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In the fisheye image viewing and the corrected 2D viewing conditions, only the performances of subject 1 and subject 3 become more accurate with training. Means upper panel and standard errors lower panel are plotted as a function of the rank number of the experimental training session. This participant was run in twelve additional training sessions, producing a total of 20 sessions instead of eight. As would be expected on the basis of previous observations [ 2 , 7 , 16 ], our results confirm that 2D video-image viewing negatively affects both time and precision of task execution compared with direct action viewing control.
This performance loss is statistically significant. Although the disadvantage of image-guidance may diminish with training and eventually level off, none of the individuals gets to perform as well as in the direct viewing condition in the last training sessions. In fact, the effects of the viewing conditions vary significantly between individuals as a function of the training session, as shown by the two-by-two interactions between these factors.
Low-level explanations in terms of vision-proprioception conflict during task execution in the indirect viewing conditions would be a possible candidate.
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This feeling of control, sometimes also referred to as agency , influences both the timing and the accuracy of hand movements [ 1 ]. Moreover, badly matched visual and proprioceptive inputs may reduce tactile sensitivity significantly [ 14 ]. We do, however, not think that this explanation is a likely candidate here. Moreover, previous work has shown that the direction of arm movements vertical vs horizontal , not monitor position, matters critically in image-guided performance. Tasks requiring arm movements mostly in the vertical direction as in our experimental task here were performed faster and with more precision than tasks requiring essentially movements in the horizontal direction, regardless of where the monitor for viewing the video images was placed [ 10 ].
Secondly, the video images received from the camera in our experiment were professionally calibrated for both time and space.
Spatial matching of the image conditions with the direct viewing condition was controlled by making sure the size of real-world action field parameters such as target, object, and tool sizes, were identical when viewed from the participants sitting position. Temporal matching was controlled by the algorithm driving the internal clock of the CPU, ensuring that the video-images where synchronized with the real-world actions, as specified in Materials and Methods. There was no perceptible mismatch or misalignment in either time or space between actions represented in the video-images and actions viewed directly.
In motor learning, both low-level and high-level processes contribute to the evolution of performance with training e. High-level action intentions, which are closely linked to psychological factors such as response strategy preferences, were deliberately not controlled or selectively manipulated no performance feed-back of any sort was given in our experiment.
These typically occur spontaneously during training, are independent of low-levels task constraints, and reflect individual goal setting strategies predicted decades ago by results from seminal work in the field e. Wearing a glove does not significantly affect speed of execution, but does affect precision. This observation was not expected in the light of previous data see [ 6 ] , but is explained by a reduction of tactile sensitivity to physical objects when no direct finger contact with the object is possible, which may be detrimental to feed-back signaling from hand to cortex for eye-hand coordination.
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This interpretation relates to earlier findings showing that the direct manipulation of objects by hand is combined with the visual and tactile integration of physical object parameters for action planning, gestural programming, and motor control [ 18 , 19 , 8 , 22 ]. This possibly involves cortical neurons with non-classic receptive field structures in the brain [ 56 , 50 , 52 ]. It can be assumed that under conditions of touch with direct contact between the physical object and the fingers of the hands, the finely tuned mechanoreceptors under the skin which control both fingertip forces and grasp kinematics [ 27 ] send stronger feed-back signals to these cortical neurons [ 31 ].
Tool-mediated object positioning was as precise as by-hand direct object positioning, but task execution was slower, as expected in the light of previous observations on novices e. Tool-specific motor requirements e. The effect of tool use on execution times is present throughout all the training sessions as shown in the conditional performance curves of the four individuals here. The most important results in the light of our study goal are the significant inter-individual differences in performance strategies during training found here in this image-guided pick-and-place task.
These differences are reflected by strategy specific trade-offs between speed of task execution and the precision with which the object is placed on the targets. As predicted, these trade-offs occur spontaneously and without performance feed-back e. The observations lead to understand why monitoring only execution times for learning curve analysis in simulator training is not a viable option. Some trainees may get faster, but not necessarily better in the task, as shown here.
Neglecting the functional relationship between the time and the precision of task execution highlighted by the results from this study here is likely to have a cost. Individuals start the training sessions with different goals on their minds. Some place their effort on performing the object positioning task as fast as possible while others place their effort on being as precise as possible. The conditional performance curves reveal that the choice to privilege one strategy goal either speed or precision at the beginning has measurable consequences on the individual performance evolution at further stages of training.
One trainee, who privileges precision at the outset subject 1 , becomes even more precise with further training, and also gets faster. Two other trainees subjects 2 and 3 start fast, and re-adjust their execution times in mid-training, possibly because they realize that they may not perform with enough precision. One of them subject 3 manages, indeed, to become more precise by adjusting his speed strategy to a slightly slower temporal performance level.
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One trainee, the fastest performer here subject 4 , starts fast and gets faster steadily with training in all conditions, yet, his precision never stabilizes. Even with twelve additional sessions, there was no measurable improvement in the precision score of this trainee.
These procedures either aim at selectively rewarding either speed or precision during learning e. This can be achieved by providing adequate feed-back to the trainee, especially in the first training sessions. Making sure that the trainee gets as precise as possible before getting faster should be a priority in surgical simulator training. Execution times then become faster automatically with training.
Once a desired level of precision is reached by a trainee, time deadlines for task execution can be introduced, and progressively reduced during further training, to ensure the trainee will get as fast as possible without losing precision e.
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A major goal identified in recent analyses [ 17 ] is to ensure that the experimental evaluation of skills in surgical simulator scenarios is not subject to the development of a single observer bias over time, as may easily be the case in fully automated unsupervised skill rating procedures. Yet, these represent economy in manpower and are therefore likely to become the adopted standard, which will result in trainees not being coached individually and receiving no proper guidance on how to optimize their learning strategies.
A closed loop model for adaptive skill assessment as shown here needs to be considered to ensure that evaluation of desired behaviours is appropriate and not subject to the development of a single observer bias over time. Adjustment of learning criteria and test design step 3 of the loop may be necessary in the light of data relative to temporal and spatial aspects of tested performance step 2 of the loop. The results from this study reveal complex and spontaneously occurring trade-offs between time and precision in the performance of four individuals, all absolute beginners, in visual spatial learning of an image-guided object positioning task.
These trade-offs reflect cognitive strategy variations that need to be monitored individually to ensure effective skill learning.
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Collecting only time data to establish learning curves is not an option, as getting faster does not straightforwardly imply getting better at the task. Training procedures should include skill evaluation by expert psychologists and procedures for the adaptive control of speed-accuracy trade-offs in the performances of novices.
Proprioception contributes to the sense of agency during visual observation of hand movements: evidence from temporal judgments of action. J Cogn Neurosci. Effects of indirect screen vision and tool-use on the time and precision of object positioning on real-world targets. Bogner MS. Human error in medicine. Bonnet C. Psychophysical approaches, contextual effects, and response bias.
Cognitive Biases. Amsterdam: Elsevier; Bonnet C, Dresp B. A fast procedure for studying conditional accuracy functions.