A Componential Model of Human Interaction with Graphs. IV: Holistic and Analytical Perception of Star Graphs

Gillan, Douglas J.; Harrison, Charles L.

doi:10.1177/154193129904302309

Cited by 6 publications

(6 citation statements)

References 10 publications

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“…The holistic strategy is one in which individuals perceive a graphic as a "unitized" representation, taking in the graphic as a whole, resulting in minute reaction time variation across complexity levels (Cooper, 1976;1982). Cooper's findings have been replicated and validated by many experiments (Doane et al, 1999;Eme & Marquer, 1998;Hogeboom & Leeuwen, 1997;Gillan & Harrison, 1999;Pratt & Sohn, 2001).…”

Section: Introductionmentioning

confidence: 80%

“…When the speed and accuracy of visual discriminations are vital, understanding the different visual processing strategies people use to discriminate between computer graphics becomes essential. Some research has focused on how the training context influences strategy development (Doane, Alderson, Sohn, Pellegrino, 1996;Doane, Sohn, & Schreiber, 1999;Folk & Luce, 1987) while other research has focused on individual differences, or individual preferences, in strategy implementation (Cooper & Podgomy, 1976;Doane et al, 1999;Eme & Marquer, 1998;Hogeboom & Leeuwen, 1997;Gillan & Harrison, 1999;Pratt & Sohn, 2001). Cooper (1976) distinguished two strategies, analytic and holistic, as individual differences in visual processing strategy based on discrimination reaction time as a function of graphical complexity.…”

Section: Introductionmentioning

confidence: 99%

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Task Context and Individual Difference Effects on Skill Acquisition of Visual Processing Strategy

Kramer¹,

Pratt²,

Sohn³

2002

Proceedings of the Human Factors and Ergonomics Society Annual

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Pratt and Sohn (2001) suggested that training context impacts those with a holistic strategy while training context does not influence transfer performance for those with an analytic strategy. The present research attempts to compliment and extend their findings by examining the role of complexity and how it interacts with individual differences in visual processing strategy. Participants were given a visual discrimination task in which they trained on one of four stimulus sets that varied in complexity and similarity and then transferred to novel stimuli. Stimulus complexity was defined by the number of points each stimulus had. Stimulus similarity was defined by the six levels of discrimination difficulty. Using methods described by Pratt & Sohn (2001), participant's individual processing strategy was extracted from a screening session prior to training. Participants were categorized as either “analytic” or “holistic” based on their response reaction time as a function of stimulus complexity. Those with an analytic strategy performed with greater accuracy in transfer as complexity level decreased; those with a holistic strategy performed with greater accuracy in transfer as complexity level increased. Graphical design and training techniques are discussed.

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Section: Introductionmentioning

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Task Context and Individual Difference Effects on Skill Acquisition of Visual Processing Strategy

Kramer¹,

Pratt²,

Sohn³

2002

Proceedings of the Human Factors and Ergonomics Society Annual

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“…One of the possible advantages of a star graph or other multivariate graph is that a graph reader might be able to perceive each star holistically, as an object (e.g., Lewandowsky and . Gillan and Harrison (1999) speculated that if a star were intended to be perceived as an object, adding object-like characteristics should improve a graph reader's holistic perception. Specifically, following from Biederman's (1987) "recognition-by-components" theory of object perception, connecting the ends of the lines in the star to create an outline would provide the basic elements that are essential to object perception, particularly, vertices.…”

Section: Research and Findingsmentioning

confidence: 99%

“…Specifically, following from Biederman's (1987) "recognition-by-components" theory of object perception, connecting the ends of the lines in the star to create an outline would provide the basic elements that are essential to object perception, particularly, vertices. Gillan and Harrison (1999) presented participants with displays that consisted of four star graphs, three of which were very similar and one that differed only by the length of one leg. Participants' task was to select the star that was different.…”

Section: Research and Findingsmentioning

confidence: 99%

A Componential Model of Human Interaction with Graphs: VII. a Review of the Mixed Arithmetic-Perceptual Model

