A literature map for a review that reads data visualization as an operation that changes the angle of interpretation

Purpose of this note

The hypothesis is that “data visualization is one of the operations for exploring the interpretation of data from new angles.” To write a review paper on this hypothesis, we collected literature in four domains on 2026-10-07 and assembled material on how the shelves (categories) of existing reviews could be restructured.

From the collection stage onward, we applied the novelty conditions of paper-pipeline (§0.1 and §2 of fdn-novelty-axes). A review paper is assessed on five axes, namely PD, Mat, Met, and Res plus RW (structural transformation of the literature map), with RW and PD at the center. For RW to reach 3 or higher, the classification of or relations among the same or overlapping literature must actually change relative to the nearest review, and that change must make new comparisons, explanations, or judgments possible. When no single review covers the same scope, Route B, which uses the nearest review in each constituent domain, is taken, and all eight conditions must be met. If the review only adds literature, rearranges it, renames it, or places different fields side by side, RW does not exceed 2. Being “the first map” earns no points either.

The following three items are fixed before the audit.

• Type of paper: an integrative review. It uses existing studies as material, and its main contribution is the restructuring of relations among the literature.
• Integrating axis: visualization operations are arranged by what they change. Some operations lighten computation while keeping the question fixed, and others find facts or hypotheses within a frame. Still others restructure the frame itself (what is seen as a unit, what is compared with what, and what is asked), or lock the frame into a single reading.
• Constituent domains: A, exploratory data analysis and visual analytics (sensemaking, insight, provenance); B, the cognitive science of external representations (representational change, insight problem solving); C, design theory (framing, ambiguity, fixation, creativity support); and D, the humanities and critical visualization (interpretive visualization, data art, Data Humanism).

What the “operation that changes the angle” must be distinguished from

The cognitive science of external representations has long shown that changing a representation changes how easy it is to think. Larkin and Simon (1987) stated that two representations that are informationally equivalent can still differ in computational efficiency, and Zhang and Norman (1994) called the phenomenon in which isomorphic representations with the same formal structure produce very different cognitive behavior the representational effect. Scaife and Rogers (1996) organized the functions of external diagrams into three: computational offloading, re-representation, and graphical constraining. Kirsh (2010) listed seven ways in which external representations enhance cognition and described some of them as ones that “change the domain and range of cognition.” Tversky (2011) listed, among the cognitive functions of diagrams, reminding, focusing thought, reorganizing thought, and exploring thought.

Therefore, the claim that “changing a representation changes the way people think” does not by itself make the hypothesis new. This was also the first point raised at the stage of collecting the literature in Domain B. The remaining difference lies in redrawing the shelves of the literature by separating the functions of visualization into those that “solve the same question faster” and those that “restructure the question or the frame.”

This distinction becomes concrete through a contrast between two studies that we were able to check in the full text. Larkin and Simon’s analysis deals with which representation reduces the effort of inference while the question remains fixed. Knoblich et al. (1999) argued that impasses are broken by changing the representation of the problem, and they tested the mechanism, as constraint relaxation and chunk decomposition, in four experiments with matchstick arithmetic. Suwa et al. (2001) confirmed, using protocols of expert architects, that reorganizing parts of a sketch into new units (regrouping) drove the discovery of new features, and they reported that novices who adopted this strategy produced more interpretations from ambiguous sketches. The former study deals with the speed of reaching an answer, and the latter two deal with changes in what is seen as a unit. In the planned paper, the “operation that changes the angle” refers to the latter.

How we searched, and the limits of the search

The collection was conducted on 2026-10-07 in four parallel lanes. The routes used were OpenAlex (title.search and title_and_abstract.search), Crossref, the arXiv API, ERIC, Europe PMC, Semantic Scholar (DOI lookups and one retrieval of citing works), Unpaywall, and general web searches. The search strings, result counts, and inclusion and exclusion criteria are recorded in full in the corpus.

Inclusion was limited to sources that address the subject of each domain, are peer-reviewed papers, identifiable preprints, or books, and whose bibliographic records could be confirmed through a DOI, the publisher, or arXiv. Excluded were retracted papers, predatory journals, surveys of data analysis by LLM agents (outside the scope of the present question), purely technical tool papers that do not address interpretation, and field-specific educational studies.

Backward searches were conducted from the reference lists of each nearest review. Forward searching consisted of a single retrieval of 100 works citing Scaife and Rogers (1996) through Semantic Scholar. OpenAlex returned HTTP 429 partway through (with a retry wait of about 13 hours), and we did not use the search interfaces of the ACM DL, IEEE Xplore, or Google Scholar. Condition 2 of Route B (the search record) is therefore in a state in which a record exists but the range of the search is insufficient.

