pith. sign in

arxiv: 1907.04702 · v1 · pith:4QWZDRJHnew · submitted 2019-07-10 · 💻 cs.HC

Deadeye Visualization Revisited: Investigation of Preattentiveness and Applicability in Virtual Environments

Andrey Krekhov , Sebastian Cmentowski , Andre Waschk , Jens Kr\"uger This is my paper

Pith reviewed 2026-05-24 23:39 UTC · model grok-4.3

classification 💻 cs.HC
keywords Deadeyepreattentive highlightingvirtual realitydichoptic presentationvolume renderingstereoscopic visualizationattention guidance
0
0 comments X

The pith

Deadeye highlighting by rendering to one eye only stays preattentive in VR even with multiple heterogeneous distractors.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tests whether Deadeye, which highlights an object by showing it to only one eye through disparity suppression, keeps its preattentive quality when moved from 2D to 3D virtual environments. It runs quantitative detection tasks in VR scenes that include varied distractors and reports average accuracies above 90 percent across conditions. This matters because the method avoids altering any visual properties of the target itself, unlike color or size cues. The work then demonstrates the technique in VR volume rendering, provides an integration workflow, and gathers survey feedback on benefits and limits. If the results hold, Deadeye becomes usable in standard stereoscopic VR without extra hardware.

Core claim

Deadeye preserves preattentiveness under real-world conditions with multiple heterogeneous distractors, with all average accuracies above 90 percent. The technique applies to VR volume rendering via a proposed workflow, and an exploratory survey yields design implications for its use.

What carries the argument

Deadeye: dichoptic presentation achieved by suppressing binocular disparities for the target object alone so that it reaches only one eye.

If this is right

  • Deadeye works in VR without needing separate 2D dichoptic hardware.
  • High detection accuracy holds even when distractors differ in multiple visual attributes.
  • A concrete workflow exists for adding Deadeye to VR volume rendering pipelines.
  • Survey data supply initial design guidelines for when the technique succeeds or fails in practice.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The approach may transfer to other stereoscopic displays that already support separate eye views.
  • It could reduce interference between highlighting and depth perception in immersive 3D tasks.
  • Longer sessions or combined use with motion or lighting cues remain untested extensions.

Load-bearing premise

Dichoptic presentation through disparity suppression in VR headsets creates a clean highlighting effect without fusion problems or other perceptual artifacts that would change detection performance.

What would settle it

A VR detection experiment that yields average accuracy below 90 percent with heterogeneous distractors or where participants report binocular fusion failures would falsify preserved preattentiveness.

Figures

Figures reproduced from arXiv: 1907.04702 by Andre Waschk, Andrey Krekhov, Jens Kr\"uger, Sebastian Cmentowski.

Figure 1
Figure 1. Figure 1: Left: Our experiments in VR with homogeneous and heterogeneous distractors, as we investigate the preattentiveness and [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: A selection of VR visualizations that can benefit from Deadeye highlighting. (a) Educational visualizations of particle physics [14]: Deadeye [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: (a) Color as a cue: The red circle can be recognized preattentively independent of the number of blue distractors. (b) Da Vinci stereopsis: The [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Top: Exp-1 with an increasing number of homogeneous distractors on the same depth plane with a slight positional jittering applied to each cube. Bottom: Exp-2 with heterogeneous distractors, from left to right: depth2 (two depth planes), depth2-color-shape (two depth planes, different color and shape), and depth3-color (three depth planes, different color). A well-known case of dichoptic presentation is th… view at source ↗
Figure 5
Figure 5. Figure 5: Average accuracies for Exp-1 and Exp-2 compared to our previous results [39] for the 2D case. A repeated measures ANOVA shows no significant difference in accuracy between the sets, supporting our hypotheses that the transition to 3D scenes does not impact the performance of Deadeye and that our technique remains robust even in the presence of strong visual cues such as color or shape. they thought that a … view at source ↗
Figure 6
Figure 6. Figure 6: Excerpt from our VR implementation of the NASA-TLX survey. All [PITH_FULL_IMAGE:figures/full_fig_p005_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Results of the NASA-TLX survey for Exp-1 and Exp-2 compared to the prior values of the 2D case. Lower values are preferable. We applied a repeated measures ANOVA with the set type as a within-subject variable to investigate whether the number and kind of distractors influence the accuracy of Deadeye in VR. The result, F(5,115) = 1.04, p = .397, shows no significant difference. In other words, the performan… view at source ↗
Figure 8
Figure 8. Figure 8: Results of our custom questions. Larger numbers indicate a more [PITH_FULL_IMAGE:figures/full_fig_p006_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Average success rates for the detection of a Deadeye-enhanced [PITH_FULL_IMAGE:figures/full_fig_p007_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Participants went through four different visualization scenarios [PITH_FULL_IMAGE:figures/full_fig_p008_10.png] view at source ↗
read the original abstract

