Designing Semantically-Resonant Abstract Patterns for Data Visualization

Jiayi Hong; Lijie Yao; Tingying He; Tobias Isenberg; Zihan Lu

REVIEW 4 major objections 5 minor 52 references

Designing Semantically-Resonant Abstract Patterns for Data Visualization

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The paper argues that creating abstract patterns which intuitively evoke the concepts they represent reduces to a two-step recipe—first identify what a concept means to people, then encode it through pattern variables—and that…

desk verdict A useful synthesis of expert strategies for embedding semantics into abstract patterns, but the evaluation measures perceived support rather than whether the resulting patterns actually resonate, so the headline claim runs ahead of the evidence. read the letter →

arxiv 2505.14816 v2 pith:JLCTBRJQ submitted 2025-05-20 cs.HC

Zihan Lu , Tingying He , Jiayi Hong , Lijie Yao , Tobias Isenberg This is my paper · ORCID

classification cs.HC

keywords semantically-resonantpatternsdatavisualizationpatterndesignmethodologyvisualvariablesabstractnon-expertsemanticassociationworkshop

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

The paper tries to establish that designing semantically-resonant abstract patterns—black-and-white textures that intuitively evoke the data category they stand for, such as small dots for olives and large dots for tomatoes—can be reduced to a structured two-step recipe that any non-designer can follow. If true, visualization designers and the general public gain a systematic way to embed meaning into charts that must do without color, labels, or icons. The recipe was distilled from workshops in which 13 design experts produced 273 patterns for vegetable, music, and emotion concept sets, and then tested with 12 non-designers, who rated the method useful, easy to use, and helpful for both concrete and abstract concepts.

What carries the argument

The two-step design methodology itself is the central object. Step 1 (identifying content) offers four association routes grounded in conceptual metaphor theory and embodied cognition: meaning-based, human-reaction-based, feature-based, and literal-meaning-based. Step 2 (encoding) draws on a pattern design space that treats patterns as composite visual variables, distinguishing basic patterns (varying shape, size, fill, orientation, or forming an ordinal scale) from complex patterns (varying spatial arrangement and appearance relationships). The methodology works because it separates what to express from how to express it.

What would settle it

A controlled study in which naive viewers are asked to match non-expert designs made with the methodology to the intended concepts without a legend; if matching accuracy is no better than chance, the claim that the methodology effectively produces semantically-resonant patterns would be refuted. A between-group comparison of design quality with and without the methodology would serve the same purpose.

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Extended reading notes

Core claim

The central claim is that semantically-resonant pattern design decomposes into two steps: identifying the content to be visualized (via the concept's meaning, human reactions to it, its features, or its literal name), then encoding that content into an abstract pattern using the visual variables available to patterns (shape, size, fill, orientation, and spatial or appearance relationships among primitives). The authors derive this structure from qualitative coding of expert design workshops and report that non-expert participants could apply it successfully to new concept sets (ball games and personality traits).

Load-bearing premise

The evaluation treats participants' self-reported ratings of usefulness, ease of use, and helpfulness as evidence that the methodology effectively supports pattern design, without measuring whether an outside audience can match the resulting patterns to their concepts or comparing against a no-methodology control group.

Editorial extensions

If this is right

People with no design or sketching training can use the two-step methodology to generate semantically-resonant patterns for both concrete concepts and abstract ones.
The methodology condenses expert design strategies into a checklist of association routes and pattern variables, making the thinking directions used by professional designers available to anyone.
Visualization designers gain a systematic alternative to semantically-resonant colors for charts that must be black-and-white, including print, data embroidery, and small displays where legends and labels waste space.
The subjective ratings suggest the method helps most for abstract concepts, where there is no physical appearance to copy, which is exactly where designers previously had the least guidance.
Because prior work linked semantically-resonant abstract patterns to improved pie-chart reading speed, following this methodology may yield patterns that are both meaningful and faster to read.

