Pith. sign in

REVIEW 4 major objections 6 minor 53 references

The paper claims that an attention-driven VR-AI workflow makes product form design four times faster while improving expert-rated quality.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

An attention-aware VR plus agentic-AI workflow (EUPHORIA-RETINA) is reported to make product form design over four times faster and to produce expert-preferred renderings, based on a small comparative study.

T0 review reviewed 2026-08-05 challenge →

load-bearing objection The efficiency win is real and the system is a genuine integration, but the Phase 2 evidence contradicts itself and undercuts the attention-preference mechanism the authors lean on. the 4 major comments →

arxiv 2508.19708 v1 pith:PFRFUXZ6 submitted 2025-08-27 cs.HC cs.AI

Attention is also needed for form design

B. Sankar , Dibakar Sen This is my paper
classification cs.HC cs.AI
keywords form designeye-trackingvirtual realityagentic AImoodboardingimplicit preferencegenerative AIattention
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The reading

The paper claims that the slow, subjective, experience-dependent stage of product form design—the moodboard—can be replaced by an attention-driven pipeline in which the designer simply looks at images in VR while eye-tracking records what holds their gaze, and agentic AI converts those gaze-weighted images into concept sketches and photorealistic renderings. The claim is supported by a three-phase study: gaze duration correlates with explicit preference in free exploration; emotional priming steers different people toward convergent selections; and a four-workflow comparison with four designers shows the fully automated EUPHORIA-RETINA path averaging 51 minutes versus 4 hours 23 minutes for the conventional route. A panel of 50 experts assigned the automated path's designs the highest Plackett-Luce worthiness scores and highest design-effectiveness ratings on all four briefs. If true, the designer's role shifts from hands-on sketching and searching to directing and curating an AI generation process.

Core claim

On its own terms, the paper establishes a new workflow and tests it end-to-end: EUPHORIA surrounds a designer with hundreds of images in a VR 'moodspace', tracks gaze continuously, and ranks images by total fixation duration; RETINA then crops the attended regions (ROIs), extracts shape via Holistically-Nested Edge Detection, derives colour palettes, and uses LLM/LIM agents to write shape, texture, and colour descriptors before generating concept sketches and photorealistic renderings. In the decisive experiment, four designers solved four intentionally conflicting design briefs through four workflows in a Latin Square design. The fully automated Path RD was not only over four times faster b

What carries the argument

The load-bearing mechanism is the implicit-attention signal: in the EUPHORIA VR moodspace, a ray-tracing eye-tracker records where and how long the designer looks at each of hundreds of displayed images, and total fixation duration is treated as a ranked preference signal. RETINA's agentic pipeline then makes that signal legible by producing three feature maps—an ROI collage of high-attention image regions, an HED edge collage for shape, and a dominant colour palette—plus LLM-generated textual descriptors, which feed two generative agents (concept sketch and photorealistic rendering). The inverse Plackett-Luce model with gradient-descent maximum-likelihood estimation converts 50 experts' ord

Load-bearing premise

The whole pipeline depends on the claim that total gaze-fixation duration in the VR moodspace encodes the aesthetic preference that should drive the design, even though the paper's own Phase 2 found that correlation disappears in goal-directed search (r = -0.0915, p = 0.2786).

What would settle it

Run Phase 3 with EUPHORIA's gaze ranking replaced by random selection from the same keyword-populated image set, keeping RETINA identical; if expert worthiness and effectiveness scores do not drop, the attention mechanism is not the source of the reported gains. Alternatively, check whether per-participant fixation-duration rankings predict the designer's stated preferred rendering; if not, the preference signal is weak.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • Moodboarding's most time-consuming stages—collection, selection, composition—can be automated from gaze data, freeing designers for reflection and curation.
  • Novice designers can produce expert-preferred product forms without executing manual sketching, because RETINA performs the feature-to-form translation.
  • The designer's job becomes strategic direction: providing keywords, exploring the moodspace, and selecting among AI-generated concepts rather than generating every option by hand.
  • Design briefs with deliberately contradictory style keywords are feasible to reconcile through a larger implicitly-selected image set than a human moodboarder would compile.
  • A fourfold time saving at equal or higher expert-rated quality makes rapid iteration across many concepts practical.

Where Pith is reading between the lines

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

  • The Phase 2 decoupling of gaze duration from preference under goal-directed search (r = -0.0915, p = 0.2786) suggests that EUPHORIA's use of fixation duration as the preference signal in Phase 3 may not be doing the causal work the title implies; the quality gains could come largely from RETINA's generation from a much larger, keyword-filtered image set (over 1,200 images vs. roughly 100). This is
  • A direct test: replacing the gaze-ranked image set with a keyword-matching baseline while keeping RETINA fixed would isolate whether gaze adds quality or only convenience. This is my suggested extension.
  • The same attention-capture-plus-agentic-generation loop could plausibly be transferred to typography, interface design, or brand identity, where moodboarding is also used, though the paper does not test those domains.
  • The worthiness-score comparison would be stronger if physical prototypes or rendered-user testing were included, since expert ratings of digital renders may not predict real-world appeal; the authors list this as a limitation and my inference follows from it.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. This manuscript proposes and evaluates an attention-aware form design framework combining EUPHORIA (a VR environment with eye-tracking that is intended to capture implicit aesthetic preference) and RETINA (an agentic AI pipeline that translates gaze-derived features into concept sketches and renderings). The paper reports three phases: Phase 1 (n=30) finds a positive correlation between gaze fixation duration and explicit preference in unconstrained exploration (r=0.3819, p<0.0001); Phase 2 (n=30) reports that emotional priming produces convergence in visual selections and also decouples fixation duration from preference in goal-directed search; Phase 3 (4 designers x 4 workflows in a Latin-square design) compares a conventional workflow, two partially automated workflows, and a fully automated EUPHORIA-RETINA workflow (Path RD). The headline claims are that Path RD reduces average total design time from 04:22:45 to 00:51:30 and that Path RD outputs receive the highest expert-based inverse Plackett-Luce worthiness scores and the highest Design Effectiveness scores across all four problems. The authors conclude that the work demonstrates a paradigm shift from Computer-Assisted Design to Designer-Assisting Computers.

