TY - GEN
T1 - Training strategies for learning a 3D trajectory with accuracy
AU - Rodríguez, Jorge
AU - Gutiérrez, Teresa
AU - Casado, Sara
AU - Sánchez, Emilio J.
PY - 2010
Y1 - 2010
N2 - The goal of this study was to evaluate different learning conditions for motor skill transfer. The study was divided into two experiments with the same task: learning a 3D trajectory with accuracy. The first experiment was focused on evaluating the efficiency of three feedback schemes for the target trajectory: visual, haptic and visual-haptic feedback. The second experiment was focused on analyzing the influence of decreasing the feedback during the training process. The results suggest that the best learning condition for learning a 3D trajectory with accuracy is to provide visual-haptic feedback, which facilitates the understanding of the dimension and orientation of each trajectory segment and solves any visual discrepancies that may exist. Furthermore, although continuous feedback can create dependences in users and impede the transfer of motor skills, feedback based on user request can also be dangerous since users can create a wrong mental representation that keep them from replicating the trajectory accurately. Therefore, when the performance of a task depends on references created during the training process, it seems appropriate for the system to provide automatic feedback based on user performance.
AB - The goal of this study was to evaluate different learning conditions for motor skill transfer. The study was divided into two experiments with the same task: learning a 3D trajectory with accuracy. The first experiment was focused on evaluating the efficiency of three feedback schemes for the target trajectory: visual, haptic and visual-haptic feedback. The second experiment was focused on analyzing the influence of decreasing the feedback during the training process. The results suggest that the best learning condition for learning a 3D trajectory with accuracy is to provide visual-haptic feedback, which facilitates the understanding of the dimension and orientation of each trajectory segment and solves any visual discrepancies that may exist. Furthermore, although continuous feedback can create dependences in users and impede the transfer of motor skills, feedback based on user request can also be dangerous since users can create a wrong mental representation that keep them from replicating the trajectory accurately. Therefore, when the performance of a task depends on references created during the training process, it seems appropriate for the system to provide automatic feedback based on user performance.
KW - Haptic feedback
KW - Motor skill transfer
KW - Virtual teaching
KW - Virtual training
UR - http://www.scopus.com/inward/record.url?scp=78650566820&partnerID=8YFLogxK
U2 - 10.1109/HAVE.2010.5623979
DO - 10.1109/HAVE.2010.5623979
M3 - Conference contribution
AN - SCOPUS:78650566820
SN - 9781424465088
T3 - HAVE 2010 - 2010 IEEE International Symposium on Haptic Audio-Visual Environments and Games, Proceedings
SP - 73
EP - 78
BT - HAVE 2010 - 2010 IEEE International Symposium on Haptic Audio-Visual Environments and Games, Proceedings
T2 - 2010 9th IEEE International Symposium on Haptic Audio-Visual Environments and Games, HAVE 2010
Y2 - 16 October 2010 through 17 October 2010
ER -
