[Paper Review] Beyond Expertise: Stable Individual Differences in Predictive Eye-Hand Coordination

Emiko Shishido|arXiv (Cornell University)|Feb 8, 2026

Motor Control and Adaptation0 citations

TL;DR

The study shows that people rely on stable, individual Predictive Protocols for eye–hand coordination during line tracing; predictive timing varies widely between individuals, is tied to each person’s pen catch up time, and is not enhanced by expertise or linked to tracing accuracy.

ABSTRACT

Human eye-hand coordination relies on internal forward models that predict future states and compensate for sensory delays. During line tracing, the gaze typically leads the hand through predictive saccades, yet the extent to which this predictive window reflects expertise or intrinsic individual traits remains unclear. In this study, I examined eye-hand coordination in professional calligraphers and non-experts performing a controlled line tracing task. The temporal coupling between saccade distance (SD) and pen speed (PS) revealed substantial interpersonal variability: SD-PS peak times ranged from approximately -50 to 400 ms, forming stable, participant-specific predictive windows that were consistent across trials. These predictive windows closely matched each individual's pen catch-up time, indicating that the oculomotor system stabilizes fixation in anticipation of the hand's future velocity rather than relying on reactive pursuit. Neither the spatial indices (mean gaze-pen distance, mean saccade distance) nor the temporal index (SD-PS peak time) differed between calligraphers and non-calligraphers, and none of these predictive parameters correlated with tracing accuracy. These findings suggest that diverse predictive strategies can achieve equivalent performance, consistent with the minimum intervention principle of optimal feedback control. Together, the results indicate that predictive timing in eye-hand coordination reflects a stable, idiosyncratic Predictive Protocol shaped by individual neuromotor constraints rather than by expertise or training history.

Motivation & Objective

• Characterize predictive timing in eye–hand coordination during line tracing and assess whether it reflects intrinsic individual traits or training history.
• Quantify the SD–PS (saccade distance–pen speed) relationship and its predictive window across trials.
• Determine whether predictive timing variables (SD–PS peak time, pen catch up time) relate to tracing accuracy.
• Compare predictive timing metrics between professional calligraphers and non-experts.
• Evaluate stability of predictive protocols across speed conditions and over trials.

Proposed method

• Employ a controlled line tracing task with 17 participants (7 professional calligraphers, 10 non-experts).
• Record eye movements with a Tobii T60 XL at 60 Hz and pen movements with a capacitive touch panel synchronized to the eye tracker.
• Compute saccade distance (SD) and pen speed (PS); determine SD–PS peak time as the predictive window.
• Define pen catch up time as the interval from saccade onset to when the pen reaches the gaze’s previous position.
• Derive mean gaze–pen distance (mGP) and mean saccade distance (mSD); assess correlations between SD and PS using robust regression.
• Assess tracing accuracy as mean absolute deviation from the centerline across trials.

Experimental results

Research questions

• RQ1 Does the SD–PS predictive window differ between professional calligraphers and non-experts?
• RQ2 Are predictive windows and related timing parameters stable across trials and speed conditions?
• RQ3 Is tracing accuracy related to predictive timing metrics (SD–PS peak time, pen catch up time, mGP, mSD)?
• RQ4 Do diverse Predictive Protocols converge to a single strategy under identical task constraints?

Key findings

• SD–PS peak times varied widely across individuals, from about -50 ms to 400 ms.
• The SD–PS peak time closely matched each participant’s pen catch up time.
• No significant differences in predictive timing metrics between calligraphers and non-calligraphers.
• Tracing accuracy did not correlate with mGP, mSD, or SD–PS peak time.
• Predictive Protocols appear to be stable, idiosyncratic characteristics rather than training-induced strategies.
• 82% of participants exhibited SD–PS peak times greater than 0 ms, indicating prediction of future hand speed.