Getting nowhere fast: trade-off between speed and precision in training to execute image-guided hand-tool movements

TL;DR

This study examines how training affects speed and precision in image-guided hand-tool tasks, revealing a trade-off where initial speed compromises eventual accuracy, with implications for training protocols.

Contribution

It provides new insights into the effects of training on speed and precision in image-guided tasks, highlighting the importance of initial pacing for skill development.

Findings

Image-guidance results in slower, less precise movements compared to direct view.

Training improves speed across conditions, but only some improve in precision.

Slower initial performers achieve the highest precision and learning gains.

Abstract

Background: The speed and precision with which objects are moved by hand or hand-tool interaction under image guidance depend on a specific type of visual and spatial sensorimotor learning. Novices have to learn to optimally control what their hands are doing in a real-world environment while looking at an image representation of the scene on a video monitor. Previous research has shown slower task execution times and lower performance scores under image-guidance compared with situations of direct action viewing. The cognitive processes for overcoming this drawback by training are not yet understood. Methods: We investigated the effects of training on the time and precision of direct view versus image guided object positioning on targets of a Real-world Action Field (RAF). Two men and two women had to learn to perform the task as swiftly and as precisely as possible with their dominant hand, using a tool or not and wearing a glove or not. Individuals were trained in sessions of mixed trial blocks with no feed-back. Results: As predicted, image-guidance produced significantly slower times and lesser precision in all trainees and sessionscompared with direct viewing. With training, all trainees get faster in all conditions, but only one of them gets reliably more precise in the image-guided conditions. Speed-accuracy trade-offs in the individual performance data show that the highest precision scores and steepest learning curve, for time and precision, were produced by the slowest starter.Conclusions: Performance evolution towards optimal precision is compromised when novices start by going as fast as they can. The findings have direct implications for individual skill monitoring in training programmes for image-guided technology applications with human operators.