Visualizations rely on highlighting to attract and guide our attention. To make an object of interest stand out independently from a number of distractors, the underlying visual cue, e.g., color, has to be preattentive. In our prior work, we introduced Deadeye as an instantly recognizable highlighting technique that works by rendering the target object for one eye only. In contrast to prior approaches, Deadeye excels by not modifying any visual properties of the target. However, in the case of 2D visualizations, the method requires an additional setup to allow dichoptic presentation, which is a considerable drawback. As a follow-up to requests from the community, this paper explores Deadeye as a highlighting technique for 3D visualizations, because such stereoscopic scenarios support dichoptic presentation out of the box. Deadeye suppresses binocular disparities for the target object, so we cannot assume the applicability of our technique as a given fact. With this motivation, the paper presents quantitative evaluations of Deadeye in VR, including configurations with multiple heterogeneous distractors as an important robustness challenge. After confirming the preserved preattentiveness (all average accuracies above 90 %) under such real-world conditions, we explore VR volume rendering as an example application scenario for Deadeye. We depict a possible workflow for integrating our technique, conduct an exploratory survey to demonstrate benefits and limitations, and finally provide related design implications.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript investigates the Deadeye highlighting technique in virtual reality (VR) for 3D visualizations. Deadeye renders the target to one eye only to suppress binocular disparities. The central claim is that this preserves preattentiveness even with multiple heterogeneous distractors, as shown by quantitative evaluations with all average accuracies above 90%. The paper also presents a workflow for integrating Deadeye in VR volume rendering, an exploratory survey on benefits and limitations, and design implications.

Significance. If the empirical findings are confirmed, this work provides a valuable highlighting method for stereoscopic 3D environments that does not modify the visual properties of the target object, distinguishing it from color or other attribute-based cues. The testing under challenging conditions with heterogeneous distractors and the application example in volume rendering strengthen its practical relevance for visualization in VR. The follow-up to community requests for 3D applicability is a positive aspect.

major comments (2)
  1. [Abstract and Evaluation section] Abstract and Evaluation section: The quantitative evaluations report average accuracies above 90% but provide no details on the number of participants, the statistical tests used, specific stimulus parameters (e.g., disparity values, distractor counts), or exclusion criteria. These omissions are load-bearing because they prevent verification that the data support the claim of preserved preattentiveness under real-world conditions with heterogeneous distractors.
  2. [VR experiment description (likely §4)] VR experiment description (likely §4): The dichoptic presentation method lacks any mention of controls for potential perceptual artifacts such as binocular rivalry, incomplete fusion, or depth misperception. No fusion checks, rivalry reports from participants, or control conditions isolating the highlighting effect from the stereo setup are described. This is critical for the central claim, as such artifacts could independently influence search performance and accuracy metrics.
minor comments (2)
  1. [Abstract] The abstract is somewhat dense; breaking out the key results more clearly (e.g., exact accuracy values per condition) would improve readability.
  2. [Throughout] Ensure consistent use of terminology for 'dichoptic presentation' and 'disparity suppression' to avoid potential confusion for readers unfamiliar with the prior work.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed review. The comments highlight important aspects of methodological reporting that will strengthen the paper. We address each major comment below and will revise the manuscript to incorporate the requested details.

read point-by-point responses

  1. Referee: [Abstract and Evaluation section] Abstract and Evaluation section: The quantitative evaluations report average accuracies above 90% but provide no details on the number of participants, the statistical tests used, specific stimulus parameters (e.g., disparity values, distractor counts), or exclusion criteria. These omissions are load-bearing because they prevent verification that the data support the claim of preserved preattentiveness under real-world conditions with heterogeneous distractors.