Reading between the lines

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

The paper's own limitation section concedes that it never tests whether viewers can decode the patterns; the natural completion is a perception study measuring legend-free matching accuracy, which would verify that 'resonant' holds at the reader's end, not just the designer's.
Because every pattern was created inside an equal-slice pie chart, the methodology's behavior in thin slices, small multiples, or non-circular geometries is untested; a computational pattern library parameterized by the Step-2 variables would let designers stress-test the approach across chart types.
The finding that concrete concepts were rated harder to design for than abstract ones suggests the bottleneck is abstraction skill rather than idea generation, so tools that scaffold the sketching step (pre-built primitives or generative fills) could directly relieve the difficulty participants reported.
The ordinal-scale technique (for instance, spike count mapped to emotional negativity) hints that semantically-resonant patterns could encode ordered magnitudes, not just category identity, which would extend the claimed benefit beyond categorical charts.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

First thing to know: this paper's real contribution is the two-step methodology for designing semantically-resonant abstract patterns, derived from 13 expert workshops with 273 patterns. That is new and genuinely useful. The evaluation, however, does not test the headline claim that the methodology 'effectively supports' the general public in producing semantically-resonant patterns. It measures participants' self-reported usefulness, ease of use, and helpfulness, with no baseline and no independent check of whether the outputs evoke their concepts. The authors are transparent about this: Sec. 5 says they intentionally did not evaluate design quality, and the Discussion concedes the patterns were 'often too abstract to identify.' So the conclusion overstates what the data support.

What the paper does well: the methodology itself is a sensible, well-organized synthesis. Step 1 (identify content via meaning, human reaction, features, literal association) and Step 2 (encode using pattern variables, with basic vs. complex patterns) are clearly presented and grounded in embodied cognition and metaphor theory. The approach of deriving it from expert practice rather than from theory alone is a real strength, and the appendix with 273 expert-designed patterns is a useful resource. They also pre-registered both studies on OSF and share materials, which is more than many HCI papers do.

Soft spots, in proportion: the main one is the claim-evidence gap. Likert means around 5.3 on a 7-point scale from 12 participants are fine as subjective impressions, but they don't establish that a non-expert following the method produces patterns that viewers can match to concepts. The methodology could be a fine ideation aid and still fail at the outcome named in the title. A second, minor issue: the evaluation used an earlier version of the methodology, which the authors acknowledge and argue is structurally equivalent; that's okay but worth noting. The self-referential aspect (experts derived the method, novices rate whether it helped) is real but not disqualifying—it's inherent to qualitative tool-building research when the claim is scoped as process support.

Who should read this: visualization designers and researchers working with black-and-white patterns, accessibility, or physicalization; also anyone teaching pattern design. As a referee, I would ask the authors to either soften the central claim to 'supports the design process' or add a perception experiment with an independent audience. The methodology is worth preserving; the conclusion needs a recalibration.

Referee Report

4 major / 5 minor

Summary. The paper presents a two-step design methodology for creating 'semantically-resonant' abstract patterns—first identifying the content to be visualized, then encoding it through pattern variables. The methodology is derived from qualitative coding of 13 design experts' workshop outputs, and its usefulness, ease of use, and helpfulness are evaluated in a second workshop with 12 non-expert participants who were taught the methodology and asked to create patterns for a concrete and an abstract concept set. The abstract and conclusion claim that the methodology 'effectively supports the general public in designing semantically-resonant abstract patterns for both abstract and concrete concepts.' The evaluation evidence, however, consists of self-reported Likert ratings and interview quotes about the design process, not of any independent assessment of whether the resulting patterns actually evoke their intended concepts.

