Quantifying Visuomotor Maturation From Middle Childhood to Adolescence

ABSTRACT

Proficiency in fine motor skills improves significantly from childhood into adolescence; however, a longitudinal approach has not been used to examine the maturation trajectory of eye–hand coordination. Capturing individual performance changes by following the same cohort over time could offer unique insight into the developmental processes. This study assessed eye and hand kinematics during the execution of a prehension task in typically developing children (n = 17) tested at two time points: middle childhood (mean age: 9.14 years) and adolescence (mean age: 13.51 years). Results showed significant improvement in overall performance, which was due to shorter fixation duration, faster reaching duration, and shorter object placement duration. Temporal pattern of eye–hand coordination became more mature (adult‐like) when picking up an object, indicating efficient coordination between the grasp and reach movements. Results demonstrate that kinematic analysis of prehension and eye–hand coordination provides a sensitive assay of the maturation trajectory of visuohaptic integration during prehension. These findings may support new methods for assessing fine motor skills and enhance the diagnosis of neurodevelopmental disorders linked to visuomotor impairments.

1. Introduction

Visuomotor control is fundamentally important for the efficient performance of many recreational, academic, and occupational tasks. At the core of visuomotor control lies eye–hand coordination, which refers to how the hands and eyes work together to enable fast, accurate, and precise movements. This coordination is crucial for performing basic motor tasks as well as more complex, goal‐directed activities that become increasingly demanding with age. Over the years, the development of visuomotor control has been studied using various approaches (Cancer et al. 2020; Flatters et al. 2014; Rueckriegel et al. 2008; Sánchez‐González et al. 2022); however, there is a dearth of studies examining the maturation of eye–hand coordination in middle childhood. This study sought to assess changes in visuomotor control by tracking eye and limb kinematics during a sequential prehension task in a cohort of typically developing school‐aged children, followed longitudinally from middle childhood through adolescence.

Goal‐directed upper limb movements rely on visual input to plan and execute sequences of actions. Visual function comprises various components, including acuity, contrast sensitivity, and binocularity. In typically developing children, visual acuity and stereoacuity improve rapidly during the postnatal period, approaching adult levels by the age of 5–7 years (Ciner et al. 1991; Leat et al. 2009). In parallel, the accommodative and vergence systems develop to ensure clear single binocular vision (Sreenivasan et al. 2016; Yang et al. 2002). Further maturation and fine‐tuning of visual functions likely continue during middle childhood and into adolescence (Meier and Giaschi 2017; Narasimhan et al. 2012), which may contribute to visuomotor development (Niechwiej‐Szwedo et al. 2023; Rakshit et al. 2024; Sánchez‐González et al. 2022). The crucial role of normal vision in the development of fine motor skills is supported by research showing that children with abnormal binocular vision due to amblyopia and/or strabismus have difficulties with fine and gross motor skills, which impacts functional activities (Kelly et al. 2025). The association between vision and the execution of motor skills in typically developing children is an active area of investigation. Understanding how visuomotor control evolves over time is essential for establishing developmental benchmarks and evaluating the effectiveness of interventions aimed at enhancing motor coordination and fine motor skills. To our knowledge, there are no published longitudinal studies concurrently investigating changes in vision and visuomotor control in typically developing children.

Fine and gross motor skills improve rapidly in the first 5 years (Franchak and Adolph 2024), which is followed by a period of gradual improvement in the efficiency, accuracy, and precision of motor performance. Various upper limb tasks have been used to evaluate motor development (Bourgeois and Hay 2003; Flatters et al. 2014). Broadly speaking, manual tasks can be characterized into four categories: (1) discrete aiming or reaching to a target, (2) prehension and object manipulation, (3) manual tracking, and (4) bimanual coordination. Using these tasks to assess visuomotor coordination provides insight into the development of different sensorimotor control processes. For example, kinematic analysis of reaching movements, including peak acceleration and the initial trajectory, allows for the evaluation of the ballistic feedforward control, while examining the deceleration interval provides insight into feedback control (Elliott et al. 2020). Tasks involving object manipulation provide insight into visuohaptic integration, that is, the integration of visual cues about an object's material properties with tactile feedback during handling, offering valuable insights into multisensory processing capabilities (Burr and Gori 2012). Conversely, manual tracking relies on encoding target motion to predictively adjust hand movement to match target velocity, and on feedback monitoring to ensure accuracy (Ferguson et al. 2015). The ability to use both hands to perform fine motor skills relies on the maturation of the corpus callosum and sensorimotor integration across hemispheres (Gerloff and Andres 2002). Cross‐sectional studies show a significant increase in motor capacity and performance proficiency between the ages of 5 and 13 years, demonstrated by shorter reach durations, higher peak velocities, and better endpoint precision (Bourgeois and Hay 2003). Object manipulation, such as grasping or placing (Alramis et al. 2016; Niechwiej‐Szwedo, Meier, et al. 2020), and bimanual coordination (Nobusako et al. 2025) also improve significantly, becoming adult‐like after the age of 11 years.