The numbers collected were 35 for A, 28 for B, 35 for C (excluding 5 already covered), and 34 for D; after removing duplicates, this note lists 118 references. We obtained the full text for 67 of them, the abstract only for 35, and nothing beyond the bibliographic record for 16 (fulltext-manifest.json; because two URLs each are listed for Lam et al. 2012 and Scaife and Rogers 1996, the manifest has 120 rows). In 4 cases, automatic retrieval picked up a different paper with a similar title (Jansson and Smith, Kaplan and Simon, Voyager 2, and Matejka and Fitzmaurice); we checked all retrieved texts for the title at the beginning and either removed these texts or replaced them with the correct version.

The nearest reviews in each domain and their shelves

Within the range we could read, we found no existing review in any domain that classifies the functions of visualization along the axis of “frame restructuring.” We use this observation only as a reason for choosing Route B, not as grounds for a score.

The shelves of each domain contain statements that serve as clues to the axis of the hypothesis. Battle and Heer define EVA as finding questions of interest, examining visualized data, and iteratively refining questions and hypotheses. Battle and Ottley (2024) divide insight into knowledge-building insight, which extends existing knowledge structures, and spontaneous insight, which reorganizes loosely connected knowledge, and then formalize only the former. Sacha et al. state that insight differs from a mere finding in that it carries an interpretation in the problem domain. Fox (2020) writes that the structure of the activities that designers of representations intend users to perform has not been classified. Windhager et al. acknowledge that the first half of their own classification, which centers on data, users, and tasks, may appear to be a questionable choice for analyzing interfaces for humanities experiences. They also state that the imprecision and interpretive openness found in the humanities literature have rarely been considered in the design of cultural heritage interfaces and visualizations. Ma et al. define creativity in visualization through divergent, convergent, and collaborative thinking and activities, and the term “reframe” did not appear in the part we could read (because we read the latter half only in summary form, we do not conclude that the term is absent).

How the shelves change when rearranged along the axis of the hypothesis

From the integrating axis, we provisionally set the following five new shelves.

• Taking over computation: reducing the effort of reading and inference while both the question and the frame remain fixed.
• Discovery within a frame: finding facts or hypotheses while the question is open but the frame of what to compare remains fixed.
• Frame restructuring: changing what is seen as a unit, what is compared with what, or what is asked.
• Frame locking: the representation binds the reading to a single one, making other angles harder to see.
• Recording and auditing paths: recording which frames were passed through to reach a conclusion, so that the path can be checked later.

The correspondence with the existing shelves is as follows (details and grounds are in the mapping table of the corpus).

The following splits and joins are candidates that cross two or more domains. Each is written in the form required by the novelty conditions: “splitting x and y, which were on the same shelf in review A, and joining x and z, which were on different shelves in review B, makes judgment J possible.”