Significance. If the central claim were supported, the paper would contribute a useful practical framework to an under-studied area, extending prior work on semantically-resonant colors to abstract patterns and providing an accessible entry point for non-experts. The paper has genuine strengths: the methodology is systematically derived from a substantial corpus of expert designs and is presented with extensive appendix material; the studies were pre-registered and the materials and data are shared in an OSF repository; and the authors are transparent about many limitations in Sec. 7 and the Discussion. The value of the methodological taxonomy itself is plausible. What is not supported by the reported evidence is the outcome claim that the process yields semantically-resonant patterns, because the evaluation measures perceived process support rather than the semantic discriminability of the generated patterns.

major comments (4)

[Abstract and Sec. 5] The central claim that the methodology 'effectively supports the general public in designing semantically-resonant abstract patterns' is not tested by the evidence in Sec. 5. The stated evaluation goal, quoted in Sec. 5, is 'to assess how our design methodology supports participants in the design process, and intentionally not to evaluate the quality of their specific designs.' The outcome measures in Sec. 5.5.1 are Likert ratings of usefulness, ease of use, and helpfulness, together with qualitative self-reports. These are process-oriented and self-referential: participants were taught the methodology and then asked whether it helped. No independent viewer was asked to match the generated patterns to their intended concepts, and no baseline condition without the methodology was included. Since the paper itself defines semantic resonance in Sec. 2.2 via Schloss et al.'s notion of semantic discriminability, which is about a viewer inferring the mapping from the visual features alone, the evaluation should have measured that inference or made clear that the effectiveness claim is only about perceived process support.
[Sec. 6 and Sec. 7] The paper's own discussion undermines the strong conclusion. Sec. 6 acknowledges that participants' semantically-resonant pattern designs 'were often too abstract to identify the relevant items, in particular in the context of abstract concept sets.' Sec. 7 states that the authors intentionally 'do not assess the impact of using semantically-resonant patterns in visualizations' and calls for future crowd-sourced perception experiments. These statements directly conflict with the abstract's claim that the methodology was shown to 'effectively support' the design of semantically-resonant patterns. At minimum, the claims should be scaled back to say that the methodology was perceived as useful, easy to use, and helpful for ideation, and that its effect on the resonance of the resulting patterns remains to be tested.
[Sec. 5.5.1 and Sec. 5.6] The interpretation of the quantitative ratings is strained given the small sample and the lack of any comparison condition. The Likert means reported in Sec. 5.5.1 (usefulness M = 5.29, ease of use M = 5.33, helpfulness M = 5.33 on a 7-point scale) are moderate, and the concrete usefulness mean is 4.92, which is close to the scale midpoint. The claim that 'our design methodology thus appears to be meaningful in reducing the challenges users face in design' goes beyond what can be concluded from absolute Likert scores without a baseline. This is particularly important because Sec. 5.6 reports that all participants confirmed they used the methodology and attributed their designs to it; in a study where the methodology was just taught to the participants, such self-reports are partly an artifact of the instructional context rather than independent evidence of the methodology's value.
[Sec. 6, 'Methodology Evaluation' paragraph] The authors provide a thoughtful justification for not running a control condition, citing learning effects and between-subject variability. This is a reasonable position for a qualitative study, but it means the paper cannot support the causal or effectiveness language used in the conclusion. The conclusion should be limited to 'participants found the methodology helpful in generating and structuring their design ideas,' not 'the methodology effectively supports the design of semantically-resonant patterns.' A controlled perception study or a comparison of designs produced with and without the methodology would be needed to substantiate the stronger claim.

minor comments (5)

[Supplemental Material Pointers] The word 'seperately' should be corrected to 'separately.'
[Sec. 5.5.1] A quote attributed to P12 is followed by '(P2 for abstract),' which appears to be an incorrect participant label; please fix this inconsistency.
[Table 3] The table lists 'hard working' as two words, whereas the text and concept selection in Sec. 5.1 use 'hardworking'; please standardize the spelling.
[Sec. 5.5.1] The sentence reporting 'a trend that participants think our design methodology is more useful for abstract concept sets than concrete concept sets' is based on overlapping standard deviations in a sample of 12; phrasing this as a hypothesis or descriptive pattern rather than a trend would be more appropriate.
[Fig. 7] The caption for Fig. 7 states that the plot shows ratings on the difficulty of designing patterns, but the caption does not clearly explain what the plotted percentages represent; please clarify whether they are percentages of participants rating each score or something else.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation; the evaluation's construct-validity gap is a limitation, not a circularity.

