Evaluating Visual Variables in a Virtual Reality Environment

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Evaluating Visual Variables in a Virtual Reality Environment

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Author(s): Somnath Arjun , G S Rajshekar Reddy , Abhishek Mukhopadhyay , Sanjana Vinod , Pradipta Biswas

Publication date (Print): July 2021

Conference name: 34th British HCI Conference (HCI2021)

Conference theme: Post-pandemic HCI – Living Digitally

Conference date: 20th - 21st July 2021

Keywords: Evaluation, Visual variables, Visualization, Virtual Reality, Eye Tracking, Cognitive load

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Abstract

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Author and article information

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Somnath Arjun

G S Rajshekar Reddy

Abhishek Mukhopadhyay

Sanjana Vinod

Pradipta Biswas

Conference

Publication date: July 2021

Publication date (Print): July 2021

Pages: 11-22

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[0001]I3D Lab, Indian Institute of Science

Bangalore 560012, India

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DOI: 10.14236/ewic/HCI2021.1

SO-VID: d3ae8066-aed2-4f56-a06f-91ef5379eb18

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This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Conference name: 34th British HCI Conference

Conference acronym: HCI2021

Conference number: 34

Conference location: London, UK

Conference date: 20th - 21st July 2021

Conference sponsor: Electronic Workshops in Computing (eWiC)

Conference theme: Post-pandemic HCI – Living Digitally

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1477-9358 BCS Learning & Development

Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/HCI2021.1

Self URI (journal page): https://ewic.bcs.org/

References

T. IsenbergP. IsenbergJ. ChenM. Sedlmair, & T. Möller (2013). A systematic review on the practice of evaluating visualization. IEEE Transactions on Visualization and Computer Graphics, 19(12), 2818-2827.
B. SteichenM. M. WuD. TokerC. Conati, & G. Carenini (2014, July). Te, Te, Hi, Hi: Eye gaze sequence analysis for informing user-adaptive information visualizations. In International Conference on User Modeling, Adaptation, and Personalization (pp. 183-194). Springer, Cham.
S. Arjun (2018, July). Personalizing data visualization and interaction. In Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization (pp. 199-202).
Y. J. HuangT. FujiwaraY. X. LinW. C. Lin, & K. L. Ma (2017, April). A gesture system for graph visualization in virtual reality environments. In 2017 ieee pacific visualization symposium (pacificvis) (pp. 41-45). IEEE.
N. CapeceU. Erra, & J. Grippa (2018, July). Graphvr: A virtual reality tool for the exploration of graphs with htc vive system. In 2018 22nd international conference information visualisation (iv) (pp. 448-453). IEEE.
U. ErraD. Malandrino, & L. Pepe (2019). Virtual reality interfaces for interacting with three-dimensional graphs. International Journal of Human–Computer Interaction, 35(1), 75-88.
P. A. Sullivan (2016). Graph-based data visualization in virtual reality: a comparison of user experiences.
C. Ware, & G. Franck (1994, October). Viewing a graph in a virtual reality display is three times as good as a 2D diagram. In Proceedings of 1994 IEEE Symposium on Visual Languages (pp. 182-183). IEEE.
M. O. WardG. Grinstein, & D. Keim (2010). Interactive data visualization: foundations, techniques, and applications. CRC Press.
K. Andrews (2006, May). Evaluating information visualisations. In Proceedings of the 2006 AVI workshop on BEyond time and errors: novel evaluation methods for information visualization (pp. 1-5).
S. Carpendale (2008). Evaluating information visualizations. In Information visualization (pp. 19-45). Springer, Berlin, Heidelberg.
C. Chen, & M. P. Czerwinski (2000). Empirical evaluation of information visualizations: an introduction. International journal of human-computer studies, 53(5), 631-635.
M. A. LivingstonJ. W. Decker, & Z. Ai (2012). Evaluation of multivariate visualization on a multivariate task. IEEE transactions on visualization and computer graphics, 18(12), 2114-2121.
R. AmarJ. EaganJ. Stasko: Low-Level Components of Analytic Activity in Information Visualization. In: Proc. of 2005 Symp. on Information Visualization, pp. 15–21(2005)
C. ConatiH. Maclaren: Exploring the role of individual differences in information visualization. In: Proc. of the Working Conf. on Advanced Visual Interfaces, pp. 199–206 (2008)
M.C. VelezD. SilverM. Tremaine: Understanding visualization through spatial ability differences. In: IEEE Visualization, VIS 2005, pp. 511–518 (2005)
D. TokerC. ConatiG. CareniniM. Haraty: Towards adaptive information visualization: On the influence of user characteristics. In: J. MasthoffB. MobasherM.C. DesmaraisR. Nkambou (eds.) UMAP 2012. LNCS, vol. 7379, pp. 274–285. Springer, Heidelberg (2012)
G. PrabhakarA. MukhopadhyayL. MurthyM. ModikshaD. Sachin, & P. Biswas (2020). Cognitive load estimation using ocular parameters in automotive. Transportation Engineering, 2, 100008.
O. PalinkoA. L. KunA. Shyrokov, & P. Heeman (2010, March). Estimating cognitive load using remote eye tracking in a driving simulator. In Proceedings of the 2010 symposium on eye-tracking research & applications (pp. 141-144).
S. P. Marshall (2002, September). The index of cognitive activity: Measuring cognitive workload. In Proceedings of the IEEE 7th conference on Human Factors and Power Plants (pp. 7-7). IEEE.
R. GavasD. Chatterjee, & A. Sinha (2017, October). Estimation of cognitive load based on the pupil size dilation. In 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 1499-1504). IEEE.
A. T. DuchowskiK. KrejtzI. KrejtzC. BieleA. NiedzielskaP. Kiefer, ... & I. Giannopoulos (2018, April). The index of pupillary activity: Measuring cognitive load vis-à-vis task difficulty with pupil oscillation. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1-13).
P. Biswas, & G. Prabhakar (2018). Detecting drivers’ cognitive load from saccadic intrusion. Transportation research part F: traffic psychology and behaviour, 54, 63-78.
J. GoldbergJ. Helfman: Eye tracking for visualization evaluation: reading values on linear versus radial graphs. Inf. Vis. 10, 182–195 (2011)
S.T. IqbalB.P. Bailey: Using eye gaze patterns to identify user tasks. Presented at the The Grace Hopper Celebration of Women in Computing (2004)
D. TokerC. ConatiB. SteichenG. Carenini: Individual user characteristics and information visualization: connecting the dots through eye tracking. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 295–304 (2013)
A. Olsen, & R. Matos (2012, March). Identifying parameter values for an I-VT fixation filter suitable for handling data sampled with various sampling frequencies. In proceedings of the symposium on Eye tracking research and applications (pp. 317-320)
B. Farnsworth (2021). 10 Most Used Eye Tracking Metrics and Terms - iMotions. Retrieved 24 April 2021, from https://imotions.com/blog/10-terms-metrics-eye-tracking/#revisits
D. D. Salvucci, & J. H. Goldberg (2000, November). Identifying fixations and saccades in eye-tracking protocols. In Proceedings of the 2000 symposium on Eye tracking research & applications (pp. 71-78).
C. ZiemkiewiczR. J. CrouserA. R. YauillaS. L. SuW. Ribarsky, & R. Chang (2011, October). How locus of control influences compatibility with visualization style. In 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) (pp. 81-90). IEEE.
T. M. Green, & B. Fisher (2010, October). Towards the personal equation of interaction: The impact of personality factors on visual analytics interface interaction. In 2010 IEEE Symposium on Visual Analytics Science and Technology (pp. 203-210). IEEE.
E. M. M. PeckB. F. YukselA. OttleyR. J. Jacob, & R. Chang (2013, April). Using fNIRS brain sensing to evaluate information visualization interfaces. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 473-482).
C. Ware, & P. Mitchell (2008). Visualizing graphs in three dimensions. ACM Transactions on Applied Perception (TAP), 5(1), 1-15.
S. H. Fisher IIIJ. V. Dempsey, & R. T. Marousky (1997). Data visualization: Preference and use of two-dimensional and three-dimensional graphs. Social Science Computer Review, 15(3), 256-263.
S. Garlandini, & S. I. Fabrikant (2009, September). Evaluating the effectiveness and efficiency of visual variables for geographic information visualization. In International Conference on Spatial Information Theory (pp. 195-211). Springer, Berlin, Heidelberg.
R. E. Roth (2017, March 6). Visual Variables.https://onlinelibrary.wiley.com/doi/abs/10.1002/9781118786352.wbieg0761
The professional-grade VR headset | VIVE Pro United States. Vive.com. (2021). Retrieved 7 May 2021, from https://www.vive.com/us/product/vive-pro/.
Insight Brainwear® 5 Channel Wireless EEG Headset | EMOTIV. EMOTIV. (2021). Retrieved 7 May 2021, from https://www.emotiv.com/insight/.
P Biswas and P Robinson, Evaluating the design of inclusive interfaces by simulation, Proceedings of the ACM International Conference on Intelligent User Interfaces (IUI) 2010
F. OnoratiR. BarbieriM. MauriV. Russo, & L. Mainardi (2013, July). Reconstruction and analysis of the pupil dilation signal: Application to a psychophysiological affective protocol. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 5-8). IEEE.
5 Types of Brain Waves Frequencies: Gamma, Beta, Alpha, Theta, Delta - Mental Health Daily. Mental Health Daily. (2021). Retrieved 10 May 2021, from https://mentalhealthdaily.com/2014/04/15/5-types-of-brain-waves-frequencies-gamma-beta-alpha-theta-delta/.
A. DrogemullerA. CunninghamJ. WalshM. CordeilW. Ross, & B. Thomas (2018, October). Evaluating navigation techniques for 3d graph visualizations in virtual reality. In 2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA) (pp. 1-10). IEEE.

