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REVIEW 4 major objections 4 minor 15 references

Evaluating Joint Attention for Mixed-Presence Collaboration on Wall-Sized Displays

T0 review · 4 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Head gaze from depth cameras can detect and quantify joint attention in mixed-presence collaboration on wall-sized displays.

desk verdict Honest short paper with a usable pipeline and a published dataset; the headline correlation result needs a ground-truth check before it supports general conclusions. read the letter →

arxiv 2507.15443 v1 pith:4RLRJAWW submitted 2025-07-21 cs.HC

Adrien Coppens , Valérie Maquil This is my paper · ORCID
classification cs.HC
keywords Wall-sizeddisplaysMixed-presencecollaborationJointattentionHeadgazeDepthcamerabodytrackingWorkspaceawarenessSpearmancorrelationnormalization
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper tries to establish that head gaze data captured by depth cameras, rather than wearable eye trackers, can serve as an unobtrusive and effective basis for measuring joint attention in mixed-presence collaboration on wall-sized displays. The authors describe a pipeline that records, normalizes, and merges head-gaze time series from two rooms, then quantify joint attention by checking whether all four participants' gaze targets stay within a distance threshold corresponding to the size of the art pieces they discuss. In a preliminary analysis of one session, they report that horizontal gaze values are correlated, particularly between participants in the same room, and also, more weakly, across the two sites. The reason this matters is that robust evaluation of collaboration quality around room-scale displays currently relies on eye-tracking hardware that is either too constrained or obtrusive, and the proposed method is meant to fill that gap.

What carries the argument

The central object is head gaze, i.e., the direction of a participant's head as estimated by depth-camera body tracking, used as a stand-in for eye gaze. The argument is carried by a processing pipeline: record body-tracking streams in both rooms, merge and normalize them into a single time series, interpolate over tracking losses, then apply Spearman correlation to examine coupling and a Euclidean-distance threshold over gaze targets to define joint attention; the threshold is anchored to the maximum dimensions of the art pieces in the shared web-based layout. A key implementation detail is that gaze values are normalized between 0 and 1 for both axes so that the two rooms' differently sized displays are comparable.

What would settle it

Equip participants in one mixed-presence session with eye-tracking glasses while the depth cameras record head gaze, then compare the two gaze targets per moment; if the two systems disagree about whether all four users were focused on the same art piece in more than a small fraction of the session, the head-gaze proxy is not reliable for joint attention.

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Extended reading notes

Core claim

On the paper's own terms, the central claim is that joint attention in a four-user, two-room wall-sized display setting can be detected and quantified from head gaze alone. The authors define joint attention operationally: at each timestamp, compute the maximum Euclidean distance between the normalized gaze targets of all four participants, and if that maximum falls below a threshold set by the maximum dimensions of an art piece, count the moment as joint attention; a second variant drops the worst participant to capture three-out-of-four joint attention. Analyzing one session, they find that horizontal gaze values are correlated across users, with the strongest correlations between collocated participants, and cross-site correlations present but weaker. These results are presented as promising early evidence that the pipeline yields meaningful insight, with the explicit next step being to aggregate across all sessions and conditions.

Load-bearing premise

The measure stands on head gaze from depth-camera body tracking being a trustworthy stand-in for where people are actually looking; if participants often look without turning their heads, joint attention is systematically undercounted.