Significance. The work is ambitious and, if the empirical claims hold, represents a concrete end-to-end validation of a gaze-driven, agentic AI design workflow. The paper has several genuine strengths: a custom VR implementation with integrated eye-tracking; a three-phase research structure; a Latin-square design in Phase 3; a large expert panel of 50 evaluators; blind presentation of final renderings; and explicit discussion of limitations. The use of an inverse Plackett-Luce model to convert ordinal rankings into worthy scores is a useful methodological contribution. However, the current evidence is not yet sufficient to support the central causal claims. The most serious issue is an internal inconsistency between Phase 2 and Phase 3: Phase 2 shows that fixation duration ceases to be a preference proxy in goal-directed search, yet Phase 3 uses fixation duration as the preference signal in the same kind of goal-directed task. In addition, the Phase 2 convergence claim is contradicted by the reported low median overlap, and the Phase 3 quality/efficiency claims lack inferential statistics despite being based on only four designers. The framework and results are promising, but these load-bearing

major comments (4)
  1. [§10.4.2 (Figure 25) vs. §5.2.2 (Table 6) and §11.3] Phase 2's goal-directed/conditioned-search result shows that fixation duration is not correlated with explicit preference (r=-0.0915, p=0.2786); the authors interpret long fixations as 'cognitive effort in assessing relevance.' Yet EUPHORIA's Phase 3 selection mechanism still ranks images by total fixation duration as the implicit preference signal, and Phase 3 is itself a goal-directed task (design briefs with keywords). The paper does not explain why a signal that was shown to decouple from preference in Phase 2 can still be used as a preference proxy in Phase 3. Unless this is resolved—e.g., by validating the fixation-preference link under the specific Phase 3 instructions, or by redefining the selection signal as task-relevance—the claim that superior Phase 3 outcomes are 'attention-driven' is unsupported; they could be due entirely to RETINA's generative capability.
  2. [§10.4.1 (Figures 22 and 23)] The quantitative distributions reported for Phase 2 are at odds with the conclusion that H3 was validated. The median pairwise cosine similarity is 0.0891 and the median number of common viewed images is 1.00. These are low values. The declaration of convergence relies on the block-diagonal appearance of the heatmap in Figure 22, but the distributions suggest that most pairs—possibly including many within-group pairs—had little overlap. To support H3, the authors need to report within-stimulus-group similarity separately from between-group similarity, test whether within-group values are significantly higher, and clarify whether the median of 1.00 common image applies to all pairs or to a subset.
  3. [§11.8 (Table 8, Figures 43 and 44)] The central efficiency and quality claims are based on only four designers. The fourfold time reduction (04:22:45 to 00:51:30) is reported as an average without variance, and the inverse Plackett-Luce worthiness scores are point estimates without uncertainty. No inferential test accompanies the claims that Path RD is 'consistently' highest. Given the strength of the conclusions, the paper should provide permutation tests or bootstrap confidence intervals for the worthiness and design-effectiveness scores and a paired analysis of completion times, or alternatively explicitly frame the Phase 3 results as descriptive pilot data without strong causal wording.
  4. [§7.2.1 (ROI Extraction Agent) and §5.2.2 (Eye-Tracking)] The ROI fixation-duration threshold and the image-selection attention threshold are described as 'predefined' but no values or sensitivity analyses are given. Because these thresholds determine which images and which image regions are passed to RETINA, the Phase 3 outcomes could depend on unstated free parameters. The authors should state the threshold values and test robustness to those choices, or justify the choices empirically using the Phase 1 and Phase 2 data.
minor comments (6)
  1. [§9.4.3, Figure 21 caption] The caption reads 'Phase 2: Participants’ Viewing Patterns (PCA Projection)' but this figure belongs to the Phase 1 analysis; the phase label should be corrected.
  2. [§10.4.1, Figure 22 caption] The caption refers to 'H1 (Stimulus-Driven Convergence),' but the corresponding hypothesis is H3. This incorrect hypothesis number should be fixed.
  3. [§8.3] The text says the detailed workflow is 'given in Figure 4'; based on the surrounding content, this appears to be a cross-reference to Table 4, not Figure 4.
  4. [§11.8.2] The phrase 'the unambiguous successor' is unclear; it should likely read 'the unambiguous winner' or similar.
  5. [§9.2.4] The phrase 'images they gave attention to' is awkward; use 'images they attended to'.
  6. [Abstract] The abstract describes the validation as a 'two-part study,' but the paper reports three experimental phases. This wording should be aligned with the actual structure.