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Comment on this article

[1] T. IsenbergP. IsenbergJ. ChenM. Sedlmair, & T. Möller (2013). A systematic review on the practice of evaluating visualization. IEEE Transactions on Visualization and Computer Graphics, 19(12), 2818-2827.

[2] B. SteichenM. M. WuD. TokerC. Conati, & G. Carenini (2014, July). Te, Te, Hi, Hi: Eye gaze sequence analysis for informing user-adaptive information visualizations. In International Conference on User Modeling, Adaptation, and Personalization (pp. 183-194). Springer, Cham.

[3] S. Arjun (2018, July). Personalizing data visualization and interaction. In Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization (pp. 199-202).

[4] Y. J. HuangT. FujiwaraY. X. LinW. C. Lin, & K. L. Ma (2017, April). A gesture system for graph visualization in virtual reality environments. In 2017 ieee pacific visualization symposium (pacificvis) (pp. 41-45). IEEE.

[5] N. CapeceU. Erra, & J. Grippa (2018, July). Graphvr: A virtual reality tool for the exploration of graphs with htc vive system. In 2018 22nd international conference information visualisation (iv) (pp. 448-453). IEEE.

[6] U. ErraD. Malandrino, & L. Pepe (2019). Virtual reality interfaces for interacting with three-dimensional graphs. International Journal of Human–Computer Interaction, 35(1), 75-88.

[7] P. A. Sullivan (2016). Graph-based data visualization in virtual reality: a comparison of user experiences.

[8] C. Ware, & G. Franck (1994, October). Viewing a graph in a virtual reality display is three times as good as a 2D diagram. In Proceedings of 1994 IEEE Symposium on Visual Languages (pp. 182-183). IEEE.

[9] M. O. WardG. Grinstein, & D. Keim (2010). Interactive data visualization: foundations, techniques, and applications. CRC Press.

[10] K. Andrews (2006, May). Evaluating information visualisations. In Proceedings of the 2006 AVI workshop on BEyond time and errors: novel evaluation methods for information visualization (pp. 1-5).

[11] S. Carpendale (2008). Evaluating information visualizations. In Information visualization (pp. 19-45). Springer, Berlin, Heidelberg.

[12] C. Chen, & M. P. Czerwinski (2000). Empirical evaluation of information visualizations: an introduction. International journal of human-computer studies, 53(5), 631-635.

[13] M. A. LivingstonJ. W. Decker, & Z. Ai (2012). Evaluation of multivariate visualization on a multivariate task. IEEE transactions on visualization and computer graphics, 18(12), 2114-2121.

[14] R. AmarJ. EaganJ. Stasko: Low-Level Components of Analytic Activity in Information Visualization. In: Proc. of 2005 Symp. on Information Visualization, pp. 15–21(2005)

[15] C. ConatiH. Maclaren: Exploring the role of individual differences in information visualization. In: Proc. of the Working Conf. on Advanced Visual Interfaces, pp. 199–206 (2008)

[16] M.C. VelezD. SilverM. Tremaine: Understanding visualization through spatial ability differences. In: IEEE Visualization, VIS 2005, pp. 511–518 (2005)

[17] D. TokerC. ConatiG. CareniniM. Haraty: Towards adaptive information visualization: On the influence of user characteristics. In: J. MasthoffB. MobasherM.C. DesmaraisR. Nkambou (eds.) UMAP 2012. LNCS, vol. 7379, pp. 274–285. Springer, Heidelberg (2012)

[18] G. PrabhakarA. MukhopadhyayL. MurthyM. ModikshaD. Sachin, & P. Biswas (2020). Cognitive load estimation using ocular parameters in automotive. Transportation Engineering, 2, 100008.

[19] O. PalinkoA. L. KunA. Shyrokov, & P. Heeman (2010, March). Estimating cognitive load using remote eye tracking in a driving simulator. In Proceedings of the 2010 symposium on eye-tracking research & applications (pp. 141-144).

[20] S. P. Marshall (2002, September). The index of cognitive activity: Measuring cognitive workload. In Proceedings of the IEEE 7th conference on Human Factors and Power Plants (pp. 7-7). IEEE.

