Revisiting Performance Models of Distal Pointing Tasks in Virtual Reality

Performance models of interaction, such as Fitts' law, are important tools for predicting and explaining human motor performance and for designing high-performance user interfaces. Extensive prior work has proposed such models for the 3D interaction task of distal pointing, in which the user po...

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Published in:	IEEE transactions on visualization and computer graphics Vol. 31; no. 10; pp. 8283 - 8296
Main Authors:	Lane, Logan, Lu, Feiyu, Davari, Shakiba, Teather, Robert J., Bowman, Doug A.
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.10.2025
Subjects:	Computational modeling Data models Hands human-centered computing–human computer interaction (HCI)–interaction paradigms–virtual reality Human-centered computing–human computer interaction (HCI)–Interaction techniques–pointing Performance evaluation Predictive models Size measurement Solid modeling Three-dimensional displays User interfaces
ISSN:	1077-2626, 1941-0506, 1941-0506
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Abstract	Performance models of interaction, such as Fitts' law, are important tools for predicting and explaining human motor performance and for designing high-performance user interfaces. Extensive prior work has proposed such models for the 3D interaction task of distal pointing, in which the user points their hand or a device at a distant target in order to select it. However, there is no consensus on how to compute the index of difficulty for distal pointing tasks. We present a preliminary study suggesting that existing models may not be sufficient to model distal pointing performance with current virtual reality technologies. Based on these results, we hypothesized that both the form of the model and the standard method for collecting empirical data for pointing tasks might need to change in order to achieve a more accurate and valid distal pointing model. In our main study, we used a new methodology to collect distal pointing data and evaluated traditional models, purely ballistic models, and two-part models. Ultimately, we found that the best model used a simple Fitts'-law-style index of difficulty with angular measures of amplitude and width.
AbstractList	Performance models of interaction, such as Fitts' law, are important tools for predicting and explaining human motor performance and for designing high-performance user interfaces. Extensive prior work has proposed such models for the 3D interaction task of distal pointing, in which the user points their hand or a device at a distant target in order to select it. However, there is no consensus on how to compute the index of difficulty for distal pointing tasks. We present a preliminary study suggesting that existing models may not be sufficient to model distal pointing performance with current virtual reality technologies. Based on these results, we hypothesized that both the form of the model and the standard method for collecting empirical data for pointing tasks might need to change in order to achieve a more accurate and valid distal pointing model. In our main study, we used a new methodology to collect distal pointing data and evaluated traditional models, purely ballistic models, and two-part models. Ultimately, we found that the best model used a simple Fitts'-law-style index of difficulty with angular measures of amplitude and width. Performance models of interaction, such as Fitts' law, are important tools for predicting and explaining human motor performance and for designing high-performance user interfaces. Extensive prior work has proposed such models for the 3D interaction task of distal pointing, in which the user points their hand or a device at a distant target in order to select it. However, there is no consensus on how to compute the index of difficulty for distal pointing tasks. We present a preliminary study suggesting that existing models may not be sufficient to model distal pointing performance with current virtual reality technologies. Based on these results, we hypothesized that both the form of the model and the standard method for collecting empirical data for pointing tasks might need to change in order to achieve a more accurate and valid distal pointing model. In our main study, we used a new methodology to collect distal pointing data and evaluated traditional models, purely ballistic models, and two-part models. Ultimately, we found that the best model used a simple Fitts'-law-style index of difficulty with angular measures of amplitude and width.Performance models of interaction, such as Fitts' law, are important tools for predicting and explaining human motor performance and for designing high-performance user interfaces. Extensive prior work has proposed such models for the 3D interaction task of distal pointing, in which the user points their hand or a device at a distant target in order to select it. However, there is no consensus on how to compute the index of difficulty for distal pointing tasks. We present a preliminary study suggesting that existing models may not be sufficient to model distal pointing performance with current virtual reality technologies. Based on these results, we hypothesized that both the form of the model and the standard method for collecting empirical data for pointing tasks might need to change in order to achieve a more accurate and valid distal pointing model. In our main study, we used a new methodology to collect distal pointing data and evaluated traditional models, purely ballistic models, and two-part models. Ultimately, we found that the best model used a simple Fitts'-law-style index of difficulty with angular measures of amplitude and width.
Author	Teather, Robert J. Lu, Feiyu Davari, Shakiba Bowman, Doug A. Lane, Logan
Author_xml	– sequence: 1 givenname: Logan orcidid: 0009-0009-4385-160X surname: Lane fullname: Lane, Logan email: logantl@vt.edu organization: Virginia Tech, Blacksburg, VA, USA – sequence: 2 givenname: Feiyu orcidid: 0000-0002-1939-9352 surname: Lu fullname: Lu, Feiyu email: feiyulu@vt.edu organization: Virginia Tech, Blacksburg, VA, USA – sequence: 3 givenname: Shakiba orcidid: 0000-0003-3128-1979 surname: Davari fullname: Davari, Shakiba email: sdavari@vt.edu organization: Virginia Tech, Blacksburg, VA, USA – sequence: 4 givenname: Robert J. orcidid: 0009-0007-4572-1820 surname: Teather fullname: Teather, Robert J. email: Rob.Teather@carleton.ca organization: Carleton University, Ottawa, ON, Canada – sequence: 5 givenname: Doug A. orcidid: 0000-0003-0491-5067 surname: Bowman fullname: Bowman, Doug A. email: dbowman@vt.edu organization: Virginia Tech, Blacksburg, VA, USA
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SubjectTerms	Computational modeling Data models Hands human-centered computing–human computer interaction (HCI)–interaction paradigms–virtual reality Human-centered computing–human computer interaction (HCI)–Interaction techniques–pointing Performance evaluation Predictive models Size measurement Solid modeling Three-dimensional displays User interfaces
Title	Revisiting Performance Models of Distal Pointing Tasks in Virtual Reality
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