Enhancing human user performance in some complex task is an important research question in many domains from skilled manufacturing to rehabilitation and surgical training. Many examples in the literature explore the effects of both haptic assistance or guidance to complete a task, as well as haptic hindrance to temporarily increase task difficulty for the ultimate goal of faster learning. Studies also suggest adaptively changing guidance based on expertise may be most effective. However, to our knowledge, there has not yet been a conclusive study evaluating these enhancement modes in a systematic experiment. In this article, we evaluate learning outcomes for 24 human subjects in a randomized control trial performing a Fitt's law reaching task under various haptic feedback conditions including: no haptics, assistive haptics, resistive haptics, and adaptively changing haptics tied to current performance measures. Subjects each performed 400 trials total and this paper reports results for 40 pre-test and 40 post-test trials. While most conditions did show improvements in performance, we found statistically significant results indicating that our adaptive haptic feedback condition leads to faster and more effective learning as evidenced by metrics of movement time, overshoot, performance index, and speed when compared to the other groups.

中文翻译：

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通过在菲茨定律任务中自适应地改变触觉反馈线索来提高用户性能

提高人类用户在某些复杂任务中的表现是从熟练制造到康复和外科训练等许多领域的一个重要研究问题。文献中的许多例子都探讨了触觉辅助或指导对完成任务的影响，以及触觉障碍对暂时增加任务难度以实现更快学习的最终目标的影响。研究还表明，根据专业知识适应性地改变指导可能是最有效的。然而，据我们所知，目前还没有一项结论性的研究在系统实验中评估这些增强模式。在本文中，我们评估了 24 名人类受试者在一项随机对照试验中的学习成果，这些试验在各种触觉反馈条件下执行费特定律达成任务，包括：无触觉、辅助触觉、电阻式触觉以及与当前表现测量相关的自适应改变触觉。每个受试者总共进行了 400 次试验，本文报告了 40 次预测试和 40 次测试后试验的结果。虽然大多数条件确实显示了性能的提高，但我们发现统计显着的结果表明，与其他组相比，我们的自适应触觉反馈条件可以带来更快、更有效的学习，这一点可以通过运动时间、超调、性能指数和速度指标来证明。