摘要：Accumulating evidence indicates that the spatial error of human's hand localization appears subject-specific. However, whether the idiosyncratic pattern persists across time with good within-subject consistency has not been adequately examined. Here we measured the hand localization map by a Visual-matching task in multiple sessions over 2 days. Interestingly, we found that participants improved their hand localization accuracy when tested repetitively without performance feedback. Importantly, despite the reduction of average error, the spatial pattern of hand localization errors remained idiosyncratic. Based on individuals' hand localization performance, a standard convolutional neural network classifier could identify participants with good accuracy. Moreover, we did not find supporting evidence that participants' baseline hand localization performance could predict their motor performance in a visual Trajectory-matching task even though both tasks require accurate mapping of hand position to visual targets in the same workspace. Using a separate experiment, we not only replicated these findings but also ruled out the possibility that performance feedback during a few familiarization trials caused the observed improvement in hand localization. We conclude that the conventional hand localization test itself, even without feedback, can improve hand localization but leave the idiosyncrasy of hand localization map unchanged.
其他摘要：Abstract Accumulating evidence indicates that the spatial error of human's hand localization appears subject-specific. However, whether the idiosyncratic pattern persists across time with good within-subject consistency has not been adequately examined. Here we measured the hand localization map by a Visual-matching task in multiple sessions over 2 days. Interestingly, we found that participants improved their hand localization accuracy when tested repetitively without performance feedback. Importantly, despite the reduction of average error, the spatial pattern of hand localization errors remained idiosyncratic. Based on individuals' hand localization performance, a standard convolutional neural network classifier could identify participants with good accuracy. Moreover, we did not find supporting evidence that participants' baseline hand localization performance could predict their motor performance in a visual Trajectory-matching task even though both tasks require accurate mapping of hand position to visual targets in the same workspace. Using a separate experiment, we not only replicated these findings but also ruled out the possibility that performance feedback during a few familiarization trials caused the observed improvement in hand localization. We conclude that the conventional hand localization test itself, even without feedback, can improve hand localization but leave the idiosyncrasy of hand localization map unchanged.