Gillan

2009

Proceedings of the Human Factors and Ergonomics Society Annual

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This paper provides a summary of the development and evaluation of a componential model of graph reading called the Mixed Arithmetic-Perceptual (MA-P) model. A review of the history underlying the development of the model begins the paper. The second section describes the research used to test the predictions from the model and to further develop it. The third section integrates the research to produce a single omnibus version of the MA-P model. Finally, the fourth section projects the future of the MA-P modeling approach, for specific versions of the model, additional research, as well as applications. HISTORICAL BACKGROUNDThe purpose of this paper is to summarize the research that I have done, often with colleagues, related to a model of how people read graphs to perform quantitative tasks. Twenty years ago, I was working for Lockheed Engineering and Sciences Company on issues related to the user interface for the Space Station. While conducting a task analysis of Space Station activities, Bob Lewis, an intern from Rice University at that time, and I became interested in the tasks that scientists on the Space Station would perform and the role that quantitative tools, such as graphical displays would play in those tasks. That interest led us to look for empirically-based models of reading graphs to solve quantitative problems. We found no models of graph reading at that time that we believed would be helpful, so we embarked on research that we hoped might lead us to such a model. (Several models did enter the literature during the time that we conducted our research -such as, Lohse, 1997 --but we had already begun what we considered to be a fruitful approach, so we continued.)We started investigating graph reading by interviewing the people around us --mostly scientists, engineers, and graduate students, as well as some undergraduate students at Rice -to discover the problems that they used graphs to solve and to describe how they interacted with graphs. Later, we became more systematic by providing other participants from these groups with simple graphs and basic tasks based on the ones that our initial participants had described, then asking them to think aloud or to write down what they thought about as they solved the problem using the graph. These qualitative data suggested to us that (1) people used a number of perceptual and cognitive processing components in order to perform quantitative tasks using graphs, and (2) people applied these processing components in systematic sequences that were similar across the various individuals and groups.The qualitative data that we collected served as the basis of a modeling approach which we came to call the "Mixed Arithmetic-Perceptual (MA-P Model); so named because some of the processing components involved sensory/perceptual processing (e.g., visual search and height comparison) and other components involved mental arithmetic (i.e., addition, subtraction, division) on values of the

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“…In research on star graphs (multivariate graphs consisting of lines radiating from a central point, with the length of each line indicating an amount), Gillan and Harrison (1999) observed graph readers using two different types of perceptual strat-egies to identify the different star in a set of four (the difference was that one of five lines that made up the star was slightly longer in one of the stars). One type of reader showed no difference in the time to identify the different star as a function of the position of the longer line on the different star; in contrast, other graph readers showed a pattern of taking longer to decide which star was different as the longer line was further off vertical.…”

Section: Presentation Of Informationmentioning

confidence: 99%

Usability Science. I: Foundations

Gillan

Bias²

2001

International Journal of Human-Computer Interaction

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In this article, we describe and analyze the emergence of a scientific discipline, usability science, which bridges basic research in cognition and perception and the design of usable technology. An analogy between usability science and medical science (which bridges basic biological science and medical practice) is discussed, with lessons drawn from the way in which medical practice translates practical problems into basic research and fosters technology transfer from research to technology. The similarities and differences of usability science to selected applied and basic research disciplines-human factors and human-computer interaction (HCI) is also described. The underlying philosophical differences between basic cognitive research and usability science are described as Wundtian structuralism versus Jamesian pragmatism. Finally, issues that usability science is likely to continue to address-presentation of information, user navigation, interaction, learning, and methods-are described with selective reviews of work in graph reading, controlled movement, and method development and validation.

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A Componential Model of Human Interaction with Graphs. IV: Holistic and Analytical Perception of Star Graphs

Cited by 6 publications

References 10 publications

Task Context and Individual Difference Effects on Skill Acquisition of Visual Processing Strategy

Task Context and Individual Difference Effects on Skill Acquisition of Visual Processing Strategy

A Componential Model of Human Interaction with Graphs: VII. a Review of the Mixed Arithmetic-Perceptual Model

Usability Science. I: Foundations

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