热力学人机交互
Thermodynamic Human-Computer Interaction
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中文总结 AI 辅助
该研究提出一种基于热力学的人机交互统一框架，可跨交互模态扩展、无需训练数据且评估时间为O(1)，在网页预取任务中取得1.37的预取点击比和98.1%的目标预测准确率。
中文摘要 AI 辅助
传统人机交互模型依赖领域特定技术对目标预测进行建模；专为光标交互预测设计的模型无法泛化到移动界面，反之亦然。我们引入一种基于热力学的统一框架，提出人类交互由热力学平衡和非平衡阶段构成。为验证这一点，我们通过为移动智能体和目标分配动能与势能，从平衡热力学中推导出菲茨定律（Fitts' law）及所提出的目标预测模型。随后，我们分析了该预测模型与菲茨定律在边缘案例中的缺陷，例如对大目标的意图预测。该分析表明，大目标无法用平衡热力学进行准确建模。所提模型可跨交互模态扩展且无需修改，仅需零训练数据，评估时间为恒定的O(1)。此外，我们表明按钮颜色等设计属性是影响对智能体施加吸引力的独立参数。将该框架应用于实时网页预取任务时，其实现了1.37的高效预取点击比（Fetch:Click ratio），并以98.1%的准确率预测了用户的目标。
英文摘要
Target acquisition is often modeled separately for desktop, mobile, and other interaction modalities. We present Thermodynamic HCI, a framework that splits interaction into thermal equilibrium and non-equilibrium regimes. The theory generalizes across interaction modalities by representing agent-target interaction using kinetic and potential energies. We derive the movement time of Fitts' law and the speed-accuracy tradeoff observed in Schmidt's law from the principles of thermal physics. Furthermore, we develop theorems that describe how target properties, such as the color of a button, affect user accuracy. The target acquisition model, derived from the theory, when evaluated on desktop and mobile website prefetching experiments, achieved an accuracy of 98% for both cursor and touchscreen based interaction. For every clicked link, it produced a fetch:click ratio of 1.37 for desktop and 1.75 for mobile.
发表机构
- Heriot-Watt University（赫瑞-瓦特大学）
机构由 AI 辅助整理，请以论文原文为准。