Circularity Check

0 steps flagged

No significant circularity; the empirical chain is independently grounded.

full rationale

The paper's core derivation chain is not circular. Phase 1 establishes gaze-fixation duration as a proxy for preference using independent participants and explicit preference ratings (r=0.3819, p<0.0001, Section 9.4.1), so the EUPHORIA selection mechanism is not validated by its own outputs. Phase 3's quality comparisons rest on expert rankings and ratings collected blind to workflow origin (Section 11.4), and the Worthiness and Design Effectiveness scores are descriptive summaries of those expert judgments, not inputs to the system or fitted parameters renamed as predictions. The inverse Plackett-Luce model is fitted to the observed rankings and reports a latent worthiness score; this is a data summary, not a prediction from the system. The reference list contains no prior works by the present authors, so no self-citation chain is load-bearing. The Phase 2 result that fixation duration decouples from preference under goal-directed search (r=-0.0915, p=0.2786, Section 10.4.2) is a serious internal-validity concern for Phase 3's use of the same gaze signal, but it does not make the derivation circular: the paper itself reports the decoupling, and the Phase 3 outcome metrics are independently evaluated. No equation or construction reduces a claimed result to its own input. Therefore, the appropriate circularity score is 0.

Axiom & Free-Parameter Ledger

3 free parameters · 5 axioms · 0 invented entities

No new physical entities are introduced. The free parameters are primarily undocumented system thresholds and the fitted Plackett-Luce scores. The main axioms are domain assumptions about gaze meaning and expert judgment, plus standard statistical modeling assumptions.

free parameters (3)
  • ROI fixation-duration threshold = not reported
    Section 7.2.1 states that regions with cumulative fixation time exceeding a predefined threshold are cropped as ROIs. The threshold value is not given, and it directly affects which image regions are fed to RETINA.
  • Inverse Plackett-Luce worthiness scores = estimated per path and per problem (Fig. 43)
    The worth scores are estimated by gradient descent MLE from the expert rankings. They are outcomes used to support the superiority claim, and no confidence intervals are reported.
  • Image selection attention threshold = not reported
    The EUPHORIA system selects 'attention-selected images' based on fixation duration, but the cutoff that determines inclusion in the composed moodspace is not specified.
axioms (5)
  • domain assumption Gaze fixation duration is a valid proxy for aesthetic preference in the EUPHORIA environment.
    Validated in Phase 1 for free exploration, but Phase 2 shows the correlation disappears in goal-directed search, and Phase 3 still relies on this assumption.
  • domain assumption Expert ratings on eight aesthetic criteria and expert rankings are a valid operationalization of design quality.
    The paper uses 50 expert alumni ratings as the ground truth for design quality without comparison to user testing or physical prototypes.
  • domain assumption The four contradictory-style design briefs are representative of form design tasks.
    Used in Phase 3, but the authors themselves note the limitation that only simple consumer products were tested.
  • standard math Standard statistical models (Pearson correlation, Plackett-Luce MLE) are correctly applied.
    The paper states these methods and gives the Plackett-Luce likelihood, but does not provide diagnostics or tests for model fit.
  • domain assumption The image sets populated via API keyword search adequately sample the intended aesthetic space.
    EUPHORIA's API search is used in Paths RC and RD; the quality of retrieved images constrains what can be selected by gaze.

reviewed 2026-08-05 · how reviews work

0 comments
Cite this review

Pith. "Pith review of Attention is also needed for form design." pith.science (2026). https://pith.science/paper/PFRFUXZ6

@misc{pith2026250819708,
  author       = {Pith},
  title        = {Pith review of: Attention is also needed for form design},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PFRFUXZ6}},
  note         = {Machine review of arXiv:2508.19708}
}
Share X Bluesky LinkedIn Reddit HN
read the original abstract

Conventional product design is a cognitively demanding process, limited by its time-consuming nature, reliance on subjective expertise, and the opaque translation of inspiration into tangible concepts. This research introduces a novel, attention-aware framework that integrates two synergistic systems: EUPHORIA, an immersive Virtual Reality environment using eye-tracking to implicitly capture a designer's aesthetic preferences, and RETINA, an agentic AI pipeline that translates these implicit preferences into concrete design outputs. The foundational principles were validated in a two-part study. An initial study correlated user's implicit attention with explicit preference and the next one correlated mood to attention. A comparative study where 4 designers solved challenging design problems using 4 distinct workflows, from a manual process to an end-to-end automated pipeline, showed the integrated EUPHORIA-RETINA workflow was over 4 times more time-efficient than the conventional method. A panel of 50 design experts evaluated the 16 final renderings. Designs generated by the fully automated system consistently received the highest Worthiness (calculated by an inverse Plackett-Luce model based on gradient descent optimization) and Design Effectiveness scores, indicating superior quality across 8 criteria: novelty, visual appeal, emotional resonance, clarity of purpose, distinctiveness of silhouette, implied materiality, proportional balance, & adherence to the brief. This research presents a validated paradigm shift from traditional Computer-Assisted Design (CAD) to a collaborative model of Designer-Assisting Computers (DAC). By automating logistical and skill-dependent generative tasks, the proposed framework elevates the designer's role to that of a creative director, synergizing human intuition with the generative power of agentic AI to produce higher-quality designs more efficiently.