    Authors: We agree that explicit reporting of these parameters is essential for verifiability. The current manuscript summarizes the key accuracy results but does not fully detail the experimental protocol in the Evaluation section. In the revised version we will expand this section to report the exact number of participants, the statistical tests performed (including any post-hoc analyses), specific stimulus parameters such as disparity values and distractor counts, and the exclusion criteria applied. This will directly address the concern and allow readers to assess support for the preattentiveness claim under heterogeneous conditions. revision: yes

  2. Referee: [VR experiment description (likely §4)] VR experiment description (likely §4): The dichoptic presentation method lacks any mention of controls for potential perceptual artifacts such as binocular rivalry, incomplete fusion, or depth misperception. No fusion checks, rivalry reports from participants, or control conditions isolating the highlighting effect from the stereo setup are described. This is critical for the central claim, as such artifacts could independently influence search performance and accuracy metrics.

    Authors: We acknowledge that the manuscript does not explicitly describe controls for binocular rivalry, fusion checks, or participant reports on depth misperception. While the VR setup inherently supports dichoptic presentation, additional safeguards should be documented. In the revision we will add a dedicated paragraph in the experiment description detailing any fusion verification procedures, rivalry monitoring, participant debriefing on perceptual artifacts, and control conditions used to isolate the Deadeye effect from general stereo viewing. This will strengthen the validity of the accuracy results. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical user study with external performance metrics

full rationale

The paper reports VR user studies measuring search accuracy (>90%) under heterogeneous distractors to assess preserved preattentiveness of Deadeye. No equations, derivations, fitted parameters, or predictions appear. Prior work is cited only for technique introduction; the new claims rest on fresh participant data, not self-referential reduction or ansatz smuggling. The dichoptic setup is an experimental condition, not a definitional loop. This is self-contained empirical work against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on standard domain assumptions from visual perception research rather than new free parameters, axioms, or invented entities introduced by the paper.

axioms (1)
  • domain assumption High accuracy in brief visual search tasks indicates preattentive processing.
    Invoked implicitly when interpreting accuracies above 90% as evidence of preserved preattentiveness.

pith-pipeline@v0.9.0 · 5789 in / 1125 out tokens · 22347 ms · 2026-05-24T23:39:21.965126+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

90 extracted references · 90 canonical work pages

  1. [1]

    M. J. Ackerman. The visible human project. Proceedings of the IEEE, 86(3):504–511, March 1998. doi: 10.1109/5.662875

    work page doi:10.1109/5.662875 1998

  2. [2]

    Alais and R

    D. Alais and R. Blake. Binocular rivalry. MIT press, 2005

    work page 2005

  3. [3]

    Alper, T

    B. Alper, T. Hollerer, J. Kuchera-Morin, and A. Forbes. Stereoscopic highlighting: 2d graph visualization on stereo displays. IEEE Transactions on Visualization and Computer Graphics, 17(12):2325–2333, Dec. 2011. doi: 10.1109/TVCG.2011.234

    work page doi:10.1109/tvcg.2011.234 2011

  4. [4]

    Anstis and A

    S. Anstis and A. Ho. Nonlinear combination of luminance excursions during flicker, simultaneous contrast, afterimages and binocular fusion. Vision Research, 38(4):523–539, 1998

    work page 1998

  5. [5]

    Assee and N

    A. Assee and N. Qian. Solving da vinci stereopsis with depth-edge- selective v2 cells. Vision Research, 47(20):2585 – 2602, 2007. doi: 10. 1016/j.visres.2007.07.003

    work page 2007

  6. [6]

    R. Blake. A neural theory of binocular rivalry. Psychological review, 96(1):145, 1989

    work page 1989

  7. [7]

    Borji and L

    A. Borji and L. Itti. State-of-the-art in visual attention modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(1):185– 207, Jan 2013. doi: 10.1109/TPAMI.2012.89

    work page doi:10.1109/tpami.2012.89 2013

  8. [8]

    Cao and S

    Y . Cao and S. Grossberg. A laminar cortical model of stereopsis and 3d surface perception: Closure and da vinci stereopsis. Spatial vision, 18(5):515–578, 2005

    work page 2005

  9. [9]

    Caziot and B

    B. Caziot and B. T. Backus. Stereoscopic offset makes objects easier to recognize. PLoS ONE, 10(6):e0129101, 2015. doi: 10.1371/journal.pone. 0129101

    work page doi:10.1371/journal.pone 2015

  10. [10]

