Cognitive Fit: A Theory‐Based Analysis of the Graphs Versus Tables Literature*
Abstract
A considerable amount of research has been conducted over a long period of time into the effects of graphical and tabular representations on decision‐making performance. To date, however, the literature appears to have arrived at few conclusions with regard to the performance of the two representations. This paper addresses these issues by presenting a theory, based on information processing theory, to explain under what circumstances one representation outperforms the other. The fundamental aspects of the theory are: (1) although graphical and tabular representations may contain the same information, they present that information in fundamentally different ways; graphical representations emphasize spatial information, while tables emphasize symbolic information; (2) tasks can be divided into two types, spatial and symbolic, based on the type of information that facilitates their solution; (3) performance on a task will be enhanced when there is a cognitive fit (match) between the information emphasized in the representation type and that required by the task type; that is, when graphs support spatial tasks and when tables support symbolic tasks; (4) the processes or strategies problem solvers use are the crucial elements of cognitive fit since they provide the link between representation and task; the processes identified here are perceptual and analytical; (5) so long as there is a complete fit of representation, processes, and task type, each representation will lead to both quicker and more accurate problem solving. The theory is validated by its success in explaining the results of published studies that examine the performance of graphical and tabular representations in decision making.
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Cognitive fit theory suggests that the alignment between an individual's cognitive processes and the representation of information by technology influences the effectiveness of problem solving and decision-making.
“Cognitive fit theory complements this relationship by suggesting that the alignment between an individual’s cognitive processes and the representation of information by technology influences the effectiveness of problem solving and decision-making [18].”
From User Intentions to Use ChatGPT for Self-Diagnosis and Health-Related Purposes: Cross-sectional Survey Study (2023) · cites this paper as [18]
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- Authors
- Vessey, I.
- Affiliations
- Pennsylvania State University (US)
- Corresponding author
- Vessey, Iris
- Venue
- Decision Sciences
- Publisher
- Wiley
- Published
- 1991-03
- Volume
- 22
- Issue
- 2
- Pages
- 219-240
- ISSN
- 0011-7315 (ISSN-L)
- 1540-5915
- OpenAlex
- W2123334556
- Cited by
- 1,263
- References
- 88
- Citations by year
- 2026: 59 · 2025: 44 · 2024: 29 · 2023: 36 · 2022: 40 · 2021: 42 · 2020: 50 · 2019: 42 · 2018: 60 · 2017: 41 · 2016: 56 · 2015: 65 · 2014: 54 · 2013: 60 · 2012: 67
- FWCI
- 12.58
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Decision Sciences (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Keywords
- cognitive fit, graphical representation, tabular representation, decision-making, information processing theory, geospatial data, symbolic information, problem-solving strategies, perceptual processing, analytical processing
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