Figures

Figures reproduced from arXiv: 2508.19708 by B. Sankar, Dibakar Sen.

Figure 1
Figure 1. Figure 1: A comparison between traditional and modern moodboarding techniques. (a) Traditional moodboarding involves creating [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Different types of Geometric Shape Abstractions [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: A collection of refined concept sketches of various [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: Examples of physical prototypes and mockups, [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The workflow of the EUPHORIA system, illustrating how the conventional moodboarding stages are transformed into an [PITH_FULL_IMAGE:figures/full_fig_p017_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: A schematic representation of the EUPHORIA system [PITH_FULL_IMAGE:figures/full_fig_p018_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: NAIVE Monolithic AI Workflow 7.1. RETINA Architecture The architecture of RETINA is a multi-agent system (MAS), managed by a central orchestrator agent as shown in [PITH_FULL_IMAGE:figures/full_fig_p020_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: RETINA Agentic AI Workflow with a representative input and the corresponding auto-generated output at each level [PITH_FULL_IMAGE:figures/full_fig_p023_10.png] view at source ↗
Figure 12
Figure 12. Figure 12: Normalized stacked bar plot of average fixation [PITH_FULL_IMAGE:figures/full_fig_p029_12.png] view at source ↗
Figure 14
Figure 14. Figure 14: Statistical distributions of viewing commonality [PITH_FULL_IMAGE:figures/full_fig_p030_14.png] view at source ↗
Figure 13
Figure 13. Figure 13: Heatmap of participant image viewing commonality, [PITH_FULL_IMAGE:figures/full_fig_p030_13.png] view at source ↗
Figure 15
Figure 15. Figure 15: Scatter plot of the Disagreement Score versus the [PITH_FULL_IMAGE:figures/full_fig_p030_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Comparison of participants’ exploration strategies. [PITH_FULL_IMAGE:figures/full_fig_p031_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Grid overview of all 150 visual stimuli with their corresponding rating distributions. Below each image, a histogram [PITH_FULL_IMAGE:figures/full_fig_p032_17.png] view at source ↗
Figure 19
Figure 19. Figure 19: Line plot of the average fixation time in ascending [PITH_FULL_IMAGE:figures/full_fig_p032_19.png] view at source ↗
Figure 18
Figure 18. Figure 18: Comparison of heatmaps for image engagement. [PITH_FULL_IMAGE:figures/full_fig_p032_18.png] view at source ↗
Figure 21
Figure 21. Figure 21: Phase 2: Participants’ Viewing Patterns (PCA [PITH_FULL_IMAGE:figures/full_fig_p033_21.png] view at source ↗
Figure 23
Figure 23. Figure 23: Statistical distributions of viewing commonality from [PITH_FULL_IMAGE:figures/full_fig_p036_23.png] view at source ↗
Figure 22
Figure 22. Figure 22: Heatmap of participant image viewing commonality [PITH_FULL_IMAGE:figures/full_fig_p036_22.png] view at source ↗
Figure 24
Figure 24. Figure 24: Scatter plot of the Disagreement Score versus [PITH_FULL_IMAGE:figures/full_fig_p036_24.png] view at source ↗
Figure 26
Figure 26. Figure 26: Line plot of the number of images viewed versus [PITH_FULL_IMAGE:figures/full_fig_p037_26.png] view at source ↗
Figure 25
Figure 25. Figure 25: Scatter plot of average rating versus average fixation [PITH_FULL_IMAGE:figures/full_fig_p037_25.png] view at source ↗
Figure 27
Figure 27. Figure 27: Normalized stacked bar plot of average fixation time [PITH_FULL_IMAGE:figures/full_fig_p038_27.png] view at source ↗
Figure 28
Figure 28. Figure 28: Bar chart showing the total number of unique images [PITH_FULL_IMAGE:figures/full_fig_p038_28.png] view at source ↗
Figure 30
Figure 30. Figure 30: Grid overview of all 150 visual stimuli with their corresponding rating distributions. Below each image, a histogram [PITH_FULL_IMAGE:figures/full_fig_p039_30.png] view at source ↗
Figure 32
Figure 32. Figure 32: Line plot of the average fixation time per image in [PITH_FULL_IMAGE:figures/full_fig_p039_32.png] view at source ↗
Figure 31
Figure 31. Figure 31: Phase 2 heatmaps of image engagement. (a) 31a [PITH_FULL_IMAGE:figures/full_fig_p039_31.png] view at source ↗
Figure 33
Figure 33. Figure 33: Participants’ Viewing Patterns (PCA Projection). [PITH_FULL_IMAGE:figures/full_fig_p039_33.png] view at source ↗
Figure 34
Figure 34. Figure 34: (Top) UMAP-based semantic clustering of participant thoughts, and (Bottom) distribution of extracted emotion keywords by stimulus phrase. in different contexts. The decoupling of the attention-preference link in a goal-directed task is a key insight. It clarifies that the meaning of a long fixation depends on the user’s goal: in free exploration (Phase 1), it signals liking or interest; in a conditioned s… view at source ↗
Figure 35
Figure 35. Figure 35: A high-level comparison of the four experimental workflow paths, illustrating the distribution of tasks between the [PITH_FULL_IMAGE:figures/full_fig_p043_35.png] view at source ↗
Figure 36
Figure 36. Figure 36: Heatmaps of the cumulative rank distributions for each of the four problem statements. Each heatmap shows the [PITH_FULL_IMAGE:figures/full_fig_p046_36.png] view at source ↗
Figure 37
Figure 37. Figure 37: The Inverse Plackett-Luce Maximum Likelihood [PITH_FULL_IMAGE:figures/full_fig_p047_37.png] view at source ↗
Figure 38
Figure 38. Figure 38: The complete design workflows and outputs for the four experimental paths (RA-RD) for the ’Portable Music Player’ [PITH_FULL_IMAGE:figures/full_fig_p048_38.png] view at source ↗
Figure 39
Figure 39. Figure 39: The complete design workflows and outputs for the four experimental paths (RA-RD) for the ’Study Lamp’ (S2) design [PITH_FULL_IMAGE:figures/full_fig_p048_39.png] view at source ↗
Figure 40
Figure 40. Figure 40: The complete design workflows and outputs for the four experimental paths (RA-RD) for the ’Handheld Flashlight’ (S3) [PITH_FULL_IMAGE:figures/full_fig_p049_40.png] view at source ↗
Figure 41
Figure 41. Figure 41: The complete design workflows and outputs for the four experimental paths (RA-RD) for the ’Candlestick Holder’ (S4) [PITH_FULL_IMAGE:figures/full_fig_p049_41.png] view at source ↗
Figure 42
Figure 42. Figure 42: Complete matrix of all 16 final product renderings from the Phase 3 study. The figure presents the final design outputs [PITH_FULL_IMAGE:figures/full_fig_p050_42.png] view at source ↗
Figure 44
Figure 44. Figure 44: The results for Design Effectiveness mirror and reinforce the findings from the worthiness scores. The designs from Paths RC and RD consistently and significantly outperformed those from Paths RA and RB. As shown in [PITH_FULL_IMAGE:figures/full_fig_p051_44.png] view at source ↗
Figure 43
Figure 43. Figure 43: Plackett-Luce worthiness scores for the four workflow path concepts (RA, RB, RC, RD) across each of the four product [PITH_FULL_IMAGE:figures/full_fig_p052_43.png] view at source ↗
Figure 44
Figure 44. Figure 44: Comparison of design effectiveness scores across the [PITH_FULL_IMAGE:figures/full_fig_p052_44.png] view at source ↗
Figure 45
Figure 45. Figure 45: Cumulative mean ratings from expert designers for the four design problems. Each spider plot shows the evaluation [PITH_FULL_IMAGE:figures/full_fig_p053_45.png] view at source ↗