full rationale

The derivation chain is empirical rather than formal. The methodology was produced by qualitative coding of expert workshops (Sec. 3.6), and the evaluation used separate non-expert workshops with new concept sets (Sec. 5). No parameter is fitted from the evaluation data and then reported as a prediction, and no load-bearing claim reduces to a self-citation chain. The paper does state in Sec. 5 that the evaluation goal 'is to assess how our design methodology supports participants in the design process, and intentionally not to evaluate the quality of their specific designs,' and Sec. 6 concedes that participant designs 'were often too abstract to identify the relevant items'; these are genuine threats to the breadth of the central effectiveness claim and should be treated as correctness and validity concerns rather than circularity. Similarly, Sec. 7's statement that 'We do not assess, however, the impact of using semantically-resonant patterns in visualizations' is an acknowledged scope limitation. The final methodology was refined after the evaluation (Secs. 4.3 and 7), which weakens the independence of the validation of the presented version, but the paper transparently says the evaluated version already contained all components and that the changes were structural and terminological. That is an iterative-design limitation, not an identity between input and output. Citations to He et al. are published, externally reviewed empirical evidence used as background and motivation, not an unverified self-citation that forces the conclusion. Therefore no circular step meeting the required standard is present.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

This is a qualitative design study; there are no fitted parameters or invented entities. The assumptions are the theoretical frameworks and the evaluation's implicit assumption that self-report measures effectiveness.

assumptions (5)

domain assumption Semantically-resonant color mappings improve chart reading speed (Lin et al. [20]).
Motivates the focus on semantically-resonant patterns; assumed true, cited from prior work.
domain assumption Embodied cognition theory accounts for how people understand concrete and abstract concepts.
Used to justify the categorization of concept types in Sec 2.1.
domain assumption Conceptual metaphor theory explains how abstract concepts are linked to concrete ones.
Foundation for Step 1 approaches in Sec 4.1.
domain assumption Patterns can be treated as a composite visual variable with attributes from He [12].
Frames Step 2 visual variables in Sec 4.2.
ad hoc to paper Self-reported ratings by participants are a valid measure of the methodology's effectiveness.
The evaluation in Sec 5 uses Likert ratings and interviews as the sole outcome, without an objective measure of pattern quality or a control group.

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Cite this review

Pith. "Pith review of Designing Semantically-Resonant Abstract Patterns for Data Visualization." pith.science (2026). https://pith.science/paper/JLCTBRJQ

@misc{pith2026250514816,
  author       = {Pith},
  title        = {Pith review of: Designing Semantically-Resonant Abstract Patterns for Data Visualization},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JLCTBRJQ}},
  note         = {Machine review of arXiv:2505.14816}
}

read the original abstract

We present a structured design methodology for creating semantically-resonant abstract patterns, making the pattern design process accessible to the general public. Semantically-resonant patterns are those that intuitively evoke the concept they represent within a specific set (e.g., in a vegetable concept set, small dots for olives and large dots for tomatoes), analogous to the concept of semantically-resonant colors (e.g., using olive green for olives and red for tomatoes). Previous research has shown that semantically-resonant colors can improve chart reading speed, and designers have made attempts to integrate semantic cues into abstract pattern designs. However, a systematic framework for developing such patterns was lacking. To bridge this gap, we conducted a series of workshops with design experts, resulting in a design methodology that summarizes the methodology for designing semantically-resonant abstract patterns. We evaluated our design methodology through another series of workshops with non-design participants. The results indicate that our proposed design methodology effectively supports the general public in designing semantically-resonant abstract patterns for both abstract and concrete concepts.

Figures

Figures reproduced from arXiv: 2505.14816 by the authors.

