Looking at the Representations in our Mind

Mayr, Eva; Schreder, Günther; Smuc, Michael; Windhager, Florian

doi:10.1145/2993901.2993914

Cited by 15 publications

(23 citation statements)

References 30 publications

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“…These interviews focused our study design towards an analysis of data interactions and how representations and tools supported data workers' practices. User-system interactions are influenced by many factors that are not easily measurable or quantifiable [9,56,65]. This challenge is magnified when the system is the mediating artifact and usability can influence behaviors and outcomes.…”

Section: Methodsmentioning

confidence: 99%

“…We therefore used a mixed-methods qualitative approach combining descriptive techniques, semi-structured interviews, and observational walkthroughs to elicit concepts and capture how peoples' data interactions contributed to their formation of understanding. These methods support insights into behavior and interaction processes as well as the knowledge and concepts that underlie them [9,65,70]. This approach is common in formative user research and cognitive engineering [81].…”

Section: Methodsmentioning

confidence: 99%

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Untidy Data: The Unreasonable Effectiveness of Tables

Bartram

Correll

Tory³

2022

IEEE Trans. Visual. Comput. Graphics

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Fig. 1: An example spreadsheet (shared with permission of Cornerstone Architects) showing various "rich" table features that our participants employed, including (1) A Master Table of base data that is often left untouched, with manipulations happening in a copy or other area separate from the base data; (2) Marginalia such as comments or derived rows or columns in the periphery of the base table, often taking the form of freeform natural language comments; (3) Annotations such as highlighting or characters with specific meaning (e.g., a dash denotes missing values) to flag particular cells as anomalous or requiring action; and (4) Multi-cell features such as labels or even data that span multiple rows or columns of the sheet.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Untidy Data: The Unreasonable Effectiveness of Tables

Bartram

Correll

Tory³

2022

IEEE Trans. Visual. Comput. Graphics

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“…Mayr et al [33] further discussed the conceptualization of mental models in InfoVis, especially touching on the topic of whether a single or multiple mental models form during an interaction with an InfoVis system. This notion aligns well with the "cognitive collage" proposition of Liu and Stasko.…”

Section: Related Work On Sense Making and Knowledge Generation Processmentioning

confidence: 99%

“…However, a smaller proportion of the effort is directed towards a comprehensive understanding of how internal cognitive mechanisms form and function. These are commonly referred to as "mental models" [22,33]-internal cognitive imprints of an external system that enable deeper comprehension of system's features-ultimately leading to sense making and knowledge generation about a system (in this context, a dataset, a data visualization, or a dashboard). Initial mental models may be driven by individual's prior experience and domain knowledge of a system, whereby these further evolve through interaction with external representations and individual's own cognitive pathways.…”

Section: Introduction 1overviewmentioning

confidence: 99%

On Sense Making and the Generation of Knowledge in Visual Analytics

Vuckovic

Schmidt

2022

Analytics

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Interactive visual tools and related visualization technologies, built to support explorative data analysis, ultimately lead to sense making and knowledge discovery from large volumes of raw data. These processes namely rely on human visual perception and cognition, in which human analysts perceive external representations (system structure, dataset, integral data visualizations) and form respective internal representations (internal cognitive imprints of external systems) that enable deeper comprehension of the employed system and the underlying data features. These internal representations further evolve through continuous interaction with external representations. They also depend on the individual’s own cognitive pathways. Currently, there has been insufficient work on understanding how these internal cognitive mechanisms form and function. Hence, we aim to offer our own interpretations of such processes observed through our daily data exploration workflows. This is accomplished by following specific explorative data science tasks while working with diverse interactive visual systems and related notebook style environments that have different organizational structures and thus may entail different approaches to thinking and shaping sense making and knowledge generation. In this paper, we deliberate on the cognitive implications for human analysists when interacting with such a diverse organizational structure of tools and approaches when performing the essential steps of an explorative visual analysis.

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“…For all scenarios of reasoning with multidimensional data, the factual design of an ER can lead to more or less integrated IRswhich further support or impede the corresponding visual reasoning processes. Consequences: The integrational strength of an ER has to be measured via the coherence and consistency of the resulting IRs -which is a non-trivial evaluation challenge of its own [19] [20] should be part of the evaluation -way more than for expert users. 5.…”

Section: Evaluation Of Irs In Casual Settingsmentioning

confidence: 99%

Integrated Visualization of Space and Time: A Distributed Cognition Perspective

Mayr¹,

Schreder²,

Salisu³

et al. 2018

Preprint

Self Cite

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Spatiotemporal visualizations pose certain challenges for comprehension and reasoning processes. This specifically applies to casual users who want to gain an overview of the spatiotemporal origins of large cultural collections in a relatively short time. Based on a distributed cognition approach, we compare four visualization techniques (coordinated multiple views, color coding, animation and space-time cube representations), which offer different solutions to represent spatial and temporal information in an integrated fashion. We firstly assess the strengths and weaknesses of these techniques with regard to existing work. We then complement these findings with a close look at the reasoning processes of 18 casual users. In a mixed methods study, they explored a photo collection from the middle of the 20th century by means of all four spatiotemporal visualization techniques. We investigate and compare the effects of these techniques on processes of internalization, spatiotemporal reasoning and comprehension by analyzing the observed spatial, temporal and spatiotemporal insights and the degree of connectedness of these insights. Our results provide an in-depth look at how each visualization technique supports the construction of coherent internal representations and, together with data on cognitive load and subjective references, help to understand for which tasks and users they are suited.

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Looking at the Representations in our Mind

Cited by 15 publications

References 30 publications

Untidy Data: The Unreasonable Effectiveness of Tables

Untidy Data: The Unreasonable Effectiveness of Tables

On Sense Making and the Generation of Knowledge in Visual Analytics

Integrated Visualization of Space and Time: A Distributed Cognition Perspective

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