Editorial extensions

If this is right

  • If head gaze suffices, mixed-presence collaboration can be evaluated without wearable or fixed eye trackers, removing a source of obtrusiveness that can alter natural behavior.
  • Joint attention becomes a time-varying quantity, so sessions can be compared on how much of the collaboration was spent with all four, or three of four, participants focused on the same display area.
  • Separating same-room from cross-site gaze correlations gives a direct measure of how strongly collocated partners coordinate their attention versus how much remote coupling the awareness cues produce.
  • The threshold-based definition ties joint attention to meaningful task objects (art pieces), making the metric interpretable rather than purely geometric.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial extension: if head gaze lags true gaze, the method likely underestimates joint attention during quick glances; a head-mounted eye-tracker comparison in the same sessions would show whether the systematic error is acceptable.
  • Editorial extension: the same pipeline applied to short windows around deictic references, as the authors suggest, could test whether joint attention spikes exactly when one participant points or names an art piece.
  • Editorial extension: comparing the screen-wide attention-cue condition against a no-cue condition in the full dataset would quantify how much the cue improves cross-site joint attention, which the single-session analysis cannot yet establish.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. This manuscript proposes a pipeline for measuring joint attention in mixed-presence collaboration around wall-sized displays using head-gaze data from Azure Kinect depth cameras and the psi framework. Section 2 describes merging recordings from two rooms, applying a time offset, manually filtering and merging skeleton IDs, linearly interpolating missing tracking data, and normalizing gaze coordinates. Section 3 computes Spearman correlations between participants' horizontal gaze values and operationalizes joint attention as all-four or three-out-of-four participants having gaze targets within a distance threshold derived from maximum art-piece dimensions, reported as the share of time below threshold over 5-second windows. The empirical content is a single selected session, presented as preliminary.

Significance. The contribution is a practical, unobtrusive evaluation method rather than a fully validated finding. Strengths include a concrete and reusable pipeline, an explicit operational definition of joint attention, and an openly available dataset on Zenodo. The central empirical claim—that same-room participants' horizontal gaze values are correlated—is plausible but not yet established because the head-gaze proxy and the preprocessing choices are not validated. If confirmed, the method could enable room-scale collaboration studies without obtrusive eye trackers; at present, the paper should be read as a proof-of-concept demonstration of the pipeline.

major comments (4)
  1. [1, 3] The load-bearing premise that head-gaze direction from Azure Kinect is a valid proxy for actual gaze is not validated. Section 1 cites Stiefelhagen et al. [13] for the claim that head tracking 'still provides a good idea' of attention, but that reference comes from a different setting and does not establish accuracy for four users at room scale on wall-sized displays, where peripheral glances and gaze at hands or at the other participant can decouple head orientation from gaze. Without a ground-truth comparison (e.g., simultaneous eye tracking or manual coding of where people are looking), the joint-attention measure can be systematically distorted; please add such a validation, or present the results as an unvalidated proof of concept rather than as evidence about joint attention.
  2. [2] Linear interpolation is used to fill missing tracking data before computing correlations and joint-attention shares, yet the amount and location of missing data are not reported. Interpolated segments can create smooth common trends across users, which can inflate Spearman's rho and artificially increase the share of time below the distance threshold. Please report per-participant missing-data rates and run a sensitivity analysis that recomputes the metrics on non-interpolated samples only or otherwise flags interpolated segments.
  3. [3, Figure 2] The correlations in Figure 2 are reported without confidence intervals, significance tests, or correction for temporal autocorrelation. With a single session and strongly autocorrelated time series, the nominal number of samples overstates precision, so the statement that 'horizontal gaze values are indeed correlated' is not statistically supported. Provide bootstrap or permutation-based intervals that account for the time-series structure, and show results for all sessions with a documented session-selection rule rather than an unspecified 'chosen session'.
  4. [3] The joint-attention measure depends on several unexamined parameter choices: the distance threshold derived from maximum art-piece dimensions, the 5-second time window, and the time offset used to synchronize the two rooms. Small changes in the threshold will directly change the reported share of joint attention. Please provide a sensitivity analysis for these parameters and justify the window length and the offset determination procedure.
minor comments (4)
  1. [3] In the sentence 'we computed the the share of time spent below threshold', the definite article 'the' is duplicated.
  2. [Figures 1 and 2] Figure 1 has no axis labels or legend identifying the four participants, and Figure 2's right panel does not state whether the plotted share refers to all-four or three-out-of-four joint attention; please clarify both figures.
  3. [1, Reference [13]] The cited validation for head tracking as a gaze proxy is from 1999; given advances in depth cameras and tracking algorithms, please justify the transfer to modern Azure Kinect data or cite a more recent validation.
  4. [Abstract, 3] The abstract and Section 4 correctly frame the analysis as preliminary, but the Section 3 wording 'indeed correlated' is stronger than the evidence supports; aligning the wording with the exploratory scope would avoid overinterpretation.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: joint attention is operationally defined from gaze proximity, and the reported correlations are descriptive rather than derived from the paper's own conclusions.

