BIGnav: Bayesian Information Gain for Guiding Multiscale Navigation

CHI, pp. 5869-5880, 2017.

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WORK BIGnav is a new multiscale navigation technique based on Bayesian Experimental Design with the criterion of maximizing the information-theoretic concept of mutual information

Abstract:

This paper introduces BIGnav, a new multiscale navigation technique based on Bayesian Experimental Design where the criterion is to maximize the information-theoretic concept of mutual information, also known as information gain. Rather than simply executing user navigation commands, BIGnav interprets user input to update its knowledge ab...More

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Introduction
  • Multiscale interfaces are a powerful way to represent large datasets such as maps and deep hierarchies.
  • Other techniques interpret users’ intentions to guide navigation: SDAZ [14], for example, adjusts the zoom level according to the user-controlled velocity
  • While these approaches have proven effective, the authors believe the authors can do better by combining them into a more general framework.
  • Consider a scientist who wants to determine some parameter θ of nature
  • He can choose to perform an experiment x that will provide an observation y.
  • The utility may involve factors such as the financial cost of performing the experiment
Highlights
  • Multiscale interfaces are a powerful way to represent large datasets such as maps and deep hierarchies
  • EXPERIMENT Our goal is to study the performance of BIGnav in what Javed et al call micro-level navigation, when the user has decided on a destination and needs to navigate to it [16]
  • We verified that misses and outliers were randomly distributed across participants, techniques and conditions
  • Task Completion Time Table 2 shows the results of a repeated-measures full factorial ANOVA on task completion time
  • WORK BIGnav is a new multiscale navigation technique based on Bayesian Experimental Design with the criterion of maximizing the information-theoretic concept of mutual information
  • Our main result is that BIGnav is up to 40.0% faster than the baseline for distant targets and non-uniform information spaces
Methods
  • Sixteen participants (3 female), age 24 to 30, were recruited from the institution and received a handful of candies for their participation.
  • A standard mouse was used with the same sensitivity for all participants
Results
  • The authors first removed 23 missed trials and 54 outliers in which TCT was 3 standard deviations larger than the mean.
  • A post-hoc Tukey HSD test reveals a robust interaction effect: BIGnav is significantly faster than STDnav when ID > 15 (p < 0.0001), significantly slower when ID = 10 (p < 0.0001) and not significantly different for ID = 15 (p = 0.99).
  • The eight participants who preferred STDnav found it “more comfortable, doesn’t require that much attention”, “more intuitive as the author can anticipate what the author would see ” and “The author is already used to it”
  • These results indicate that BIGnav can be a practical technique for efficient navigation
Conclusion
  • The authors have shown that BIGnav is an effective technique, especially for distant targets and non-uniform information spaces.
  • Comfort in Navigation In standard pan-and-zoom interfaces, users can navigate the space in a continuous manner and constantly anticipate the system response
  • This gives them a sense of control and makes for a smooth user experience.
  • BIGnav uses discrete steps and the system’s response can be difficult to anticipate and even frustrating, in particular when getting close to the target
  • This results in long idle times between commands (Fig. 8) and a higher cognitive load as users reorient themselves and decide on their move.
  • The authors want to improve the computational cost of the technique in order to support more input commands and a finer grid
Summary
  • Introduction:

    Multiscale interfaces are a powerful way to represent large datasets such as maps and deep hierarchies.
  • Other techniques interpret users’ intentions to guide navigation: SDAZ [14], for example, adjusts the zoom level according to the user-controlled velocity
  • While these approaches have proven effective, the authors believe the authors can do better by combining them into a more general framework.
  • Consider a scientist who wants to determine some parameter θ of nature
  • He can choose to perform an experiment x that will provide an observation y.
  • The utility may involve factors such as the financial cost of performing the experiment
  • Methods:

    Sixteen participants (3 female), age 24 to 30, were recruited from the institution and received a handful of candies for their participation.
  • A standard mouse was used with the same sensitivity for all participants
  • Results:

    The authors first removed 23 missed trials and 54 outliers in which TCT was 3 standard deviations larger than the mean.
  • A post-hoc Tukey HSD test reveals a robust interaction effect: BIGnav is significantly faster than STDnav when ID > 15 (p < 0.0001), significantly slower when ID = 10 (p < 0.0001) and not significantly different for ID = 15 (p = 0.99).
  • The eight participants who preferred STDnav found it “more comfortable, doesn’t require that much attention”, “more intuitive as the author can anticipate what the author would see ” and “The author is already used to it”
  • These results indicate that BIGnav can be a practical technique for efficient navigation
  • Conclusion:

    The authors have shown that BIGnav is an effective technique, especially for distant targets and non-uniform information spaces.
  • Comfort in Navigation In standard pan-and-zoom interfaces, users can navigate the space in a continuous manner and constantly anticipate the system response
  • This gives them a sense of control and makes for a smooth user experience.
  • BIGnav uses discrete steps and the system’s response can be difficult to anticipate and even frustrating, in particular when getting close to the target
  • This results in long idle times between commands (Fig. 8) and a higher cognitive load as users reorient themselves and decide on their move.
  • The authors want to improve the computational cost of the technique in order to support more input commands and a finer grid
Tables
  • Table1: Calibration results used as prior knowledge about the user behavior P(Y = y Θ = θ , X = x)
  • Table2: Full-factorial ANOVA on TCT
  • Table3: Full-factorial ANOVA on the number of commands
Download tables as Excel
Related work
  • BIGnav uses a human-computer partnership to guide navigation. We therefore discuss related work in assisted multiscale navigation and human-computer collaboration and adaptation.

    Assisted Multiscale Navigation Pan and zoom are the canonical navigation commands in multiscale interfaces. Panning lets users change the position of the view while zooming lets them modify the magnification of the viewport [8, 22]. Navigation tasks consist in acquiring or pointing a specific target, characterized by a position and size. It generally involves view navigation, whereby the user must first bring the target into view, at a scale where it can be selected. Despite being quite different from traditional pointing, Guiard & Beaudouin-Lafon [10] have shown that Fitts’ law [6] applies to multiscale pointing. Therefore we adopt Fitts’ paradigm and use the index of difficulty (ID) in our controlled experiment to assess pointing performance.
Funding
  • This research was partially funded by Labex DigiCosme (ANR-11-LABEX-0045DIGICOSME), operated by the French Agence Nationale de la Recherche (ANR) as part of the program “Investissement d’Avenir” Idex Paris-Saclay (ANR-11-IDEX-0003-02), by European Research Council (ERC) grant n° 695464 ONE: Unified Principles of Interaction, and by CNPq (Brazilian National Research Council) grant n° 201545/2015-2
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