Currently, there is a lack of detailed characterization of the maturation profile of limb kinematics and eye–hand coordination for upper limb movements using a longitudinal approach. A review of the literature revealed only one study that examined changes in limb kinematics in children followed over a 3‐year period (Bäckström et al. 2025). The study by Bäckström et al. focused on children with autism and included 17 typically developing children aged 7–11 years old. A prehension sequencing task was used, which provided insight into motor planning and subtask integration, that is, coordination between reaching, grasping, transport, and placement. Typically developing children exhibited a significant increase in motor planning efficiency and higher integration among the sequence's subtasks, while these processes were less developed in the autistic group. Overall, results from this study highlight the utility of using a kinematic approach to provide a detailed characterization of typical and atypical visuomotor development.

Improvements in the performance of manual tasks as children get older may also be related to changes in temporal eye–hand coordination. To date, only one study has directly compared eye–hand coordination between children and adolescents using the same task (Niechwiej‐Szwedo, Wu, et al. 2021). Results demonstrated that younger children tend to have a longer duration between eye and hand movement initiation, and a longer fixation on the object that was manipulated. Other studies have shown differences in eye–hand coordination in children with neurodevelopmental disorders (Abid et al. 2025). For example, children with amblyopia (Niechwiej‐Szwedo et al. 2025) and developmental coordination disorder (DCD) (Wilmut et al. 2006) exhibit longer eye–hand delays and prolonged fixation duration when compared to typically developing peers. Thus, it is plausible that maturation of eye–hand coordination could be an important marker of developmental changes in sensorimotor processing. Employing a longitudinal design is crucial for understanding the developmental trajectories and how individual children mature. Such knowledge could be used to detect atypical development and assess the impact of interventions.

The present study examined changes in limb kinematics and eye–hand coordination during the performance of a precision grasping and placement task in typically developing children tested longitudinally. Specifically, the bead‐threading task used in this study was developed to assess eye–hand coordination using eye tracking and limb kinematics, which provide insight into the neuromotor control of prehension movements. The bead‐threading task comprises a sequence of four movements that enable assessment of reach‐to‐grasp coordination as well as object manipulation skills, as the bead must be accurately positioned onto the needle. The task has good test–retest reliability (Niechwiej‐Szwedo, Nouredanesh, et al. 2021) and is child‐friendly (Niechwiej‐Szwedo, Meier, et al. 2020). Our previous cross‐sectional study demonstrated significant differences in bead‐threading task performance and eye–hand coordination between children younger than 11 years old and adolescents (Niechwiej‐Szwedo, Wu, et al. 2021). Therefore, the current study included children younger than 11 years in the first session. The same cohort of children returned for follow‐up testing 2.5 years later, allowing us to examine the maturation trajectory of limb kinematics and eye–hand coordination. Importantly, children tested in the study also underwent vision testing, including an assessment of visual acuity, stereoacuity, vergences, and accommodative function. It was hypothesized that limb kinematics would show significant improvement, reflecting higher efficiency in motor skill performance. Additionally, it was expected that the eye–hand coordination pattern would display a more mature pattern characterized by a shorter difference in eye–hand latency. A secondary hypothesis proposed that children with poorer binocular vision would exhibit less efficient motor performance.

2. Methods

2.1. Participants

The study received ethics clearance from the institutional research ethics board. Written informed consent and assent were provided by the legal guardians and the child participant, respectively. This longitudinal study included 17 typically developing children (six girls and 11 boys) who were tested in two sessions. Children were first tested at an average age of 9.14 (SD 1.17) years (range: 6.33–10.92 years). The second session was scheduled when children were on average 13.51 (SD 1.14) years old (range: 11.25–15.92 years). All children were right‐hand dominant, which was established by asking them which hand they used for writing and drawing.

