Local Density Guides Visual Search: Sparse Groups are First and Faster

Halverson, Tim; Hornof, Anthony J.

doi:10.1177/154193120404801615

Cited by 15 publications

(20 citation statements)

References 8 publications

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“…Another one is distractors: Searching for a number among letter distractors is faster than searching for a letter [2]. The density of items impacts also visual search [17,35]. Related to this is the saliency effect: Items that are salient in terms of color or size can be easier to localize.…”

Section: Absent Targetmentioning

confidence: 95%

Model of visual search and selection time in linear menus

Bailly

Oulasvirta

Brumby

et al. 2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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This paper presents a novel mathematical model for visual search and selection time in linear menus. Assuming two visual search strategies, serial and directed, and a pointing sub-task, it captures the change of performance with five factors: 1) menu length, 2) menu organization, 3) target position, 4) absence/presence of target, and 5) practice. The novel aspect is that the model is expressed as probability density distribution of gaze, which allows for deriving total selection time. We present novel data that replicates and extends the Nielsen menu selection paradigm and uses eye-tracking and mouse tracking to confirm model predictions. The same parametrization yielded a high fit to both menu selection time and gaze distributions. The model has the potential to improve menu designs by helping designers identify more effective solutions without conducting empirical studies.

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Section: Absent Targetmentioning

confidence: 95%

Model of visual search and selection time in linear menus

Bailly

Oulasvirta

Brumby

et al. 2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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“…This result accords with a previous study about password authentication without eye-gaze-based input [21], which showed that participants needed more search time when the number of grid keys increased. Previous research on eye movements already had reported that participants needed less search time for sparse layouts than for dense layouts [22]. It is thus likely that the participants needed more time to search the necessary object keys to form the password as the total number of key options increased.…”

Section: Discussionmentioning

confidence: 97%

What Kind of Grid Formations and Password Formats are Useful for Password Authentication with Eye-Gaze-Based Input?

Paulus¹,

Remijn²

2019

J Ergonomics

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This study investigates which grid formations and password formats are useful for password authentication by means of eye-gazebased input. Sixteen grid formations were made, in between 3×3 to 6×6 cells (columns-by-rows), for three password formats. The formats were an alphanumeric format, a pattern format, and a picture format. The participants were asked to memorize a 4-object or a 6-object password and register (Task 1), confirm (Task 2), and log in (Task 4) the password on each of the 16 grids with eye-gaze-based input. In Task 3, the participants evaluated each grid with a rating scale. The results showed that task-completion time was mostly shorter for the alphanumeric password format than for the pattern or picture format. Task-completion time of 4-object or 6-object passwords generally increased as the grid density increased, while the task-success rate at the first attempt decreased when the grid density increased. Task-completion time often was longer for grids with more rows than columns (vertical formations, e.g., 3×4 cells) than for grids with more columns than rows (horizontal formations, e.g., 4×3 cells). These results suggest that password authentication with eye-gaze-based input is best performed on horizontal grids with relatively few cells and a traditional (alphanumeric) format.

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“…The experiment is discussed in more detail in Halverson and Hornof (2004b) and is presented here specifically with regard to developing a comprehensive model of active vision.…”

Section: Mixed Density Taskmentioning

confidence: 99%

“…The mixed density experiment (Halverson & Hornof, 2004b) investigated the effects of varying the visual density of elements in a structured layout. The CVC search experiment (Hornof, 2004) investigated the effects of layout size and visual hierarchy.…”

Section: Eye-tracking Experiments To Develop the Modelmentioning

confidence: 99%

A Computational Model of “Active Vision” for Visual Search in Human–Computer Interaction

Halverson

Hornof

2011

Human–Computer Interaction

Self Cite

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Human visual search plays an important role in many human-computer interaction (HCI) tasks. Better models of visual search are needed not just to predict overall performance outcomes, such as whether people will be able to find the information needed to complete an HCI task, but to understand the many human processes that interact in visual search, which will in turn inform the detailed design of better user interfaces. This article describes a detailed instantiation, in the form of a computational cognitive model, of a comprehensive theory of human visual processing known as ''active vision'' (Findlay & Gilchrist, 2003). The computational model is built using the Executive Process-Interactive Control cognitive architecture. Eye-tracking data from three experiments inform the development and validation of the model. The modeling asks-and at least partially answers-the four questions of active vision: (a) What can be perceived in a fixation? (b) When do the eyes move? (c) Where do the eyes move? (d) What information is integrated between eye movements? Answers include: (a) Items nearer the point of gaze are more likely to be perceived, and the visual features of objects are sometimes misidentified. (b) The eyes move after the fixated visual stimulus has been processed (i.e., has entered working memory). (c) The eyes tend to go to nearby objects. (d) Only the coarse spatial information of what has been fixated is likely maintained between fixations. The model developed to answer these questions has both scientific and practical value in that the model gives Tim Halverson is a cognitive scientist with an interest in human-computer interaction, cognitive modeling, eye movements, and fatigue; he is a Research Computer Scientist in the Applied Neuroscience Branch of the Air Force Research Laboratory. Anthony Hornof is a computer scientist with an interest in human-computer interaction, cognitive modeling, visual search, and eye tracking; he is an Associate Professor in the

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Local Density Guides Visual Search: Sparse Groups are First and Faster

Cited by 15 publications

References 8 publications

Model of visual search and selection time in linear menus

Model of visual search and selection time in linear menus

What Kind of Grid Formations and Password Formats are Useful for Password Authentication with Eye-Gaze-Based Input?

A Computational Model of “Active Vision” for Visual Search in Human–Computer Interaction

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