**Figure 2.** Overview of our design methodology. The design methodology categorize the methodology into two steps, with each step categorizing potential approaches for designing semantically-resonant patterns. that the variation in a shape should be limited to abstract geometric forms, with no iconic shapes. Next, we gave participants 5 minutes to practice—asking them to design two semantically-resonant patterns, one for “sunny”… view at source ↗

**Figure 3.** Frequencies of use of each visual variable in our design workshop [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (51 more)

**Figure 4.** Figure 4: Examples of pattern design from our design workshop: (left) [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

**Figure 5.** Figure 5: Rating of how useful, easy to use, and helpful our design methodology is, for the concrete concept set. The percentages on the left represent negative scores (1–3), and the percentages on the right represent positive scores (5–7). 8.3% 83.3% 8.3% 8.3% 91.7% 75.0% Hel…

**Figure 6.** Figure 6: Rating of how useful, easy to use, and helpful our design methodology is, for the abstract concept set. The percentages on the left represent negative scores (1–3), and the percentages on the right represent positive scores (5–7). level of “intermediate” or above in …

**Figure 7.** Figure 7: Rating on the difficulty of designing semantically-resonant abstract [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]

**Figure 9.** Figure 9: Screenshot of the real-world visualization example for the music genre concept set. Link: www.statista.com/chart/15763/most -popular-music-genres-worldwide; website accessed: Feburary 2025. and circular; black olives are dark; ripe tomatoes are a deep red; carrots, ce…

**Figure 10.** Figure 10: Screenshot of the real-world visualization example for the emotion concept set. Link: support.lifecraft.com/hc/en-us/articles/ 4404785101079-Emotion-Charts; website accessed: Feburary 2025. 11.4% 8.6% 11.4% 8.6% 8.6% 5.7% 11.4% 5.7% 11.4% 5.7% 11.4% Correctness Effic…

**Figure 11.** Figure 11: Real-world visualization example for the quality metric concept set. This is our own re-creation of a pie chart based on the data shown in [PITH_FULL_IMAGE:figures/full_fig_p012_11.png]

**Figure 14.** Figure 14: Design methodology, version 2. separate approach in Step 1. We also add a “Ordinal scale” approach in Step 2 for this idea of creating an ordinal scale. 3. The visual variables used in Step 2 remained unchanged from v1 and v2. However, upon reviewing the pattern desig…

**Figure 13.** Figure 13: Design methodology, version 1. 1. ) We refined the two step names as “Step 1: Identifying the content to be visualized” and “Step 2: Visualizing the refined concept as a pattern.” Because these new names can better and precisely describe what design experts exactly di…

**Figure 15.** Figure 15: A semantically-resonant pattern design (Ex1) for vegetable [PITH_FULL_IMAGE:figures/full_fig_p014_15.png]

**Figure 17.** Figure 17: A semantically-resonant pattern design (Ex3) for vegetable [PITH_FULL_IMAGE:figures/full_fig_p014_17.png]

**Figure 18.** Figure 18: A semantically-resonant pattern design (Ex4) for vegetable [PITH_FULL_IMAGE:figures/full_fig_p014_18.png]

**Figure 19.** Figure 19: A semantically-resonant pattern design (Ex5) for vegetable [PITH_FULL_IMAGE:figures/full_fig_p015_19.png]

**Figure 20.** Figure 20: A semantically-resonant pattern design (Ex6) for vegetable [PITH_FULL_IMAGE:figures/full_fig_p015_20.png]

**Figure 21.** Figure 21: A semantically-resonant pattern design (Ex7) for vegetable [PITH_FULL_IMAGE:figures/full_fig_p015_21.png]

**Figure 25.** Figure 25: A semantically-resonant pattern design (Ex11) for vegetable [PITH_FULL_IMAGE:figures/full_fig_p016_25.png]