2.2. Materials

The task is illustrated in Figure 1 and described in detail in our previous work (Niechwiej‐Szwedo, Wu, et al. 2021). The bead‐threading apparatus consists of a bead holder positioned 35 cm from the chinrest and a vertical needle (height: 12 cm; diameter: 0.3 cm) located 15 cm in front of the chinrest, both aligned with the body midline. A wooden bead (diameter: 1.0 cm; hole diameter: 0.5 cm) was placed on the bead holder by the experimenter before the start of each trial.

FIGURE 1.

Experimental setup for the bead‐threading task (reprinted from Niechwiej‐Szwedo, Wu, et al. 2021).

Eye position was recorded using a video‐based eye tracker (Eyelink 2, SR Research, Ottawa) at a sampling rate of 250 Hz. The eye tracker was calibrated using a 5‐point calibration array presented on a computer monitor placed 80 cm away from the participant. Each calibration was followed by a validation trial to ensure that the spatial eye‐tracking error was <1°. Hand position was recorded by the Optotrak motion capture system (NDI, Waterloo) at a sampling frequency of 250 Hz. To assess hand kinematics, infrared markers were placed on the thumb and index finger of the preferred hand. The eye tracker and motion capture system were synchronized using a transistor–transistor logic (TTL) pulse.

Eye position data were filtered at 80 Hz, and hand kinematic data were filtered at 10 Hz using a low‐pass second‐order Butterworth filter. The position data were differentiated to obtain the velocity trajectory (two‐point differentiation). A custom‐written Matlab script was used to identify the initiation and termination of reaches toward the bead and the needle using velocity criteria. Reach initiation was detected when the index finger velocity exceeded 0.030 m/s for 20 ms, and reach termination was detected when finger velocity dropped to 0.100 m/s. The four components of the prehension sequence were defined based on the movement initiation and termination events: (1) reach to the bead (reach initiation to reach termination); (2) grasping (reach termination to the next reach initiation); (3) reach to the needle (reach initiation to reach termination); and (4) threading (reach termination to next movement [i.e., hand away from the needle] initiation). The reaching interval was partitioned into two components: the acceleration interval, defined as extending from reach onset to the time of peak reach velocity, and the deceleration interval, defined as extending from the time of peak reach velocity to reach termination. Total movement time, which is an overall measure of performance, was defined as the interval from reach‐to‐bead initiation to the end of thread duration.

2.3. Procedure

Following the consent procedure, children underwent vision testing, which was performed by a registered optometrist. Testing included an assessment of binocular and monocular (left and right eye) visual acuity at distance (6m) using the Lovie–Baily chart and near (0.4m) using the Lighthouse Continuous Text Card for Children. Acuity was defined as the line where three out of five letters were reported correctly. Stereoacuity or depth perception was measured using the Randot Stereoacuity Test (Stereo Optical Company, Chicago, IL, USA) following the publisher's guidelines.

The vergence system was measured using prism bars in free space to determine the horizontal fusional reserves (the ability for the eyes to converge [positive reserve] or diverge [negative reserve]). A base‐in (negative) prism was presented first, followed by a base‐out (positive) prism. Participants were asked to keep a vertical line single as long as possible and to report when the line became double (fusion break), while prism demand increased. If the patient reported double, prism demand decreased until the patient reported the line was single again (fusion recovery). Vergence facility was tested using a 12BO/3BI prism flipper, which was interchangeably placed (BI first, then BO) in front of the child's eyes as they focused on the vertical column and reported when the target was single for a period of 1 min. The outcome measure was the number of times children reported that the vertical column was clear and single, which was recorded in cycles per minute.

Accommodative function was assessed using two tests. First, accommodative amplitude was measured by the push‐up method. Using a 0.6‐M target held beyond the child's near point of accommodation as calculated using the Duane–Hoffstetter minimum amplitude equation (15.0 − 0.25 × age), the examiner moved the target slowly toward the child until they reported that the target began to blur. The distance between the target and the spectacle plane is measured and recorded as the amplitude of accommodation. Accommodative facility was assessed binocularly using the 20/50 Bernell Accommodative Rock Card held at 0.4m, and ±2.00D lenses were interchangeably placed in front of the child as they read the letters on the target card for a period of 1 min. The outcome measure was the number of times children reported that the image was clear and single, which was recorded in cycles per minute.

