Tuning Endpoint-variability Parameters by Observed Error Rates to Obtain Better Prediction Accuracy of Pointing Misses

Document Title

Tuning Endpoint-variability Parameters by Observed Error Rates to Obtain Better Prediction Accuracy of Pointing Misses

Document Information

• Subject Area: Human-Computer Interaction (HCI), Performance Modeling, Error Rate Prediction
• Keywords: Human motor performance, error rate prediction, endpoint distribution, data analysis, hyperparameter optimization, UI design, nonlinear regression, static target selection, human-computer interface

Research Background and Problem

• Key Issues or Challenges:

  • Error rate models for static target pointing tasks are typically conducted in two steps: predicting the variability of pointing endpoints (standard deviation 𝜎) and calculating the probability of pointing outside the target. This method indirectly focuses on accurate prediction of 𝜎 while neglecting direct prediction of error rates (ER).
  • Traditional methods lack precision in error rate prediction, especially under new target conditions.

• Importance of the Problem:

  • Accurate error rate models can assist interface designers in optimizing button size and layout, thereby enhancing user experience.
  • The theory can be applied to derive the upper limit of input speed and optimize UI layouts, such as those based on Fitts' Law and ant colony algorithms.

• Research Motivation and Related Work:

  • Existing studies use nonlinear regression to directly optimize 𝜎 prediction for error rate accuracy but have not conducted detailed comparisons with traditional SD methods.
  • The academic community focuses on understanding human motor behavior, but higher precision prediction models are particularly critical for application design.

Solution

• Proposed Solution:

  • The "Error Rate Optimization Method" (ER-based method), which directly aims to optimize the accuracy of error rate prediction by adjusting endpoint variability parameters.
  • Utilizing hyperparameter optimization tools (HPO) from the machine learning domain to address issues with improper initialization of parameters in traditional nonlinear regression.

• Innovations:

  • Proposing a new method for directly optimizing error rate prediction and conducting an in-depth comparison of the advantages and disadvantages of the traditional "Standard Deviation Method" (SD-based method) versus the "Error Rate Optimization Method."
  • Demonstrating that HPO enables error rate models with superior predictive performance across various experimental conditions.

• Implementation Steps and Key Techniques:

  1. Regression of the 𝜎 model in the data, fitting parameters such as target size using existing formulas.
  2. Using HPO to automatically adjust regression initialization values and identify optimized parameters.
  3. Reanalyzing 8 experimental datasets to compare the predictive performance of different methods (including results from all datasets and cross-validation).
  4. Evaluating prediction accuracy using different metrics: R², Mean Absolute Error (MAE), and Root Mean Square Error (RMSE).

Research Outcomes

• Specific Results:

  • In evaluating 8 experimental datasets, the ER-based method consistently outperformed the SD-based method, especially after optimizing parameters with HPO, significantly improving prediction accuracy.
  • Regardless of target shape, input device, or experimental environment, the ER-based method demonstrated excellent and stable performance.
  • Experiments showed that even if the traditional SD method accurately predicts 𝜎, direct optimization of error rates can still significantly enhance prediction accuracy.

• Comparative Advantages Over Existing Solutions:

  • The ER-based method avoids the potential error accumulation issues of the two independent steps in the SD method.
  • HPO ensures that the parameter search range in the nonlinear regression process is reasonably constrained, avoiding local optimum problems.

• Experimental or Evaluation Results:

  • The ER-based method with HPO demonstrated higher R², lower MAE, and RMSE across all 8 experimental datasets.
  • LOOCV (Leave-one-out Cross-Validation) indicated that the ER-based method provides greater predictive stability under new target conditions.

• Limitations and Future Directions:

  • Current analyses are limited to static target selection, and applicability to dynamic targets, virtual reality, and other complex scenarios requires further validation.
  • The ER-based method has lower parameter interpretability; while suitable for UI design, it may not fully support studies on human motor behavior.
  • Hyperparameter optimization methods may incur time costs; future work could focus on improving search efficiency through methods such as sparse recovery or interactive adjustment of search processes.