discussion (0)

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

Reference graph

Works this paper leans on

53 extracted references · 40 canonical work pages

  1. [1]

    editor Annie Gentes, Fred eric Valentin, E.B.e. (Ed.), . title Mood boards as a tool for the ” in-discipline ” of design. IASDR Interplay 2015, Nov 2015 . publisher IASDR Interplay , address Brisbane, Australia

    work page 2015

  2. [2]

    , author Neta, M

    author Bar, M. , author Neta, M. , year 2006 . title Humans prefer curved visual objects . journal Psychol. Sci. volume 17 , pages 645--648

    work page 2006

  3. [3]

    , author Itti, L

    author Borji, A. , author Itti, L. , year 2013 . title State-of-the-art in visual attention modeling . journal IEEE Transactions on Pattern Analysis and Machine Intelligence volume 35 , pages 185–207 . http://ieeexplore.ieee.org/document/6180177/, :10.1109/TPAMI.2012.89

    work page arXiv 2013

  4. [4]

    , author Department of Design, Politecnico di Milano (Italy) , author Celi, M

    author Brevi, F. , author Department of Design, Politecnico di Milano (Italy) , author Celi, M. , author Gaetani, F. , author Department of Design, Politecnico di Milano (Italy) , author Department of Design, Politecnico di Milano (Italy) , year 2019 . title Creating moodboards with digital tools: A new educational approach , in: booktitle Education and N...

    work page 2019

  5. [5]

    , author Stárková, T

    author Brom, C. , author Stárková, T. , author Lukavský, J. , author Javora, O. , author Bromová, E. , year 2016 . title Eye tracking in emotional design research: What are its limitations? , in: booktitle Proceedings of the 9th Nordic Conference on Human-Computer Interaction , publisher ACM , address Gothenburg Sweden . p. pages 1–6 . https://dl.acm.org/...

    work page arXiv 2016

  6. [6]

    , author Habekost, T

    author Bundesen, C. , author Habekost, T. , author Kyllingsbæk, S. , year 2005 . title A neural theory of visual attention: Bridging cognition and neurophysiology. journal Psychological Review volume 112 , pages 291–328 . https://doi.apa.org/doi/10.1037/0033-295X.112.2.291, :10.1037/0033-295X.112.2.291

    work page doi:10.1037/0033-295x.112.2.291 2005

  7. [7]

    , author Levy, S

    author Casakin, H. , author Levy, S. , year 2023 . title Measuring behaviors for assessing design expertise: A comprehensive framework for professional development . journal Sage Open volume 13 , pages 21582440231206625 . https://journals.sagepub.com/doi/10.1177/21582440231206625, :10.1177/21582440231206625

    work page doi:10.1177/21582440231206625 2023

  8. [8]