Visuomotor coordination was tested using a bead‐threading task. Children sat in a height‐adjusted chair with their chin placed in a chinrest. Each trial began with a fixation target presented on a monitor 80 cm in front of the chinrest. The index finger and the thumb of the dominant hand were placed on the vertical needle (height: 12 cm; diameter: 0.3 cm) placed 15 cm in front of the chinrest. Participants were asked to look at the tip of the needle and begin the task as soon as the auditory sound was presented. Children were instructed to reach toward the bead (diameter: 1.0 cm; hole diameter: 0.5 cm), which was placed 20 cm away from the needle (i.e., the starting position), and grasp it using a precision grip, then transport it and place it on the needle. Thus, the first reaching movement (i.e., reach‐to‐grasp) was away from the body, and the transport movement was toward the body. As soon as the bead was placed on the needle, children were asked to move their hand away from the needle, marking the end of the trial. The instructions emphasized performing the task as fast as possible without dropping the bead. Children completed five to eight practice trials prior to data recording, which included 30 experimental trials.

3. Results

Table 1 provides a detailed description of the vision test results for the two sessions. There were no significant differences between the sessions for visual acuity, stereoacuity, phoria, horizontal fusional reserves, or accommodative facility. Vergence facility improved significantly, with an average increase of 3D, while the amplitude of accommodation significantly decreased by an average of 2D in the older group of children (Session 2).

TABLE 1.

Vision test results (mean, SD) for the two sessions and the corresponding results from a paired t‐test (Wilcoxon signed‐rank test was used when normality assumption was violated).

Vision test	Session 1	Session 2	Statistic (df = 16), p‐value
Binocular Visual Acuity at 6m (logMAR)	−0.02, SD 0.09	−0.03, SD 0.09	z = 0.031, p = 1.000
Binocular Visual Acuity at 0.4m (logMAR)	0.02, SD 0.05	0.01, SD 0.03	z = 0.267, p = 1.000
Stereoacuity (arc sec)	24, SD 9	27, SD 16	z = −0.178, p = 0.905
Phoria (diopter [D])	3.65, SD 5.13	4.53, SD 8.67	z = −0.222, p = 0.858
Positive Fusional Vergence break (D)	22, SD 9	22, SD 11	t = 0.059, p = 0.954
Negative Fusional Vergence break (D)	14, SD 3	12, SD 3	t = 1.649, p = 0.119
Vergence Facility (cycles per minute [cpm]	12, SD 3	15, SD 6	t = −2.263, p = 0.038
Accommodative Facility (cpm)	7.4, SD 3.2	7.7, SD 3.2	t = −0.261, p = 0.797
Amplitude of Accommodation (D)	11.7, SD 2.5	9.8, SD 2.1	t = 2.342, p = 0.032

Note: The values in bold text highlight the statistically significant results

Differences in the kinematic measures between sessions were investigated using a one‐tailed paired t‐test to assess the hypothesis that performance would improve in the second session as a function of age. The limb kinematic analyses included 17 children; however, eye tracking data were not recorded for one participant due to difficulties with calibration during the first test session. Therefore, any comparisons that include eye movements are based on the results from 16 children only. The statistical results reported include the exact p‐values and Cohen's d to indicate the effect size. We did not apply corrections for multiple comparisons because our analyses were hypothesis‐driven rather than exploratory. Adjusting for multiple tests in this context would increase the risk of Type II errors, potentially obscuring meaningful effects that were specified a priori.

Age distribution of children who participated in each session is presented in Figure 2A. Figure 2B shows that the time to complete the bead‐threading task was significantly shorter in the second session compared to Session 1 (mean difference: 271 ms, SD 286 ms; t ₁₆ = 3.90, p = 0.001, Cohen's d = 0.947), indicating a global improvement in motor performance.

FIGURE 2.

Differences in children's age (A) and global performance measure indicated by the total movement time to complete the task (B) as a function of testing session. Individual data points represent the mean measure for each child tested in Session 1 (green) and Session 2 (red). A box plot represents the interquartile range (IQR), the horizontal line is the median, and the whiskers extend to values 1.5 times the IQR.

Results for eye and hand reaction time, which indicate the duration of the planning interval, are shown in Figure 3. Eye reaction time was marginally significant between the sessions (Figure 3A; mean difference: 75 ms, SD 156 ms; t ₁₅ = 1.77, p = 0.049, Cohen's d = 0.441). Hand reaction time was significantly shorter in Session 2 compared with Session 1 (Figure 3B; mean difference: 82 ms, SD 134 ms; t ₁₆ = 2.52, p = 0.011, Cohen's d = 0.612).

