What Aimed Movement Models Fit Distal Pointing With Varying Depth?

Area of Science:

• Human-Computer Interaction
• Robotics
• Biomechanics

Background:

• Existing models for predicting movement time in distal pointing tasks often rely on statistical approaches.
• These models typically consider target distance but may not fully capture the complexities of human motor control.

Purpose of the Study:

• To investigate the influence of depth on distal pointing performance.
• To evaluate and compare different mathematical models for predicting movement time in distal pointing tasks, particularly when the hand does not traverse the full distance.

Main Methods:

• A full-factorial Fitts' paradigm experiment was conducted with 24 participants.
• Independent variables included movement amplitude, target width, and distance from the participant to the screen.
• Movement time and error rates were recorded as dependent variables.

Main Results:

• Depth significantly affected movement time for small target widths, but not for large target widths.
• The angular two-part model demonstrated superior predictive accuracy compared to the one-part Fitts' model at greater distances.
• A modified Index of Difficulty ($$) with an adjustable parameter (k) provided the best fit but raised concerns about model complexity and overfitting.

Conclusions:

• The angular two-part model offers a robust and interpretable framework for describing distal pointing.
• While the $$ model with adjustable k predicts movement time effectively when depth is a factor, its lack of physical interpretability limits its practical application.