[21] R. GavasD. Chatterjee, & A. Sinha (2017, October). Estimation of cognitive load based on the pupil size dilation. In 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 1499-1504). IEEE.

[22] A. T. DuchowskiK. KrejtzI. KrejtzC. BieleA. NiedzielskaP. Kiefer, ... & I. Giannopoulos (2018, April). The index of pupillary activity: Measuring cognitive load vis-à-vis task difficulty with pupil oscillation. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1-13).

[23] P. Biswas, & G. Prabhakar (2018). Detecting drivers’ cognitive load from saccadic intrusion. Transportation research part F: traffic psychology and behaviour, 54, 63-78.

[24] J. GoldbergJ. Helfman: Eye tracking for visualization evaluation: reading values on linear versus radial graphs. Inf. Vis. 10, 182–195 (2011)

[25] S.T. IqbalB.P. Bailey: Using eye gaze patterns to identify user tasks. Presented at the The Grace Hopper Celebration of Women in Computing (2004)

[26] D. TokerC. ConatiB. SteichenG. Carenini: Individual user characteristics and information visualization: connecting the dots through eye tracking. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 295–304 (2013)

[27] A. Olsen, & R. Matos (2012, March). Identifying parameter values for an I-VT fixation filter suitable for handling data sampled with various sampling frequencies. In proceedings of the symposium on Eye tracking research and applications (pp. 317-320)

[28] B. Farnsworth (2021). 10 Most Used Eye Tracking Metrics and Terms - iMotions. Retrieved 24 April 2021, from https://imotions.com/blog/10-terms-metrics-eye-tracking/#revisits

[29] D. D. Salvucci, & J. H. Goldberg (2000, November). Identifying fixations and saccades in eye-tracking protocols. In Proceedings of the 2000 symposium on Eye tracking research & applications (pp. 71-78).

[30] C. ZiemkiewiczR. J. CrouserA. R. YauillaS. L. SuW. Ribarsky, & R. Chang (2011, October). How locus of control influences compatibility with visualization style. In 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) (pp. 81-90). IEEE.

[31] T. M. Green, & B. Fisher (2010, October). Towards the personal equation of interaction: The impact of personality factors on visual analytics interface interaction. In 2010 IEEE Symposium on Visual Analytics Science and Technology (pp. 203-210). IEEE.

[32] E. M. M. PeckB. F. YukselA. OttleyR. J. Jacob, & R. Chang (2013, April). Using fNIRS brain sensing to evaluate information visualization interfaces. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 473-482).

[33] C. Ware, & P. Mitchell (2008). Visualizing graphs in three dimensions. ACM Transactions on Applied Perception (TAP), 5(1), 1-15.

[34] S. H. Fisher IIIJ. V. Dempsey, & R. T. Marousky (1997). Data visualization: Preference and use of two-dimensional and three-dimensional graphs. Social Science Computer Review, 15(3), 256-263.

[35] S. Garlandini, & S. I. Fabrikant (2009, September). Evaluating the effectiveness and efficiency of visual variables for geographic information visualization. In International Conference on Spatial Information Theory (pp. 195-211). Springer, Berlin, Heidelberg.

[36] R. E. Roth (2017, March 6). Visual Variables.https://onlinelibrary.wiley.com/doi/abs/10.1002/9781118786352.wbieg0761

[37] The professional-grade VR headset | VIVE Pro United States. Vive.com. (2021). Retrieved 7 May 2021, from https://www.vive.com/us/product/vive-pro/.

[38] Insight Brainwear® 5 Channel Wireless EEG Headset | EMOTIV. EMOTIV. (2021). Retrieved 7 May 2021, from https://www.emotiv.com/insight/.

[39] P Biswas and P Robinson, Evaluating the design of inclusive interfaces by simulation, Proceedings of the ACM International Conference on Intelligent User Interfaces (IUI) 2010

[40] F. OnoratiR. BarbieriM. MauriV. Russo, & L. Mainardi (2013, July). Reconstruction and analysis of the pupil dilation signal: Application to a psychophysiological affective protocol. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 5-8). IEEE.

[41] 5 Types of Brain Waves Frequencies: Gamma, Beta, Alpha, Theta, Delta - Mental Health Daily. Mental Health Daily. (2021). Retrieved 10 May 2021, from https://mentalhealthdaily.com/2014/04/15/5-types-of-brain-waves-frequencies-gamma-beta-alpha-theta-delta/.

[42] A. DrogemullerA. CunninghamJ. WalshM. CordeilW. Ross, & B. Thomas (2018, October). Evaluating navigation techniques for 3d graph visualizations in virtual reality. In 2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA) (pp. 1-10). IEEE.

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