FIGURE 3.

Reaction time for the eye movement (A) and hand movement (B) as a function of testing session. Individual data points represent the mean measure for each child tested in Session 1 (green) and Session 2 (red). A box plot represents the interquartile range (IQR), the horizontal line is the median, and the whiskers extend to values 1.5 times the IQR.

Next, to gain a deeper understanding of the maturation of eye–hand coordination and the control of upper limb movements, the sequencing task was decomposed into several components. The kinematic results describing the first part of the sequence (i.e., movement to the bead and grasping) are shown in Figure 4A–E, whereas Figure 4F–J illustrates the kinematic measures for the latter part of the sequence (reach to needle and threading). There was no significant difference between sessions for eye–hand latency when reaching to the bead (Figure 4A; mean difference: 10 ms, SD 61 ms; t ₁₅ = 0.67, p = 0.257, Cohen's d = 0.167). As expected, reach peak velocity was significantly higher in Session 2 (mean difference: 0.115 m/s, SD 0.164 m/s; t ₁₇ = −2.866, p = 0.005, Cohen's d = 0.700). Reach duration when moving toward the bead was significantly shorter in Session 2 (mean difference: 33 ms, SD 43 ms; t ₁₆ = 3.124, p = 0.003, Cohen's d = 0.758). Examining the acceleration and deceleration interval duration revealed no difference for the duration of acceleration (Figure 4B; mean difference: 5 ms, SD 33 ms; t ₁₆ = 0.599, p = 0.279, Cohen's d = 0.145). In contrast, the deceleration interval was significantly shorter in Session 2 (Figure 4C; mean difference: 28 ms, SD 27 ms; t ₁₆ = 4.278, p < 0.001, Cohen's d = 1.038). Analysis of eye fixations demonstrated shorter fixation on the bead in Session 2 (Figure 4D; mean difference: 130 ms, SD 100 ms; t ₁₅ = 5.224, p < 0.001, Cohen's d = 1.306). The grasping interval duration was not significantly different between the sessions (Figure 4E; mean difference: 28 ms, SD 27 ms; t ₁₆ = 1.275, p = 0.110, Cohen's d = 0.309).

FIGURE 4.

Eye–hand coordination and limb kinematics plotted as a function of session. (A) Reach‐to‐bead eye–hand latency difference. (B) Reach‐to‐bead acceleration duration. (C) Reach‐to‐bead deceleration duration. (D) Bead fixation duration. (E) Grasping duration. (F) Reach‐to‐needle eye–hand latency difference. (G) Reach‐to‐needle acceleration duration. (H) Reach‐to‐needle deceleration duration. (I) Needle fixation duration. (J) Threading duration. Individual data points represent the mean measure for each child tested in Session 1 (green) and Session 2 (red). A box plot represents the interquartile range (IQR), the horizontal line is the median, and the whiskers extend to values 1.5 times the IQR.

Analysis of the second part of the sequence revealed a significant eye–hand latency difference when reaching for the needle (Figure 4F, mean difference: 75 ms, SD 57 ms; t ₁₅ = −5.287, p < 0.0001, Cohen's d = 1.322). As shown in the figure, this interval became more positive, indicating a shorter delay between the initiation of eye and hand movements, with the eyes more likely to precede the hand movement toward the needle. Reach peak velocity was significantly higher in Session 2 (mean difference: 0.081 m/s, SD 0.176 m/s; t ₁₇ = −1.912, p = 0.037, Cohen's d = 0.464). Reaching toward the needle was significantly shorter in the second session (mean difference: 25 ms, SD 39 ms; t ₁₆ = 2.598, p = 0.0310, Cohen's d = 0.630). The reduced reach duration was due to a shorter acceleration interval (Figure 4G; mean difference: 27 ms, SD 23 ms; t ₁₆ = 4.707, p < 0.001, Cohen's d = 1.142), whereas the deceleration interval duration was similar across both sessions (Figure 4H; mean difference: 2 ms, SD 24 ms; t ₁₆ = 0.350, p = 0.635, Cohen's d = 0.085). Finally, fixation on the needle and the threading duration were significantly shorter during Session 2 (Figure 4I; mean difference: 120 ms, SD 259 ms; t ₁₅ = 1.847, p = 0.042, Cohen's d = 0.462; Figure 4J: mean difference: 188 ms, SD 220 ms; t ₁₆ = 3.521, p < 0.001, Cohen's d = 0.854).