    Cheng, M.-Y

    C.-Y . Cheng, M.-Y . Yen, H.-Y . Lin, W.-W. Hsia, and W.-M. Hsu. As- sociation of ocular dominance and anisometropic myopia. Investigative ophthalmology & visual science, 45(8):2856–2860, 2004

    work page 2004

  11. [11]

    F. Cole, D. DeCarlo, A. Finkelstein, K. Kin, K. Morley, and A. Santella. Directing gaze in 3d models with stylized focus. InProceedings of the 17th Eurographics Conference on Rendering Techniques, EGSR ’06, pp. 377–

    work page

  12. [12]

    Eurographics Association, Aire-la-Ville, Switzerland, Switzerland,

    work page

  13. [13]

    doi: 10.2312/EGWR/EGSR06/377-387

    work page doi:10.2312/egwr/egsr06/377-387

  14. [14]

    Corporation

    H. Corporation. HTC Vive Pro. Website, 2019. Retrieved March 12, 2019 from https://www.vive.com/

    work page 2019

  15. [15]

    C. M. De Weert and W. J. M. Levelt. Binocular brightness combinations: Additive and nonadditive aspects. Perception & Psychophysics, 15(3):551– 562, 1974

    work page 1974

  16. [16]

    Z. Duer, L. Piilonen, and G. Glasson. Belle2vr: A virtual-reality visu- alization of subatomic particle physics in the belle ii experiment. IEEE computer graphics and applications, 38(3):33–43, 2018

    work page 2018

  17. [17]

    J. T. Enns and R. A. Rensink. Influence of scene-based properties on visual search. Science, 247(4943):721, 1990

    work page 1990

  18. [18]

    J. T. Enns and R. A. Rensink. Sensitivity to three-dimensional orientation in visual search. Psychological Science, 1(5):323–326, 1990

    work page 1990

  19. [19]

    Oculus Go

    Facebook Technologies. Oculus Go. Website, 2019. Retrieved March 12, 2019 from https://www.oculus.com/go/

    work page 2019

  20. [20]

    M. A. Formankiewicz and J. Mollon. The psychophysics of detecting binocular discrepancies of luminance. Vision research, 49(15):1929–1938, 2009

    work page 1929

  21. [21]

    Friedenberg

    J. Friedenberg. Visual attention and consciousness. Psychology Press, 2012

    work page 2012

  22. [22]

    Gillam, M

    B. Gillam, M. Cook, and S. Blackburn. Monocular discs in the occlusion zones of binocular surfaces do not have quantitative deptha comparison with panum’s limiting case. Perception, 32(8):1009–1019, 2003

    work page 2003

  23. [23]

    Gillam and K

    B. Gillam and K. Nakayama. Quantitative depth for a phantom surface can be based on cyclopean occlusion cues alone. Vision Research, 39(1):109– 112, 1999

    work page 1999

  24. [24]

    Gutwin, A

    C. Gutwin, A. Cockburn, and A. Coveney. Peripheral popout: The in- fluence of visual angle and stimulus intensity on popout effects. In Pro- ceedings of the 2017 CHI Conference on Human Factors in Computing Systems, CHI ’17, pp. 208–219. ACM, New York, NY , USA, 2017. doi: 10.1145/3025453.3025984

    work page doi:10.1145/3025453.3025984 2017

  25. [25]

    K. Hall, C. Perin, P. Kusalik, and C. Gutwin. Formalizing emphasis in information visualization. In Computer Graphics Forum, vol. 35, pp. 717–737, 2016

    work page 2016

  26. [26]

    H ¨anel, B

    C. H ¨anel, B. Weyers, B. Hentschel, and T. W. Kuhlen. Visual quality adjustment for volume rendering in a head-tracked virtual environment. IEEE transactions on visualization and computer graphics, 22(4):1472– 1481, 2016

    work page 2016

  27. [27]

    J. M. Harris and L. M. Wilcox. The role of monocularly visible regions in depth and surface perception. Vision research, 49(22):2666–2685, 2009

    work page 2009

  28. [28]

    S. G. Hart and L. E. Staveland. Development of nasa-tlx (task load index): Results of empirical and theoretical research. Advances in psychology, 52:139–183, 1988

    work page 1988

  29. [29]

    Hayashi, T

    R. Hayashi, T. Maeda, S. Shimojo, and S. Tachi. An integrative model of binocular vision: a stereo model utilizing interocularly unpaired points produces both depth and binocular rivalry. Vision Research, 44(20):2367– 2380, 2004

    work page 2004

  30. [30]