full rationale

The paper does not present any derivation whose conclusion is equivalent to its inputs. Joint attention is defined directly as all gaze targets being within a Euclidean distance threshold, with the threshold set from art-piece dimensions and display-normalized coordinates. This is an operational definition, not a fitted parameter or a prediction drawn from the same data. The Spearman correlations compare raw horizontal gaze time series and are reported descriptively with hedged language ('It seems that horizontal gaze values are indeed correlated'), so no correlation is predicted from a model that was fit to it. The use of the authors' prior tracking pipeline [3] and their own categorization of gaze as attention [4] are methodological or terminological self-citations, but they are not load-bearing for the central empirical observation, which rests on data collection and an externally cited justification for head gaze as a proxy [13]. Potential issues such as unvalidated head-gaze accuracy, interpolation-induced correlation, and task-structure confounds are correctness or validity concerns, not circularity. Accordingly, the circularity score is 0.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The method depends on a small number of hand-set parameters (distance threshold, time offset, five-second window) and on assumptions about sensor validity and data cleaning. No invented entities are introduced; the central measure is a definitional threshold rather than a fitted model.

free parameters (3)
  • joint attention distance threshold = Not numerically specified; derived from maximum art piece dimensions as a normalized percentage of display size
    The definition of joint attention depends directly on this threshold. Changing it changes the share of joint attention reported in Figure 2.
  • time offset for syncing the two rooms = Manual, session-specific offset
    The offset was determined based on on-screen events and audio, and errors in this offset propagate to all pairwise correlations and joint attention shares.
  • time window for joint attention share = 5 seconds
    The share of time below threshold is computed over five-second windows; the window size affects the temporal resolution and the reported values.
assumptions (4)
  • domain assumption Head gaze from Azure Kinect is a valid proxy for visual attention at room scale
    Introduced in Section 1: depth-camera head tracking is described as less accurate than eye tracking but still providing a good idea of where attention is [13]. The entire joint attention measure depends on this assumption.
  • ad hoc to paper Linear interpolation of missing tracking data does not distort joint attention
    Section 2 states missing gaze data were linearly interpolated to ensure values for all users at all times. Interpolation during tracking dropouts can create artificial gaze alignment and spurious joint attention.
  • ad hoc to paper Skeleton ID merging and manual filtering are correct
    Section 2 describes manually merging inconsistent skeleton IDs and filtering out non-participants. This relies on the authors' judgment and is not reproducible from the paper alone.
  • standard math Spearman correlation captures relevant gaze similarity
    Section 3 uses Spearman's rho for robustness to outliers. It is a standard choice but only measures monotone association, not the actual spatial proximity that joint attention requires.

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Cite this review

Pith. "Pith review of Evaluating Joint Attention for Mixed-Presence Collaboration on Wall-Sized Displays." pith.science (2026). https://pith.science/paper/4RLRJAWW

@misc{pith2026250715443,
  author       = {Pith},
  title        = {Pith review of: Evaluating Joint Attention for Mixed-Presence Collaboration on Wall-Sized Displays},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4RLRJAWW}},
  note         = {Machine review of arXiv:2507.15443}
}
read the original abstract

To understand and quantify the quality of mixed-presence collaboration around wall-sized displays, robust evaluation methodologies are needed, that are adapted for a room-sized experience and are not perceived as obtrusive. In this paper, we propose our approach for measuring joint attention based on head gaze data. We describe how it has been implemented for a user study on mixed presence collaboration with two wall-sized displays and report on the insights we gained so far from its implementation, with a preliminary focus on the data coming from one particular session.

Figures

Figures reproduced from arXiv: 2507.15443 by the authors.