1. Splitting representational change into two (B, C, and D): Larkin and Simon (computational efficiency for the same question) and Knoblich et al. (representational change that breaks an impasse), which sit side by side on the re-representation shelf of Scaife and Rogers, are split. The latter is joined to the regrouping of Suwa et al. (C), which is not on the shelves of Ma et al., and to the aesthetic provocation (D) that Hinrichs et al. (2019) list among the roles of visualization. The candidate judgment is that the claim “evaluations that measure effects by speed and accuracy on the same question do not measure frame restructuring” can be shown at the level of which study measured what.
2. Joining exploratory recommendation to fixation (A and C): Voyager (which supported exploration of unfamiliar data and broadened the range of variables examined) and Zgraggen et al. (according to the abstract, more than 60% of users’ insights were false on synthetic data with known ground truth), which sit side by side on the exploration shelf of Battle and Heer, are split according to whether “the frame is restructured or comparisons are added within the frame.” The Voyager paper cites as its motivation that analysts may narrow their exploration before examining variables one by one, because of premature fixation on specific questions or the effort of manual specification. This motivation is joined to fixation research in C (the manipulated variables of Vasconcelos and Crilly). The candidate judgment is that the manipulated variables of fixation research (for example, the number of examples, their diversity, and the timing of presentation) can be transferred to the evaluation of visualization recommendation.
3. Splitting constraint into two (B, A, and D): graphical constraining in Scaife and Rogers (elements of a diagram guide inference) and constraint relaxation in Knoblich et al. (relaxing constraints produces insight) use the same word, “constraint,” for functions in opposite directions. Part of the former is joined to anchoring (A), such as that reported by Calero Valdez et al. (2018; judgments of how classes in the same scatterplot separate change depending on the scatterplot viewed immediately before), and to the function, discussed by Kennedy et al. (2016), by which visualization conventions make data appear objective and factual (D). The candidate judgment is that a single table can treat the same encoding constraint as one that supports inference in some tasks and closes the reading to a single one in other situations.
4. Joining through the division between exploration and confirmation (D and A): Underwood (2017; the practice of posing historical questions as experiments in which hypotheses and samples are defined before conclusions) and Hinrichs et al. (2016; visualization supports speculative thinking, hypothesis testing, and preliminary interpretation), which sit side by side on the distant reading shelf, are split. The former is joined to Hullman and Gelman (2021; a design philosophy that values exploration risks mistaking the need for flexibility for the conclusion that “exploratory visual analysis has no model”) and to Pu and Kay (2018; the problem of forking paths, in which analytic flexibility leads to unreliable conclusions) in A. The candidate judgment is that the distant reading debate in the humanities and the forking paths debate in visual analytics can be compared as the same question of where to draw the line between exploration and confirmation.
5. Rearranging by the point at which the frame enters (A and D): on the sensemaking shelf, Pirolli and Card (2005) place the stage of re-representing information as schemas in the middle of the process, whereas Berret and Munzner (2025) regard data as the visible tip of a submerged schema and state that overlooking the prior existence of the schema creates the illusion that data exist apart from people. The latter is joined to Drucker (2011; data are not given but taken and constructed, that is, capta) in D. The candidate judgment is that, when visualization is viewed as frame restructuring, models of sensemaking can be rearranged according to when, and by whom, the frame to be restructured enters.

Under the RW ceiling rule (adding, rearranging, or renaming literature, or placing different fields side by side, does not raise RW above 2), candidates 1 and 3 are the strongest, because the classification of the same body of literature (the studies of representation that Scaife and Rogers placed side by side) actually changes. For candidates 2, 4, and 5, unless the relation with the joined partners can be argued in the full text, the result remains a juxtaposition of different fields, and RW is held to 2.

Prospects for the removal and substitution tests

The removal test examines which of the named judgments no longer hold when one domain is removed.

• Removing A eliminates the evaluation of exploration (the range of variables examined, false insights, provenance), and candidates 2 and 4 no longer hold.
• Removing B eliminates the explanation of the mechanism by which changing a representation changes what is seen, and candidates 1 and 3 no longer hold.
• Removing C eliminates the vocabulary and manipulated variables of fixation and framing, and one side of candidates 1 and 2 no longer holds.
• Removing D eliminates the critique of conventions that close interpretation to a single reading and the design practices that open interpretation, and candidates 3, 4, and 5 no longer hold.

The substitution test examines whether the same argument holds when a domain is replaced with an unrelated one. For example, replacing D with research on educational assessment makes the comparison in candidate 4 (“the division between exploration and confirmation”) impossible. Replacing C with research on musical improvisation loses the concrete manipulated variables of fixation, and candidate 2 can no longer be stated. Both are prospects, and confirmation is left to the audit on the paper-pipeline side.

A one-sentence statement of the newly enabled judgment

The candidate is the following sentence. “Dividing the functions of visualization into taking over computation, discovery within a frame, frame restructuring, frame locking, and recording and auditing paths makes it possible to explain, as differences in the type of operation, why separate fields have produced opposite answers to the question ‘is visualization useful?’ (recommendation broadens the range examined but also increases false insights; encoding constraints support inference but close the reading).” This sentence is the basis for RW and is counted separately from the bases for Mat, Met, and PD.

The nearest work in three directions

The core proposition is that “visualization can function as an operation that restructures the frame of data interpretation.” The works whose vocabulary can state this proposition as it is are, in order of strength, Kirsh (2010; changing the domain and range of cognition), Scaife and Rogers (1996; re-representation), Tversky (2011; reorganizing and exploring thought), and Hinrichs et al. (2019). As a claim about the phenomenon, the proposition is almost not new compared with these four. The novelty of the paper must be placed not in a claim about the phenomenon but in the restructuring of the literature’s shelves.