    Healey and J

    C. Healey and J. Enns. Attention and visual memory in visualization and computer graphics. IEEE Transactions on Visualization and Computer Graphics, 18(7):1170–1188, July 2012. doi: 10.1109/TVCG.2011.127

    work page doi:10.1109/tvcg.2011.127 2012

  31. [31]

    Huang and H

    L. Huang and H. Pashler. A boolean map theory of visual attention. Psychological review, 114(3):599, 2007

    work page 2007

  32. [32]

    D. E. Huber and C. G. Healey. Visualizing data with motion. In Visualiza- tion, 2005. VIS 05. IEEE, pp. 527–534. IEEE, 2005

    work page 2005

  33. [33]

    HKKINEN and G

    J. HKKINEN and G. NYMAN. Depth asymmetry in da vinci stereopsis. Vision Research, 36(23):3815 – 3819, 1996. doi: 10.1016/0042-6989(96) 00099-5

    work page doi:10.1016/0042-6989(96 1996

  34. [34]

    Itti and C

    L. Itti and C. Koch. Computational modelling of visual attention. Nature reviews. Neuroscience, 2(3):194, 2001

    work page 2001

  35. [35]

    A. P. Johnston, J. Rae, N. Ariotti, B. Bailey, A. Lilja, R. Webb, C. Ferguson, S. Maher, T. P. Davis, R. I. Webb, et al. Journey to the centre of the cell: Virtual reality immersion into scientific data.Traffic, 19(2):105–110, 2018

    work page 2018

  36. [36]

    B. Julesz. Binocular depth perception of computer-generated patterns. Bell Labs Technical Journal, 39(5):1125–1162, 1960

    work page 1960

  37. [37]

    B. Julesz. Foundations of cyclopean perception. 1971

    work page 1971

  38. [38]

    Julesz et al

    B. Julesz et al. Textons, the elements of texture perception, and their interactions. Nature, 290(5802):91–97, 1981

    work page 1981

  39. [39]

    Kratz, M

    A. Kratz, M. Hadwiger, A. Fuhrmann, R. Splechtna, and K. B¨uhler. Gpu- based high-quality volume rendering for virtual environments. In Interna- tional Workshop on Augmented Environments for Medical Imaging and Computer Aided Surgery (AMI-ARCS), vol. 2006. Citeseer, 2006

    work page 2006

  40. [40]

    Krekhov, S

    A. Krekhov, S. Cmentowski, K. Emmerich, M. Masuch, and J. Kr ¨uger. Gullivr: A walking-oriented technique for navigation in virtual reality games based on virtual body resizing. In Proceedings of the 2018 Annual Symposium on Computer-Human Interaction in Play , CHI PLAY ’18, pp. 243–256. ACM, New York, NY , USA, 2018. doi: 10.1145/3242671. 3242704

    work page doi:10.1145/3242671 2018

  41. [41]

    Krekhov and J

    A. Krekhov and J. Kr¨uger. Deadeye: A novel preattentive visualization technique based on dichoptic presentation. IEEE transactions on visual- ization and computer graphics, 25(1):936–945, 2019

    work page 2019

  42. [42]

    Kruger and R

    J. Kruger and R. Westermann. Acceleration techniques for gpu-based volume rendering. In Proceedings of the 14th IEEE Visualization 2003 (VIS’03), p. 38. IEEE Computer Society, 2003

    work page 2003

  43. [43]

    O.-H. Kwon, C. Muelder, K. Lee, and K.-L. Ma. Spherical layout and rendering methods for immersive graph visualization. In 2015 IEEE Pacific Visualization Symposium (PacificVis), pp. 63–67. IEEE, 2015

    work page 2015

  44. [44]

    O.-H. Kwon, C. Muelder, K. Lee, and K.-L. Ma. A Study of Layout, Rendering, and Interaction Methods for Immersive Graph Visualization. IEEE Transactions on Visualization and Computer Graphics, 22(7):1802– 1815, 2016

    work page 2016

  45. [45]

    O.-H. Kwon, C. Muelder, K. Lee, and K.-L. Ma. A study of layout, rendering, and interaction methods for immersive graph visualization. IEEE transactions on visualization and computer graphics, 22(7):1802– 1815, 2016

    work page 2016

  46. [46]

