the incongruent movement

trajectory of another person (i.e., a motor contagion effect). In sum, the result that both imagined

and perceived MTs followed Fitts’ speed-accuracy trade-off is additional evidence for motor

system activation in the imagination and perception of movement in children.

The relationship between age and action imagination

In contrast to past work, the current experiment found no age-related differences in the

congruency between imagined and executed MTs. Specifically, although action imagination

times were longest in the youngest children, the difference between real and imagined MTs did

not change as a function of age. Instead, imagined MTs were consistently greater than actual

execution MTs. Although it is not entirely clear why this difference emerges, it should be noted

that the overestimation of imagined MTs is very common for this type of aiming task (see Wong

et al., 2013; Young et al., 2009; Yoxon et al., 2015). Additionally, because the younger

children’s actual execution MTs were numerically (but not significantly) longer than those of the

other groups, the small decrease in imagined MT from the younger to the older group does not

represent a developmental change in the temporal congruency of action imagination. Moreover,

the correlational analysis presented here demonstrates that there is not a consistent change in the

way imagined MTs approached actual MTs as a function of age. Therefore, the ability to imagine

the temporal aspect of movements (i.e., the congruency between real and imagined movements)

does not seem to change from later childhood into adolescence.

This result may be related to the nature of developmental changes in motor imagery in

late childhood. Previous studies (Caeyenberghs, Wilson, et al., 2009; Smits-Engelsman &


Wilson, 2013) included a large number of children in early childhood. It is this early age range

(five to eight years) that seem to have the largest discrepancies between real and imagined MT

(Caeyenberghs, Wilson, et al., 2009; Molina, Tijus, & Jouen, 2008; Skoura, Vinter, &

Papaxanthis, 2009; Smits-Engelsman & Wilson, 2013). It is possible, therefore, that these

differences in younger children under the age of seven (where it is thought that motor imagery

processes are largely developed, see Gabbard, 2009; Molina et al., 2008) contributed to the age-

related differences seen in previous studies. Relatedly, a recent study that evaluated age-related

imagery ability using a self-report questionnaire developed for use with children found no age-

related differences in self-reported ease of imagery in children aged seven to twelve years

(Martini, Carter, Yoxon, Cumming, & Ste-Marie, 2016). Therefore, it is likely that age-related

changes in the temporal congruency of motor imagery did not emerge in the current experiment

because developmental differences in action imagination are more subtle at the age range used in

the current study.

Recent task experience may have also played an important role in the consistency of

motor imagery ability. In the current study, all children executed the pointing task before

imagination and perception tasks. This experience, however, was not provided in past studies.

For example, the study of Caeyenberghs, Tsoupas and colleagues (2009) included many tasks

that were counterbalanced across participants, meaning that execution experience may have

occurred at various times relative to the imagination task. Smits-Engelsman and Wilson (2013)

only used imagination and execution tasks but these were also counterbalanced so that half the

participants began with imagined movements. Recent work has demonstrated the effect of task

experience on the accuracy of imagination and perception of continuous aiming movements

(Chandrasekharan et al., 2012; Wong et al., 2013; Yoxon et al., 2015) suggesting that task


experience may lead to more accurate imagined and perceived MTs. Essentially, it is thought that

experience with a given task can generate a more accurate representation of an action and its

associated perceptual effects – leading to more accurate imagination and perception of

movement. Therefore, it is also possible that the very recent task experience in the current study

may have led to more accurate perception-action representations, particularly in the younger

children who may lack in general motor experience compared to older children and adolescents.

However, if this were the case, similar effects would be expected in action perception, because

this process would also be affected by experience (as in Chandrasekharan et al., 2012).

Therefore, although the lack of age-related changes in action imagination (in comparison to the

results of other studies) may be related to recent task experience, it is more likely that this effect

is due to more subtle developmental differences in this task, from late childhood to adolescence.

