Using coefficient K to distinguish ambient/focal visual attention during map viewing

Abstract

We demonstrate the use of the ambient/focal coecient K for studying the dynamics of visual behavior when performing cartographic tasks. Participants viewed a cartographic map and satellite image of Barcelona while performing a number of map-related tasks. Cartographic maps can be viewed as summary representations of reality, while satellite images are typically more veridical, and contain considerably more information. Our analysis of traditional eye movement metrics suggests that the satellite representation facilitates longer fixation durations, requiring greater scrutiny of the map. The cartographic map affords greater peripheral scanning, as evidenced by larger saccade amplitudes. Evaluation of K elucidates task dependence of ambient/focal attention dynamics when working with geographic visualizations: localization progresses from ambient to focal attention; route planning fluctuates in an ambient-focal.ambient pattern characteristic of the three stages of route end point localization, route following, and route confirmation.

Towards capturing focal/ambient attention during dynamic wayfinding

“…In the single-participant analysis, it is surprising that episodes of ambient attention are mixed with episodes of focal attention along the entire trajectory. Based on previous research with stationary eye-tracking [Krejtz et al 2017] it could be rather expected that the attentional mode changes gradually over time, and in addition that it changes swiftly with the task at hand (e.g., reading a map vs. walking along a corridor). In the between-participants analysis, clusters of focal attention could be expected to be larger than single 2x2 meter grid cells.…”

“…It is possible to study a map located in the environment with either focal or ambient mode of attention [Krejtz et al 2017], but is it possible (within the theoretical definition of the concept) to look at large architectural elements with either focal or ambient mode of attention? With the current implementation, the detection of ambient/focal attention modes might be inconsistent across these two situations.…”

“…Focal attention has been associated with deeper cognitive processing of stimuli while ambient attention has been associated with scanning or exploring the stimuli. It has been evaluated in viewing artworks [Krejtz et al 2016] and maps [Krejtz et al 2017]. K is derived by calculating the difference between fixation duration and its subsequent saccade expressed in standardised (z-score) values.…”

Detecting Relevance during Decision-Making from Eye Movements for UI Adaptation

“…Similarly, advertisements on a website were adapted based on relevance inferred from overall dwell time [Alt et al 2012]. Several works have analyzed the gaze behavior while exploring a map [Krejtz et al 2017], for example to adapt complex legends to only show relevant items [Göbel et al 2018], to highlight important points of interest to facilitate planning [Göbel and Kiefer 2019], or to follow-up on user's interests [Qvarfordt and Zhai 2005]. Other systems extract relevant information to compile a summary for later use [Buscher et al 2012a;Nguyen and Liu 2016].…”

Visual complexity and memorability of maps

“…Facilitated by such short or long-term recall, map use is a big part of how we acquire and retain spatial knowledge in modern times. In our previous studies we explored measures of map complexity in relation to direct human ranking of complexity at different scales (Schnur, Bektaş, & Çöltekin, 2017), human experience with maps where high levels of information complexity cannot be avoided (Çöltekin et al, 2016;Krejtz, Çöltekin, Duchowski, & Niedzielska, 2017), and if clutter and entropy measures correlate with cartographic generalization (Brychtová, Çöltekin, & Paszto, 2016;Brychtová & Çöltekin, 2017). Given the above, in this work-in-progress paper, we explore how well the computational approaches to visual complexity (e.g., saliency, clutter, memorability) work with maps vs. satellite images by comparing the outcomes across datasets, methods and contexts.…”