    , author Lai, H.H

    author Chang, H.C. , author Lai, H.H. , author Chang, Y.M. , year 2006 . title Expression modes used by consumers in conveying desire for product form: A case study of a car . journal International Journal of Industrial Ergonomics volume 36 , pages 3–10 . https://linkinghub.elsevier.com/retrieve/pii/S0169814105001046, :10.1016/j.ergon.2005.06.004

    work page doi:10.1016/j.ergon.2005.06.004 2006

  9. [9]

    , author Chikwanya, E.V

    author Chipambwa, W. , author Chikwanya, E.V. , year 2022 . title Design communication: Fashion design students' perspectives on digital vs physical mood boards , in: booktitle Proceedings - The Eleventh International Symposium GRID 2022 , publisher University of Novi Sad, Faculty of technical sciences, Department of graphic engineering and design . p. pa...

    work page doi:10.24867/grid-2022-p76 2022

  10. [10]

    , author Wu, Z

    author Das, A. , author Wu, Z. , author Skrjanec, I. , author Feit, A.M. , year 2024 . title Shifting focus with hceye: Exploring the dynamics of visual highlighting and cognitive load on user attention and saliency prediction . journal Proceedings of the ACM on Human-Computer Interaction volume 8 , pages 1–18 . https://dl.acm.org/doi/10.1145/3655610, :10...

    work page doi:10.1145/3655610 2024

  11. [11]

    , year 2002

    author Duchowski, A.T. , year 2002 . title A breadth-first survey of eye-tracking applications . journal Behavior Research Methods, Instruments, & Computers volume 34 , pages 455–470 . http://link.springer.com/10.3758/BF03195475, :10.3758/BF03195475

    doi:10.3758/bf03195475 2002

  12. [12]

    , author Friesen, W.V

    author Ekman, P. , author Friesen, W.V. , author Ellsworth, P. , year 2013 . title Emotion in the human face: Guidelines for research and an integration of findings . volume volume 11 . publisher Elsevier

    work page 2013

  13. [13]

    , author Islam, G

    author Endrissat, N. , author Islam, G. , author Noppeney, C. , year 2016 . title Visual organizing: Balancing coordination and creative freedom via mood boards . journal Journal of Business Research volume 69 , pages 2353–2362 . https://linkinghub.elsevier.com/retrieve/pii/S0148296315004270, :10.1016/j.jbusres.2015.10.004

    work page doi:10.1016/j.jbusres.2015.10.004 2016

  14. [14]

    , author Marcketti, S

    author Freeman, C. , author Marcketti, S. , author Karpova, E. , year 2017 . title Creativity of images: using digital consensual assessment to evaluate mood boards . journal Fashion and Textiles volume 4 , pages 17 . https://fashionandtextiles.springeropen.com/articles/10.1186/s40691-017-0102-4, :10.1186/s40691-017-0102-4

    work page doi:10.1186/s40691-017-0102-4 2017

  15. [15]

    , author Kókai, Z

    author Gere, A. , author Kókai, Z. , author Sipos, L. , year 2017 . title Influence of mood on gazing behavior: Preliminary evidences from an eye-tracking study . journal Food Quality and Preference volume 61 , pages 1–5 . https://linkinghub.elsevier.com/retrieve/pii/S0950329317301040, :10.1016/j.foodqual.2017.05.004

    work page doi:10.1016/j.foodqual.2017.05.004 2017

  16. [16]

    , author Nuthmann, A

    author Hessels, R.S. , author Nuthmann, A. , author Nyström, M. , author Andersson, R. , author Niehorster, D.C. , author Hooge, I.T.C. , year 2024 . title The fundamentals of eye tracking part 1: The link between theory and research question . journal Behavior Research Methods volume 57 , pages 16 . https://link.springer.com/10.3758/s13428-024-02544-8, :...

    work page doi:10.3758/s13428-024-02544-8 2024

  17. [17]

    , author Koch, C

    author Itti, L. , author Koch, C. , year 2001 . title Computational modelling of visual attention . journal Nature Reviews Neuroscience volume 2 , pages 194–203 . https://www.nature.com/articles/35058500, :10.1038/35058500

    arXiv 2001

  18. [18]

    , author Proctor, R.W

    author Johnson, A. , author Proctor, R.W. , year 2004 . title Attention: theory and practice . publisher Sage Publications , address Thousand Oaks, Calif

    work page 2004

  19. [19]

    , author Sibony, O

    author Kahneman, D. , author Sibony, O. , author Sunstein, C.R. , year 2021 . title Noise: a flaw in human judgment . publisher Little, Brown Spark , address New York

    work page 2021

  20. [20]

    , author Häfner, P

    author Katicic, J. , author Häfner, P. , author Ovtcharova, J. , year 2015 . title Methodology for emotional assessment of product design by customers in virtual reality . journal Presence: Teleoperators and Virtual Environments volume 24 , pages 62–73 . https://direct.mit.edu/pvar/article/24/1/62-73/18887, :10.1162/PRES_a_00215

    work page doi:10.1162/pres_a_00215 2015

  21. [21]

    , author Gero, J.S

    author Kavakli, M. , author Gero, J.S. , year 2003 . title Strategic knowledge differences between an expert and a novice designer , in: booktitle Human Behaviour in Design . publisher Springer Berlin Heidelberg , address Berlin, Heidelberg , pp. pages 42--52

    work page 2003

  22. [22]