**Figure 26.** Figure 26: A semantically-resonant pattern design (Ex12) for vegetable [PITH_FULL_IMAGE:figures/full_fig_p016_26.png]

**Figure 27.** Figure 27: A semantically-resonant pattern design (Ex13) for vegetable [PITH_FULL_IMAGE:figures/full_fig_p016_27.png]

**Figure 31.** Figure 31: A semantically-resonant pattern design (Ex4) for music concept [PITH_FULL_IMAGE:figures/full_fig_p017_31.png]

**Figure 32.** Figure 32: A semantically-resonant pattern design (Ex5) for music concept [PITH_FULL_IMAGE:figures/full_fig_p017_32.png]

**Figure 33.** Figure 33: A semantically-resonant pattern design (Ex6) for music concept [PITH_FULL_IMAGE:figures/full_fig_p017_33.png]

**Figure 37.** Figure 37: A semantically-resonant pattern design (Ex10) for music concept [PITH_FULL_IMAGE:figures/full_fig_p018_37.png]

**Figure 38.** Figure 38: A semantically-resonant pattern design (Ex11) for music concept [PITH_FULL_IMAGE:figures/full_fig_p018_38.png]

**Figure 39.** Figure 39: A semantically-resonant pattern design (Ex12) for music concept [PITH_FULL_IMAGE:figures/full_fig_p018_39.png]

**Figure 43.** Figure 43: A semantically-resonant pattern design (Ex3) for emotion con [PITH_FULL_IMAGE:figures/full_fig_p019_43.png]

**Figure 44.** Figure 44: A semantically-resonant pattern design (Ex4) for emotion con [PITH_FULL_IMAGE:figures/full_fig_p019_44.png]

**Figure 45.** Figure 45: A semantically-resonant pattern design (Ex5) for emotion con [PITH_FULL_IMAGE:figures/full_fig_p019_45.png]

**Figure 49.** Figure 49: A semantically-resonant pattern design (Ex9) for emotion con [PITH_FULL_IMAGE:figures/full_fig_p020_49.png]

**Figure 50.** Figure 50: A semantically-resonant pattern design (Ex10) for emotion [PITH_FULL_IMAGE:figures/full_fig_p020_50.png]

**Figure 51.** Figure 51: A semantically-resonant pattern design (Ex11) for emotion [PITH_FULL_IMAGE:figures/full_fig_p020_51.png]

**Figure 55.** Figure 55: A semantically-resonant pattern design by the author (Au2) for [PITH_FULL_IMAGE:figures/full_fig_p021_55.png]

**Figure 56.** Figure 56: A semantically-resonant pattern design by the author (Au3) for [PITH_FULL_IMAGE:figures/full_fig_p021_56.png]

**Figure 57.** Figure 57: A semantically-resonant pattern design by the author (Au4) for [PITH_FULL_IMAGE:figures/full_fig_p021_57.png]

**Figure 64.** Figure 64: A semantically-resonant pattern design by the non-expert par [PITH_FULL_IMAGE:figures/full_fig_p022_64.png]

**Figure 62.** Figure 62: A semantically-resonant pattern design by the non-expert par [PITH_FULL_IMAGE:figures/full_fig_p022_62.png]

**Figure 63.** Figure 63: A semantically-resonant pattern design by the non-expert par [PITH_FULL_IMAGE:figures/full_fig_p022_63.png]

**Figure 67.** Figure 67: A semantically-resonant pattern design by the non-expert par [PITH_FULL_IMAGE:figures/full_fig_p023_67.png]

**Figure 68.** Figure 68: A semantically-resonant pattern design by the non-expert par [PITH_FULL_IMAGE:figures/full_fig_p023_68.png]

**Figure 69.** Figure 69: A semantically-resonant pattern design by the non-expert par [PITH_FULL_IMAGE:figures/full_fig_p023_69.png]

**Figure 73.** Figure 73: A semantically-resonant pattern design by the non-expert par [PITH_FULL_IMAGE:figures/full_fig_p024_73.png]