    B. Laha, K. Sensharma, J. D. Schiffbauer, and D. A. Bowman. Effects of immersion on visual analysis of volume data. IEEE Transactions On Visualization & Computer Graphics, (4):597–606, 2012

    work page 2012

  47. [47]

    Litjens, T

    G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoo- rian, J. A. Van Der Laak, B. Van Ginneken, and C. I. S´anchez. A survey on deep learning in medical image analysis. Medical image analysis , 42:60–88, 2017

    work page 2017

  48. [48]

    L. Liu, S. B. Stevenson, and C. M. Schor. Binocular matching of dissimilar features in phantom stereopsis. Vision Research, 37(5):633–644, 1997

    work page 1997

  49. [49]

    N. K. Logothetis, D. A. Leopold, and D. L. Sheinberg. What is rivalling during binocular rivalry? Nature, 380(6575):621, 1996

    work page 1996

  50. [50]

    Marr and T

    D. Marr and T. Poggio. A computational theory of human stereo vision. Proceedings of the Royal Society of London. Series B. Biological Sciences, 204(1156):301–328, 1979

    work page 1979

  51. [51]

    D. Marr, T. Poggio, et al. Cooperative computation of stereo disparity. From the Retina to the Neocortex, pp. 239–243, 1976

    work page 1976

  52. [52]

    McLeod, J

    P. McLeod, J. Driver, and J. Crisp. Visual search for a conjunction of movement and form is parallel. Nature, 332(6160):154–155, 1988

    work page 1988

  53. [53]

    Mil´an, W

    M. Mil´an, W. David, and M. Stefan. Extending a virtual reality nasal cavity education tool with volume rendering. In 2018 IEEE International Con- ference on Teaching, Assessment, and Learning for Engineering (TALE), pp. 811–814. IEEE, 2018

    work page 2018

  54. [54]

    H. J. M ¨uller and A. von Muhlenen. Visual search for conjunctions of motion and form: The efficiency of attention to static versus moving items depends on practice. Visual Cognition, 6(3-4):385–408, 1999

    work page 1999

  55. [55]

    Nakayama and S

    K. Nakayama and S. Shimojo. Da vinci stereopsis: Depth and subjec- tive occluding contours from unpaired image points. Vision Research, 30(11):1811 – 1825, 1990. Optics Physiology and Vision. doi: 10.1016/ 0042-6989(90)90161-D

    work page 1990

  56. [56]

    Nakayama and G

    K. Nakayama and G. H. Silverman. Serial and parallel processing of visual feature conjunctions. Nature, 320(6059):264–265, 1986

    work page 1986

  57. [57]

    Noton and L

    D. Noton and L. Stark. Scanpaths in saccadic eye movements while viewing and recognizing patterns. Vision Research, 11(9):929 – IN8, 1971. doi: 10.1016/0042-6989(71)90213-6

    work page doi:10.1016/0042-6989(71)90213-6 1971

  58. [58]

    Novotny, J

    J. Novotny, J. Tveite, M. L. Turner, S. Gatesy, F. Drury, P. Falkingham, and D. H. Laidlaw. Developing virtual reality visualizations for unsteady flow analysis of dinosaur track formation using scientific sketching. IEEE transactions on visualization and computer graphics, 25(5):2145–2154, 2019

    work page 2019

  59. [59]

    A. J. OToole and C. L. Walker. On the preattentive accessibility of stereoscopic disparity: Evidence from visual search. Perception & Psy- chophysics, 59(2):202–218, 1997

    work page 1997

  60. [60]

    C. L. Paffen, R. S. Hessels, and S. Van der Stigchel. Interocular conflict attracts attention. Attention, Perception, & Psychophysics, 74(2):251–256, 2012

    work page 2012

  61. [61]

    C. L. E. Paffen, I. T. C. Hooge, J. S. Benjamins, and H. Hogendoorn. A search asymmetry for interocular conflict. Attention, Perception, & Psychophysics, 73(4):1042–1053, May 2011. doi: 10.3758/s13414-011 -0100-3

    work page doi:10.3758/s13414-011 2011

  62. [62]

    Porac and S

    C. Porac and S. Coren. The dominant eye. Psychological bulletin, 83(5):880, 1976

    work page 1976

  63. [63]