    , author Ryu, H

    author Kim, J. , author Ryu, H. , year 2014 . title A design thinking rationality framework: Framing and solving design problems in early concept generation . journal Human–Computer Interaction volume 29 , pages 516–553 . http://www.tandfonline.com/doi/abs/10.1080/07370024.2014.896706, :10.1080/07370024.2014.896706

    arXiv 2014

  23. [23]

    , author Taffin, N

    author Koch, J. , author Taffin, N. , author Lucero, A. , author Mackay, W.E. , year 2020 . title Semanticcollage: Enriching digital mood board design with semantic labels , in: booktitle Proceedings of the 2020 ACM Designing Interactive Systems Conference , publisher ACM , address Eindhoven Netherlands . p. pages 407–418 . https://dl.acm.org/doi/10.1145/...

    arXiv 2020

  24. [24]

    , year 2011

    author Kowler, E. , year 2011 . title Eye movements: The past 25years . journal Vision Research volume 51 , pages 1457–1483 . https://linkinghub.elsevier.com/retrieve/pii/S0042698910005924, :10.1016/j.visres.2010.12.014

    work page doi:10.1016/j.visres.2010.12.014 2011

  25. [25]

    , author Hirst, A

    author Lavie, N. , author Hirst, A. , author de Fockert, J.W. , author Viding, E. , year 2004 . title Load theory of selective attention and cognitive control . journal J. Exp. Psychol. Gen. volume 133 , pages 339--354

    work page 2004

  26. [26]

    , author Mountstephens, J

    author Lim, J.Z. , author Mountstephens, J. , author Teo, J. , year 2020 . title Emotion Recognition Using Eye-Tracking: Taxonomy, Review and Current Challenges . journal Sensors volume 20 , pages 2384 . https://www.mdpi.com/1424-8220/20/8/2384, :10.3390/s20082384

    work page doi:10.3390/s20082384 2020

  27. [27]

    , author Storer, I

    author Mcdonagh, D. , author Storer, I. , year 2004 . title Mood Boards as a Design Catalyst and Resource: Researching an Under-Researched Area . journal The Design Journal volume 7 , pages 16–31 . https://www.tandfonline.com/doi/full/10.2752/146069204789338424, :10.2752/146069204789338424

    work page doi:10.2752/146069204789338424 2004

  28. [28]

    , year 2007

    author Norman, D.A. , year 2007 . title The design of future things . publisher Basic Books , address New York

    work page 2007

  29. [29]

    , author Masner, J

    author Novak, J.S. , author Masner, J. , author Benda, P. , author Simek, P. , author Merunka, V. , year 2024 . title Eye Tracking, Usability, and User Experience: A Systematic Review . journal International Journal of Human–Computer Interaction volume 40 , pages 4484–4500 . https://www.tandfonline.com/doi/full/10.1080/10447318.2023.2221600, :10.1080/1044...

    arXiv 2024

  30. [30]

    , author Mueller Loose , S

    author Orquin, J.L. , author Mueller Loose , S. , year 2013 . title Attention and choice: A review on eye movements in decision making . journal Acta Psychologica volume 144 , pages 190–206 . https://linkinghub.elsevier.com/retrieve/pii/S0001691813001364, :10.1016/j.actpsy.2013.06.003

    doi:10.1016/j.actpsy.2013.06.003 2013

  31. [31]

    , year 2016

    author Park, H.S. , year 2016 . title A Review on the Application of Eye-tracking in Design Areas . journal Journal of the Ergonomics Society of Korea volume 35 , pages 391–401 . http://www.dbpia.co.kr/Journal/ArticleDetail/NODE07071518, :10.5143/JESK.2016.35.5.391

    work page doi:10.5143/jesk.2016.35.5.391 2016

  32. [32]

    , author van Duijn, M

    author Plaat, A. , author van Duijn, M. , author van Stein, N. , author Preuss, M. , author van der Putten, P. , author Batenburg, K.J. , year 2025 . title Agentic large language models, a survey . http://arxiv.org/abs/2503.23037 arXiv:2503.23037

    arXiv 2025

  33. [33]

    , year 1980

    author Posner, M.I. , year 1980 . title Orienting of attention . journal Quarterly journal of experimental psychology volume 32 , pages 3--25

    work page 1980

  34. [34]

    , author Merino, E.A.D

    author Reis, M.R.D. , author Merino, E.A.D. , year 2021 . title Image composition influences on the mood board visual reading process through eye-tracking . journal Product Management & Development volume 19 , pages e20210010 . http://www.pmd.igdp.org.br/article/doi/10.4322/pmd.2021.005, :10.4322/pmd.2021.005

    work page doi:10.4322/pmd.2021.005 2021

  35. [35]

    , author Bouchard, C

    author Rieuf, V. , author Bouchard, C. , author Aoussat, A. , year 2015 . title Immersive moodboards, a comparative study of industrial design inspiration material . journal J. of Design Research volume 13 , pages 78 . http://www.inderscience.com/link.php?id=67233, :10.1504/JDR.2015.067233

    work page arXiv 2015

  36. [36]