Figure 1
Figure 1. Scatter plot of horizontal gaze values for all four participants over time, for the mixed-presence part of a session. [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Correlation matrix for horizontal gaze values (left) and share of joint attention over time (right) for the chosen session. [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

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Reference graph

Works this paper leans on

15 extracted references · 13 canonical work pages

  1. [13]

    Rainer Stiefelhagen, Michael Finke, Jie Yang, and Alex Waibel. 1999. From gaze to focus of attention. In Visual Information and Information Systems: Third International Conference, VISUAL’99 Amsterdam, The Netherlands, June 2–4, 1999 Proceedings 3. Springer, 765–772

    work page 1999

  2. [1]

    [n. d.]. Dataset of gaze target values for a mixed presence experiment (Zenodo repository). https://doi.org/10.5281/zenodo.15027047

    work page doi:10.5281/zenodo.15027047

  3. [2]

    Dan Bohus, Sean Andrist, Ashley Feniello, Nick Saw, Mihai Jalobeanu, Patrick Sweeney, Anne Loomis Thompson, and Eric Horvitz. 2021. Platform for Situated Intelligence. arXiv:2103.15975 [cs.AI]

    work page Pith review arXiv 2021

  4. [3]

    Adrien Coppens, Johannes Hermen, Lou Schwartz, Christian Moll, and Valérie Maquil. 2024. Supporting mixed-presence awareness across wall-sized displays using a tracking pipeline based on depth cameras. Proceedings of the ACM on Human-Computer Interaction 8, EICS (2024), 1–32

    work page 2024

  5. [4]

    Adrien Coppens, Lou Schwartz, and Valérie Maquil. 2024. Workspace Awareness Needs in Mixed-Presence Collaboration on Wall-Sized Displays. In International Conference on Cooperative Design, Visualization and Engineering. Springer, 20–30

    work page 2024

  6. [5]

    Joost CF De Winter, Samuel D Gosling, and Jeff Potter. 2016. Comparing the Pearson and Spearman correlation coefficients across distributions and sample sizes: A tutorial using simulations and empirical data. Psychological methods 21, 3 (2016), 273

    work page 2016

  7. [6]

    Carl Gutwin and Saul Greenberg. 2002. A descriptive framework of workspace awareness for real-time groupware. Computer Supported Cooperative Work (CSCW) 11 (2002), 411–446. doi:10.1023/A:1021271517844

    doi:10.1023/a:1021271517844 2002

  8. [7]

    Patrick Jermann, Darren Gergle, Roman Bednarik, and Susan Brennan. 2012. Duet 2012: Dual eye tracking in CSCW. InProceedings of the ACM 2012 conference on Computer Supported Cooperative Work Companion . 23–24

    work page 2012

Show all 15 references
  1. [8]

    Magdalena Mateescu, Christoph Pimmer, Carmen Zahn, Daniel Klinkhammer, and Harald Reiterer. 2021. Collaboration on large interactive displays: a system- atic review. Human–Computer Interaction 36, 3 (2021), 243–277

    2021

  2. [9]

    Tao Ni, Greg S Schmidt, Oliver G Staadt, Mark A Livingston, Robert Ball, and Richard May. 2006. A survey of large high-resolution display technologies, techniques, and applications. In IEEE Virtual Reality Conference (VR 2006) . IEEE, 223–236

    2006

  3. [10]

    Jakub Štěpán Novák, Jan Masner, Petr Benda, Pavel Šimek, and Vojtěch Merunka

  4. [11]

    Bertrand Schneider, Kshitij Sharma, Sebastien Cuendet, Guillaume Zufferey, Pierre Dillenbourg, and Roy Pea. 2018. Leveraging mobile eye-trackers to capture joint visual attention in co-located collaborative learning groups. International Journal of Computer-Supported Collabora...

    2018

  5. [12]

    Stacey D Scott, Karen D Grant, and Regan L Mandryk. 2003. System guidelines for co-located, collaborative work on a tabletop display. In ECSCW 2003: Proceedings of the Eighth European Conference on Computer Supported Cooperative Work 14–18 September 2003, Helsinki, Finland . S...

    2003

  6. [14]

    Katarzyna Wisiecka, Yuumi Konishi, Krzysztof Krejtz, Mahshid Zolfaghari, Birgit Kopainsky, Izabela Krejtz, Hideki Koike, and Morten Fjeld. 2023. Supporting complex decision-making: evidence from an eye tracking study on in-person and remote collaboration. ACM Transactions on C...

    2023

  7. [2024]

    Interna- tional Journal of Human–Computer Interaction 40, 17 (2024), 4484–4500

    Eye tracking, usability, and user experience: A systematic review. Interna- tional Journal of Human–Computer Interaction 40, 17 (2024), 4484–4500

    2024

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Reviewed August 6, 2026 · model on record in the stance chip above.