Accuracy of hand localization is subject-specific and improved without performance feedback.

Area of Science:

• Neuroscience
• Human motor control
• Spatial cognition

Background:

• Spatial error in human hand localization is recognized as subject-specific.
• The temporal consistency of these individual hand localization patterns requires further investigation.

Purpose of the Study:

• To examine the within-subject consistency of idiosyncratic hand localization patterns over time.
• To investigate whether repetitive testing influences hand localization accuracy and spatial error patterns.
• To determine if baseline hand localization performance predicts motor performance in related tasks.

Main Methods:

• Hand localization maps were measured using a Visual-matching task across multiple sessions over two days.
• A convolutional neural network classifier was employed to identify participants based on their hand localization performance.
• A separate visual Trajectory-matching task was used to assess motor performance prediction.

Main Results:

• Participants showed improved hand localization accuracy with repetitive testing, even without performance feedback.
• Despite accuracy improvements, the spatial pattern of hand localization errors remained idiosyncratic across individuals.
• A convolutional neural network could accurately identify participants based on their unique hand localization maps.
• Baseline hand localization performance did not predict motor performance in the visual Trajectory-matching task.

Conclusions:

• The conventional hand localization test can enhance localization accuracy through repetition, independent of performance feedback.
• Idiosyncratic hand localization maps demonstrate high within-subject consistency over time.
• Individual hand localization patterns are stable and sufficiently distinct for subject identification using machine learning.