The relationship between age and action perception

In contrast to the results of action imagination, the difference between actual MTs and

MTs selected as possible in the perception task decreased as a function of age. Specifically,

younger children were shown to have a larger disparity between actual execution MTs and

perceived MTs than adolescents, indicating that the younger participants overestimated their

abilities (shorter estimated MTs mean more efficient performance). These action possibility

judgements were more congruent with actual MTs in the adolescents, indicating that the

discrepancy and overestimation of their abilities relative to their actual abilities decreased as a

function of age. Developmental research of risk taking and injury proneness in children would

suggest that this overestimation of abilities in younger children is not uncommon (Sandseter &

Kennair, 2011). It is known, for instance, that children regularly engage in “risky play” or

situations that provide a thrilling experience (such as jumping from heights and moving at high


speeds) as part of normal cognitive and motor development. These behaviours are likely related

to the abilities of younger children to estimate their abilities and the consequences of their

actions (Sandseter & Kennair, 2011). Specifically, children are more likely to overestimate their

abilities in tasks beyond their abilities than adults (Plumert, 1995).

Although these abilities may also be influenced by social and individual factors, there is

also an evident developmental component as older children and adolescents seem to be able to

more accurately characterize their abilities than younger children (Plumert & Schwebel, 1997). It

should be noted here that the body of literature on children’s ability to estimate what they are

capable of performing goes beyond what is very briefly discussed here. For example, authors

have examined the individual and developmental differences that impact a child’s risk perception

for a given task (e.g.,Schwebel & Bounds, 2003; Schwebel & Plumert, 1999, see Sandseter &

Kennair, 2011 for review). Critically, however, whereas the body of literature on children’s

assessment of ability focuses mainly on how children perceive their own abilities when they are

presented with a given task, the current study asks children to make a judgement on a movement

that they are actively observing. To the authors’ knowledge, this is the first time that action

perception and possibility judgements have been studied in this way. For this reason, although

developmental differences in ability estimation and risk taking are likely related to the results of

the current study, these initial conclusions should be considered preliminary and with some

caution. Future more dedicated and expansive work is needed to assess the interpretation of the

results and conclusion.

The current study’s results could also be accounted for by typical perceptual-motor

development. From late childhood to adolescence (the age range used in the current study),

children begin to engage in more complex activities that present new challenges (e.g. engaging in


more open motor skills). These new challenges, coupled with neurophysiological development,

are thought to preclude improvements in perception-action coupling (Sugden & Wade, 2013).

Essentially, more experience and a larger motor repertoire afford older children and adolescents

stronger associations between actions and their effects, allowing them to better predict the

outcomes of their actions. This effect is evidenced by experimental work demonstrating the

increased ability of older children to intercept objects (Chohan, Verheul, Van Kampen, Wind, &

Savelsbergh, 2008) and to plan safe movement trajectories (Chihak et al., 2010; Plumert &

Schwebel, 1997). In the context of the current experiment, weaker perception-action coupling in

younger children could have led to less accurate action possibility judgements in that an inability

to link the perceptual effects of an action (observed by the participants) with a specific motor

pattern could hinder a child’s ability to make effective predictions about the action’s possibility.

Consequently, children may be choosing faster or “riskier” perceived MTs, as they often do in

more ecologically valid situations, because they cannot adequately predict the true consequences

of the observed action. Although the presence of Fitts' law in their perception (as well as

imagination) MTs suggests that action simulation occurs and is intact, underdeveloped

perception-action coupling may have interfered with the transformation of perception-to-action.

This underdevelopment may have also interfered with the ability to relate the motor simulation to

an accurate choice of possible MT.

Another related important consideration is that action possibility judgements, in

comparison to action execution and imagination, rely on the comparison between what is

observed and what is simulated as well as a determination of a threshold for possibility. The

factor associated with determining the threshold of what is possible or not has been addressed to

a certain degree earlier in the discussion of risk-taking. That is, it is possible that there is a


greater distinction between actual and perceived MTs because the younger children have a

relatively low threshold for that they think is possible for them to perform - there is an

overestimation of their capabilities because of a low threshold/cut-off for what is possible. The

factor that might have led to challenges for the youngest group was the comparison between the

observed movements on the screen and the simulation. That is, the presentation of a young adult

arm in the perception task may have led to challenges in self-other matching in younger children.