    R. A. Ruddle and S. Lessels. The benefits of using a walking interface to navigate virtual environments. ACM Transactions on Computer-Human Interaction (TOCHI), 16(1):5, 2009

    work page 2009

  64. [64]

    R. A. Ruddle, E. V olkova, and H. H. B¨ulthoff. Walking improves your cognitive map in environments that are large-scale and large in extent. ACM Transactions on Computer-Human Interaction (TOCHI), 18(2):10, 2011

    work page 2011

  65. [65]

    Scholl, S

    I. Scholl, S. Suder, and S. Schiffer. Direct volume rendering in virtual reality. In A. Maier, T. M. Deserno, H. Handels, K. H. Maier-Hein, C. Palm, and T. Tolxdorff, eds., Bildverarbeitung f¨ur die Medizin 2018, pp. 297–302. Springer Berlin Heidelberg, Berlin, Heidelberg, 2018

    work page 2018

  66. [66]

    Schuchardt and D

    P. Schuchardt and D. A. Bowman. The benefits of immersion for spatial understanding of complex underground cave systems. In Proceedings of the 2007 ACM symposium on Virtual reality software and technology, pp. 121–124. ACM, 2007

    work page 2007

  67. [67]

    R. Shen, P. Boulanger, and M. Noga. Medvis: A real-time immersive visualization environment for the exploration of medical volumetric data. In 2008 Fifth International Conference BioMedical Visualization: Infor- mation Visualization in Medical and Biomedical Informatics, pp. 63–68. IEEE, 2008

    work page 2008

  68. [68]

    Shimojo and K

    S. Shimojo and K. Nakayama. Real world occlusion constraints and binocular rivalry. Vision research, 30(1):69–80, 1990

    work page 1990

  69. [69]

    Shimojo and K

    S. Shimojo and K. Nakayama. Interocularly unpaired zones escape local binocular matching. Vision Research, 34(14):1875–1881, 1994

    work page 1994

  70. [70]

    Shimojo, G

    S. Shimojo, G. H. Silverman, and K. Nakayama. An occlusion-related mechanism of depth perception based on motion and interocular sequence. Nature, 333(6170):265, 1988

    work page 1988

  71. [71]

    B. Suh, A. Woodruff, R. Rosenholtz, and A. Glass. Popout prism: Adding perceptual principles to overview+detail document interfaces. In Proceed- ings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’02, pp. 251–258. ACM, New York, NY , USA, 2002

    work page 2002

  72. [72]

    D. Y . Teller and E. Galanter. Brightnesses, luminances, and fechners paradox. Perception & Psychophysics, 2(7):297–300, 1967

    work page 1967

  73. [73]

    J. T. Townsend. Serial vs. parallel processing: Sometimes they look like tweedledum and tweedledee but they can (and should) be distinguished. Psychological Science, 1(1):46–54, 1990

    work page 1990

  74. [74]

    Treisman and S

    A. Treisman and S. Gormican. Feature analysis in early vision: Evidence from search asymmetries. Psychological review, 95(1):15, 1988

    work page 1988

  75. [75]

    Treisman and J

    A. Treisman and J. Souther. Illusory words: The roles of attention and of top–down constraints in conjoining letters to form words. Journal of Experimental Psychology: Human Perception and Performance, 12(1):3, 1986

    work page 1986

  76. [76]

    A. M. Treisman and G. Gelade. A feature-integration theory of attention. Cognitive psychology, 12(1):97–136, 1980

    work page 1980

  77. [77]

    Tsirlin, L

    I. Tsirlin, L. M. Wilcox, and R. S. Allison. Da vinci decoded: Does da vinci stereopsis rely on disparity? Journal of Vision, 12(12):2–2, 2012

    work page 2012

  78. [78]

    Waldner, M

    M. Waldner, M. L. Muzic, M. Bernhard, W. Purgathofer, and I. Viola. Attractive flicker: Guiding attention in dynamic narrative visualizations. IEEE Transactions on Visualization and Computer Graphics, 20(12):2456– 2465, Dec. 2014

    work page 2014

  79. [79]

    C. Ware. Information visualization: perception for design. Elsevier, 2012

    work page 2012

  80. [80]

    Ware and G

    C. Ware and G. Franck. Evaluating stereo and motion cues for visualizing information nets in three dimensions. ACM Transactions on Graphics (TOG), 15(2):121–140, 1996

    work page 1996

Showing first 80 references.