A multiple regression was conducted to determine whether the results from vision testing explain any significant variance in motor performance. Total movement time to complete the task in each session was used in this analysis because it reflects overall motor performance. Results from the regression analysis were not significant for Session 1 (F _8, 16 = 0.617, p = 0.745) or for Session 2 (F _8, 16 = 0.744, p = 0.669), indicating that the components of visual function assessed in this study were not associated with the performance of the bead‐threading task.

4. Discussion

This study used a kinematic approach to investigate the maturation of eye–hand coordination in a cohort of typically developing children tested first in middle childhood and then during adolescence. Our results offer new insights into visuomotor development by providing detailed information about limb kinematics and eye movements, highlighting the changes in sensorimotor control as children gain proficiency in motor performance. Moreover, the study assessed various visual functions, confirming that this cohort of children had normal binocular vision, which provided sufficient sensory input for the typical maturation of fine motor skills.

To our knowledge, the current study is the first longitudinal, kinematic investigation of prehension and eye–hand coordination in typically developing children aged 7–16 years. Our results extend the work of Bäckström et al., who also examined upper limb kinematics during a sequencing prehension task in children who were tested at three time points between the ages of 7 and 11 years (Bäckström et al. 2025). Notably, the children who participated in the second session in our study were older than 11 years old; thus, our results offer further insight into the developmental trajectory as children transition into adolescence. Results from both Bäckström et al. and this study clearly demonstrated improvements in motor planning ability and motor performance in typically developing children. Motor planning was evaluated by the response reaction time, where lower latency indicates a more efficient ability to transform the sensory inputs into a motor plan. Examining the results from both studies reveals greater improvements in latency across the three time points used in the study by Bäckström et al. While the current study shows significant improvement in the latency of both eye and hand movements when children were tested during adolescence, Cohen's effect size indicates a small to medium effect. Motor planning is task dependent; therefore, the smaller effect seen in the current study may be due to a relatively easy and repetitive task, as children performed the same sequence over many trials. Using tasks that vary the extrinsic or intrinsic task parameters might provide better insight into the maturation of motor planning abilities. Nonetheless, the task used in the current study highlights that planning abilities continue to develop throughout adolescence, consistent with age‐related changes in neural activity during motor planning (Ward et al. 2025).

Results from this study demonstrate a significant improvement in task execution, as shown by a reduction in the total time to perform the sequencing task. Although the task used in the current study and the one used by Bäckström might seem quite different (Bäckström et al. 2025), they were both designed to assess prehension embedded in a sequencing task, which requires higher‐order planning to pick up, grasp, and manipulate an object to accomplish the behavioral goal. Using such a task combined with a kinematic approach allows detailed characterization of the task components and the underlying control processes. Results from the current study suggest that the bead‐threading task provides a sensitive assay of the maturation trajectory of visuohaptic integration during prehension, which relies on efficient coordination between the reach and grasp movements. Examining the duration of the acceleration and deceleration intervals of both reaching movements (i.e., reach‐to‐grasp and reach‐to‐needle) revealed an interesting maturation pattern. The older children had a shorter reach‐to‐grasp interval, which was attributed to a shorter deceleration interval that preceded the grasp, whereas the shortened reach‐to‐needle interval was due to a shorter acceleration interval that followed grasping. This pattern of results suggests that improved reaching efficiency could be attributed to a more mature coordination between the reaching, grasping, and transporting movements. Our findings add to the understanding of visuohaptic integration in children, which has been previously investigated using a size and orientation discrimination task (Gori et al. 2008). Results from the study by Gori et al. revealed that multisensory integration becomes optimal and adult‐like after the age of 8 years. Our findings demonstrate that prehension efficiency depends on the coordination between reaching and grasping, which continues to improve into adolescence. It is possible that the optimal integration of visual and haptic information that seems to occur around the age of 8 years is a prerequisite for the subsequent maturation of reach and grasp coordination.