**Figure 74.** Figure 74: A semantically-resonant pattern design by the non-expert par [PITH_FULL_IMAGE:figures/full_fig_p024_74.png]

**Figure 75.** Figure 75: A semantically-resonant pattern design by the non-expert par [PITH_FULL_IMAGE:figures/full_fig_p024_75.png]

**Figure 79.** Figure 79: A semantically-resonant pattern design by the non-expert par [PITH_FULL_IMAGE:figures/full_fig_p025_79.png]

**Figure 80.** Figure 80: A semantically-resonant pattern design by the non-expert par [PITH_FULL_IMAGE:figures/full_fig_p025_80.png]

**Figure 81.** Figure 81: A semantically-resonant pattern design by the non-expert par [PITH_FULL_IMAGE:figures/full_fig_p025_81.png]

**Figure 88.** Figure 88: A semantically-resonant pattern design by the non-expert par [PITH_FULL_IMAGE:figures/full_fig_p026_88.png]

**Figure 86.** Figure 86: A semantically-resonant pattern design by the non-expert par [PITH_FULL_IMAGE:figures/full_fig_p026_86.png]

**Figure 87.** Figure 87: A semantically-resonant pattern design by the non-expert par [PITH_FULL_IMAGE:figures/full_fig_p026_87.png]

**Figure 91.** Figure 91: A semantically-resonant pattern design by the non-expert par [PITH_FULL_IMAGE:figures/full_fig_p027_91.png]

**Figure 92.** Figure 92: A semantically-resonant pattern design by the non-expert par [PITH_FULL_IMAGE:figures/full_fig_p027_92.png]

**Figure 93.** Figure 93: A semantically-resonant pattern design by the non-expert par [PITH_FULL_IMAGE:figures/full_fig_p027_93.png]

Discussion (0). Continue with ORCID to comment.

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Conceptual bridging: Linking the target concept to a more concrete one using: Psychological and physiological reac- tions/Personal experiences/Conceptual imagery/Common knowl- edge and cultural background/Literal associations
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Attribute extraction: Identifying features of the target concept, categorized into: Intrinsic properties of the concept/Personal feelings to the concept
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Make the concept more concrete

Quantification: Unlike the previous two methods, which focus on designing for individual target concepts, this method treats the entire concept set as a whole. The designer transforms the categorical value set into an orderable value set. Since all concepts within the set shar...
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conceptual bridging

Link to a more concrete concept: Similar to “conceptual bridging” in v1, but with revised the categories what this link can be based on. The new categories under this method are as following: “re- actions”(refined from “psychological and physiological reactions” in v1, corresp...
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intrinsic properties

Extract an attribute of the target concept. Same as attribute ex- traction method in v1. Within this method, “intrinsic properties” corresponding to “Step 1: Features” in v3, “personal feelings” corresponding to “Step 1: Human reaction → Emotional reaction” in v3
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Step 2: Basic patterns → Ordinal scale

Compare the target concept to other concepts in the concept set. Same as the quantification method in v1. Corresponding to “Step 2: Basic patterns → Ordinal scale” in v3. We explain why we moved this method from Step 1 to Step 2 in next subsection. Step 2. In v2, for Step 2, w...
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Step 1: Identifying the content to be visualized

) We refined the two step names as “Step 1: Identifying the content to be visualized” and “Step 2: Visualizing the refined concept as a pattern.” Because these new names can better and precisely describe what design experts exactly did during each step
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concept bridging

We reorganized Step 1. (a) Since all specific approaches under Step 1 involve concep- tual bridging or linking, we no longer use “concept bridging” or “link the target concept to a more concrete concept” as a specific approach name, as was done in v1 and v2. Instead, we interp...
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complex patterns

The visual variables used in Step 2 remained unchanged from v1 and v2. However, upon reviewing the pattern designs again, we observed that it is important to point out that some patterns have more basic look, resembled those more commonly found in cur- rent charts (characteriz...

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

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