• Direction of the question: Hinrichs et al. 2019 regard visualization as itself a research process and propose three roles, including aesthetic provocation, which elicits critical insight, interpretation, speculation, and argument. They discuss the same question through cases of collaboration in the humanities and do not build a literature map, which is where they differ. Kleymann and Stange 2021 define hermeneutic visualization, which produces visual interpretations usable as arguments by manipulating, rearranging, and exploring visual representations of annotations. Their subject is limited to literary studies, which is where they differ.
• Direction of the material: Battle and Ottley 2024 organized the literature on definitions of insight, and Ma et al. 2025 organized 104 papers on creativity in visualization. Both overlap with the literature of the planned paper, but neither has the axis of frame restructuring.
• Direction of the method: Vasconcelos and Crilly 2016 rearranged 25 fixation experiments by their manipulated variables rather than by their results. The method of rearranging literature along an axis is the same, but the domain differs. Saharan et al. 2026 read five visualization textbooks and extracted the values of universality, objectivity, and efficiency. They are close in questioning the assumptions of visualization research, but their work is not a restructuring of a literature map.

Literature that runs against the hypothesis

Literature that runs against the hypothesis, or that sets conditions on it, is needed to determine the conditions under which frame restructuring produces errors.

• Micallef et al. (2012) found no measurable benefit from adding diagrams to Bayesian reasoning problems and could not replicate the results of earlier studies.
• Zhang and Wang (2009) report that, when external and internal information do not correspond well, external representations instead hinder task performance.
• Fansher et al. (2022) report that, in the Tower of Hanoi, external representations promote speed whereas internal representations promote accuracy.
• Xiong et al. (2020) show that, for the same data, the perception of causality is strong with text and bar charts and weak with scatterplots, and they conclude that different visualization designs give different inferential affordances for the same data. This result also supports the hypothesis.
• Calero Valdez et al. (2018), Cho et al. (2017), and Wesslen et al. (2019) show anchoring and priming in the use of visualization.
• Wadinambiarachchi et al. (2024) report that using AI image generation in ideation strengthened fixation on the initial example and reduced the number, diversity, and originality of ideas (a between-subjects experiment with 60 participants).
• Vasconcelos and Crilly (2016) state that fixation has consistently been treated as an undesirable phenomenon, and Crilly and Cardoso (2017) list whether fixation is always bad as one of their questions.
• Underwood (2017) and Hullman and Gelman (2021) discuss, from the humanities and from statistics respectively, the risk of treating the results of exploration as confirmation.

Based on these studies, the scope of the hypothesis must be narrowed as follows. Visualization can function as an operation that restructures the frame, but the same operation can also work in the direction of locking the frame, and interpretations obtained through restructuring can become false discoveries unless they are checked by a separate procedure.

Current status against the novelty conditions

The current status against the eight conditions of Route B is as follows.

PD points are difficult to award to the claim about the phenomenon (changing a representation changes the way people think), because this claim can be stated directly in the vocabulary of Kirsh, Scaife and Rogers, and Tversky. If PD is to be established, it would have to rest on the framing of the question that “the yardsticks for evaluating visualization do not measure frame restructuring.”

Next tasks

In a later audit in the paper-writing process (2026-11), none of the proposals met the novelty conditions (the best reached PD 2; the question about the yardsticks for evaluating visualization was judged to have already been raised in research on visualization evaluation).

• Obtain the full texts of Zhang 1997, Ainsworth 2006, Franconeri et al. 2021, Jänicke et al. 2015, Frich et al. 2019, Dorst 2011, Dorst and Cross 2001, Paton and Dorst 2011, Klein et al. 2006, Kaplan and Simon 1990, Jansson and Smith 1991, Stacey and Eckert 2003, Schön and Wiggins 1992, and Gaver et al. 2003.
• Add forward searches: works citing Scaife and Rogers 1996, Hinrichs et al. 2019, Battle and Ottley 2024, and Ma et al. 2025 (after the OpenAlex limit is lifted).
• Run the same search strings in the ACM DL and IEEE Xplore and record the result counts.
• Create the table of overlap between the nearest reviews and the corpus (condition 4).
• Hand the material over to paper-pipeline, create reading notes on the nearest reviews, and then pass the audit.

The core of Domain C (Rittel and Webber, Schön, Dorst and Cross, and Dorst’s frame creation) is summarized in リフレーミング探索としてのデザイン：問題設定と枠組みの組み替えをめぐる文献地図private, which was built from the literature of paper-pipeline, and the paper can use its reading notes as candidates for the full-text notes on the nearest review. Other related notes are What Does Data Visualization Show When It Departs from Efficiency? Wholes Without Numbers, Readings That Stay Open, and Deliberate Abstraction, which deals with the value of visualization apart from the yardstick of efficiency, What Data Visualization and Data Art Share, and Where They Differ, which deals with the boundary between data visualization and data art, and How Data Visualization and Data Art Have Been Treated in Creativity Education, a literature map of creativity education.

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