This is the first study to reveal a distinct maturational trend in eye–hand coordination. Improvement in task performance was associated with shorter fixations on the bead in the older group of children and with a change in the pattern of eye–hand coordination when reaching for the needle. The younger children had a tendency to initiate the hand movement prior to moving their eyes (i.e., hand minus eye latency was negative). This pattern was reversed at the second testing session, where the hand movement was initiated after the eye movement, which reflects an adult‐like coordination pattern (Niechwiej‐Szwedo, Wu, et al. 2021). The pattern found in the younger group of children, with the eye movement lagging the hand, might reflect less mature coordination between grasp execution and the second reaching movement to the needle. It is possible that fixation on the bead during grasping provides visual feedback that must be integrated with the haptic input as the reaching movement is initiated. This process might be less efficient in younger children (Domellöf and Säfström 2020).

Unexpectedly, the duration of grasping was not significantly different between the sessions. In light of our previous cross‐sectional study, which found an average difference of 62 ms between the younger and older groups of children, it was expected that grasping duration would be shorter in the older children. The mean difference in grasp duration observed in the current longitudinal study was only 27 ms, which shows that the children tested in the second session did not reach the same performance level as participants who were of a similar age in the earlier cross‐sectional study. It is possible that grasping efficiency might have a longer maturation trajectory outside of the age range tested, which remains to be examined in future studies.

Threading the bead on the needle is the most difficult part of our sequencing task, which takes approximately 30% of the total task duration. Consistent with the cross‐sectional study results (Niechwiej‐Szwedo, Wu, et al. 2021), children assessed in Session 2 had a similar improvement in threading duration, indicating a significant gain in motor proficiency. Similarly, Bäckström et al. found a large improvement over the age range tested in that study in the execution of object placement and the fitting of the object into a target (Bäckström et al. 2025). Altogether, results from the two longitudinal studies offer consistent results and complementary insight into the maturation of visuomotor control in school‐aged children.

An important strength of our study is that children received a visual examination, including testing of binocular functions. Vision provides important sensory input for movement planning and execution. The findings in this study demonstrate that the visual function is relatively stable over the age period examined. We did find a significant improvement in vergence facility in the older group, which suggests more efficient oculomotor control. On the other hand, there was a slight reduction in the amplitude of accommodation, which is consistent with a body of research showing a decrease with age. Our previous study with a larger cohort of children revealed a distinct contribution of stereopsis to grasp execution, while vergence was associated with reach efficiency (Niechwiej‐Szwedo, Thai, et al. 2020). Given these findings, it was hypothesized that children with higher visual function would perform the bead‐threading task more efficiently. However, current results did not support this hypothesis. All children had normal visual acuity, stereoacuity, vergence, and accommodative function (Scheiman and Wick 2008), and these measures did not explain any significant variance in motor task performance. Our cohort was relatively small, and the results from the acuity and stereoacuity tests were within a very narrow range, limiting any potential insights. It would be important to further investigate the association between visual function and motor proficiency in a larger, more heterogeneous cohort.

While our findings provide valuable insights, there are certain limitations that must be considered when interpreting the results from a longitudinal study. First, our study included a relatively small number of participants, which might limit the statistical power to detect significant differences in the multiple regression analysis. Future studies should include a larger cohort of children to validate the current findings. Second, children tested at the initial session were younger, and their performance might have been influenced by poorer attention span and fatigue, which could have contributed to the lower motor performance. Assessment of vergence and accommodative facility can be challenging in younger children because of a lack of understanding of test instructions. On the other hand, children tested at the second session might have performed better because they were familiar with both the vision tests and the motor task. Because the same tests were used to assess children in both sessions, it is possible that some of the improvement that was evident in the second session was due to the familiarity with the task rather than neurological maturation. However, the task is short in duration (∼15 min) and does not involve extensive practice; thus, it is unlikely that children developed a procedural memory. Moreover, the two sessions were separated by approximately 4 years, which makes it less likely that the improvement in performance was due to familiarity or procedural learning.

In conclusion, this longitudinal study reveals notable developmental shifts in sensorimotor control as children transition into adolescence. Combined eye and hand tracking demonstrates improvement in motor proficiency, which can be attributed to motor planning and execution. Notably, this is the first study supporting that kinematic metrics provide insight into visuohaptic coordination and the corresponding maturation from middle childhood to adolescence. As technology advances, kinematic assessments may become available in clinical or educational settings. However, implementing these novel approaches outside of a research laboratory will require significant work to evaluate their potential usefulness in tracking maturation and identifying deviations from the typical developmental trajectory. This study is a step toward that application.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.