    , author Bouchard, C

    author Rieuf, V. , author Bouchard, C. , author Meyrueis, V. , author Omhover, J.F. , year 2017 . title Emotional activity in early immersive design: Sketches and moodboards in virtual reality . journal Design Studies volume 48 , pages 43–75 . https://linkinghub.elsevier.com/retrieve/pii/S0142694X16300771, :10.1016/j.destud.2016.11.001

    work page doi:10.1016/j.destud.2016.11.001 2017

  37. [37]

    author Robinson, A.C. , . title Visualizing Ideas and Opportunities through Mood Boards . https://shorturl.at/QGMxq

    work page

  38. [38]

    , author Nouchi, R

    author Saito, T. , author Nouchi, R. , author Kinjo, H. , author Kawashima, R. , year 2017 . title Gaze Bias in Preference Judgments by Younger and Older Adults . journal Frontiers in Aging Neuroscience volume 9 , pages 285 . http://journal.frontiersin.org/article/10.3389/fnagi.2017.00285/full, :10.3389/fnagi.2017.00285

    work page arXiv 2017

  39. [39]

    , author Roumeliotis, K.I

    author Sapkota, R. , author Roumeliotis, K.I. , author Karkee, M. , year 2025 . title AI agents vs. agentic AI : A conceptual taxonomy, applications and challenges . http://arxiv.org/abs/2505.10468 arXiv:2505.10468

    arXiv 2025

  40. [40]

    , year 2025

    author Schneider, J. , year 2025 . title Generative to agentic AI : Survey, conceptualization, and challenges . http://arxiv.org/abs/2504.18875 arXiv:2504.18875

    Pith/arXiv arXiv 2025

  41. [41]

    , author Simion, C

    author Shimojo, S. , author Simion, C. , author Shimojo, E. , author Scheier, C. , year 2003 . title Gaze bias both reflects and influences preference . journal Nature Neuroscience volume 6 , pages 1317–1322 . https://www.nature.com/articles/nn1150, :10.1038/nn1150

    work page doi:10.1038/nn1150 2003

  42. [42]

    , author Kaspersen, E

    author Solstad, T. , author Kaspersen, E. , author Eggen, M. , year 2025 . title Eye movements in conceptual and non-conceptual thinking . journal Educational Studies in Mathematics volume 118 , pages 323–338 . https://link.springer.com/10.1007/s10649-024-10358-w, :10.1007/s10649-024-10358-w

    work page doi:10.1007/s10649-024-10358-w 2025

  43. [43]

    , year 2021

    author Spawforth-Jones, S. , year 2021 . title Utilising Mood Boards as an Image Elicitation Tool in Qualitative Research . journal Sociological Research Online volume 26 , pages 871–888 . https://journals.sagepub.com/doi/10.1177/1360780421993486, :10.1177/1360780421993486

    work page doi:10.1177/1360780421993486 2021

  44. [44]

    , author Lee, S

    author Stappers, P. , author Lee, S. , author Harada, A. , year 2002 . title Design based on kansei , in: booktitle Pleasure With Products . publisher CRC Press , p. pages 392

    work page 2002

  45. [45]

    , author Gelade, G

    author Treisman, A.M. , author Gelade, G. , year 1980 . title A feature-integration theory of attention . journal Cognitive psychology volume 12 , pages 97--136

    work page 1980

  46. [46]

    , author Kwiatkowski, B

    author Tuszyńska-Bogucka, W. , author Kwiatkowski, B. , author Chmielewska, M. , author Dzieńkowski, M. , author Kocki, W. , author Pełka, J. , author Przesmycka, N. , author Bogucki, J. , author Galkowski, D. , year 2020 . title The effects of interior design on wellness – Eye tracking analysis in determining emotional experience of architectural space. ...

    work page 2020

  47. [47]

    , year 2021

    author Wickens, C. , year 2021 . title Attention: Theory, Principles, Models and Applications . journal International Journal of Human–Computer Interaction volume 37 , pages 403–417 . https://www.tandfonline.com/doi/full/10.1080/10447318.2021.1874741, :10.1080/10447318.2021.1874741

    arXiv 2021

  48. [48]

    , author Ounjai, K

    author Wolf, A. , author Ounjai, K. , author Takahashi, M. , author Kobayashi, S. , author Matsuda, T. , author Lauwereyns, J. , year 2018 . title Evaluative Processing of Food Images: A Conditional Role for Viewing in Preference Formation . journal Frontiers in Psychology volume 9 , pages 936 . https://www.frontiersin.org/article/10.3389/fpsyg.2018.00936...

    work page arXiv 2018

  49. [49]

    , year 2023

    author Xie, X. , year 2023 . title The cognitive process of creative design: A perspective of divergent thinking . journal Thinking Skills and Creativity volume 48 , pages 101266 . https://linkinghub.elsevier.com/retrieve/pii/S1871187123000366, :10.1016/j.tsc.2023.101266

    work page arXiv 2023

  50. [50]

    , author Campbell, R

    author Ye, J. , author Campbell, R. , author Page, T. , author Badni, K. , year 2006 . title An investigation into the implementation of virtual reality technologies in support of conceptual design . journal Design Studies volume 27 , pages 77–97 . https://linkinghub.elsevier.com/retrieve/pii/S0142694X05000347, :10.1016/j.destud.2005.06.002

    work page doi:10.1016/j.destud.2005.06.002 2006

  51. [51]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in ":" * " " * FUNCTION f...

  52. [52]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in ":" * " " * FUNCTION f...

  53. [53]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in ":" * " " * FUNCTION f...

This paper was first reviewed by deepseek-v4-flash on August 5, 2026.