Past research has demonstrated that children may more readily represent the actions and hands of

younger (i.e. age matched) children (Liuzza, Setti, & Borghi, 2012; Marshall et al., 2010).

Therefore, it is possible that the participants in the current study were unable to fully represent

the adult hand, or were unable to match this representation of "other" on the screen with

representation of the “self” in the simulation. Younger children, therefore, may have been unable

to produce a possibility judgement that is congruent with their own capabilities, but is more

congruent with the assumed abilities of the observed hand. The results of Welsh et al. (2013)

suggest that adults can successfully make judgements for people with different capabilities

(notably between child and adult performers) and are not driven to make the judgements for the

person they are observing (child vs. adult model). In this context, it might be instructive to note

that perceived MTs for each group were consistent with each other (i.e., perceived MTs for the

youngest group were not different from those of the adolescents and the adults). Because the

present work was the first to target this question of action perception and possibility judgements

using this approach, it was not possible to anticipate this distinction for the youngest group and

so a child vs. adult model comparison was not included in the design. Overall, it is not clear if

the differences between perception and execution MTs for the youngest group were due to an

inappropriately low threshold for distinguishing between what is possible and impossible, a


relatively poor ability to distinguish and compare between “self” and “other”, or some other

mechanism. Future research that more specifically addresses these issues (perhaps as in

Chandrasekharan et al., 2012, and Welsh et al., 2013) is needed.

Conclusions

The critical finding of the current study was that the developmental trajectories of

imagination and perception from late childhood to adolescence are different. Between the ages of

seven and sixteen years, the temporal congruency between real and imagined MTs remained

relatively stable, whereas the congruency between MTs selected in the action possibility

judgement task and real MTs increased as age increased. This result stands in contrast to the

prediction that the temporal congruence in these two tasks should increase in a similar way due

to their shared mechanisms. Therefore, it is likely that there are differences in the underlying

cognitive and neural mechanisms of action imagination and perception. Results compliant with

Fitts’ law in both tasks and the stability of action imagination suggest that neural motor

simulation is developed by late childhood. Further, results compliant with Fitts’ law in the action

possibility judgement task indicate that, at some level, an action simulation process is intact.

However, the underestimation of MTs in the action possibility judgements may be due to

differences in an additional self-other, perception-action matching processes, or threshold

setting; all of which are necessary to form accurate judgements. Specifically, action possibility

judgements involve the comparing or relating of observed action effects to one’s own

representation of the task. Therefore, although children may be able to neurally simulate actions,

their ability to effectively use these simulations to predict movement outcomes likely continues

to develop into adolescence.


Acknowledgements

This research was supported by grants from the Natural Sciences and Engineering Research

Council and the Ontario Ministry of Research and Innovation to T.N.W.


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Tables and Figures

Figure 1. An example of the pictures that were displayed in the perception task. Images 1 (hand

on the right side target) and 2 (hand on the left side target) were alternated at a range of

stimulus onset asynchronies (SOAs) to create the apparent motion of the hand.


Table 1. Fitts’ law equations and statistical analysis for the linear regressions calculated between

MT and ID for each of the tasks and groups.

Children Aged 7-11 Equation

𝑴𝑻 = 𝒂 + 𝒃 ∙ 𝑰𝑫 R2 p

Execution MT = 116.1 + 81.13(ID) .98 .0002

Imagination MT = 324.0 + 59.99(ID) .95 .0009

Perception MT = 25.78 + 75.56(ID) .99


Figure 2. Linear regressions between index of difficulty (ID) and movement time for each of the

three tasks, for each of the three experimental groups.


Figure 3. Mean imagined MTs for each of the execution, perception and imagination tasks.

Asterisks indicate significant (Tukey’s HSD, p < .05, CV = 79.9) within group

differences between the tasks.


Figure 4. Difference scores between imagination and execution (A) and perception and

execution (B) as a function of age. Note: This analysis includes only child participants.

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Independent Development of Imagination and Perception of …...Data demonstrating Fitts’ law in action imagination and perception suggests that these processes share a common mechanism