Detection, Inspection, Return: An Object-Based Classification and Metric of Fixations in Complex Scenes
PMC13053019
· 10.1162/OPMI.a.319
Gap Declaration
A potential explanation for this is that the interplay between Detection and Return fixations contextualizes objects within a scene. In shorter trials, the prioritization of understanding contextual object (inter-)relations may take precedence over Inspections as it can play an important role in more efficient scene comprehension (Murlidaran & Eckstein,). Future experiments can test this hypothesis directly, e.g., by testing changes in D, I, R proportions as a function of object-scene congruency and viewing time (cf. Võ & Henderson,). An alternative explanation could be that the increased pace of exploration, with Detections spread across more objects and fewer Inspections, may lead to incomplete processing and therefore require more Returns, akin to regressions in reading (Engbert et al.,; Vitu & McConkie,).
Abstract
Abstract Analyses of human gaze behaviour towards complex scenes typically aim to explain heatmaps or scan-paths. While heatmaps lack temporal information, scan-paths aim for a level of detail which often is impractical. We introduce a novel approach, based on the premise that most fixations target objects and do so in meaningfully different ways, depending on temporal context: Detection fixations (D) foveate an object for the first time; Inspection fixations (I) successively target object details; and Return fixations (R) revisit a previously fixated object after going elsewhere. To test the hypothesis that these classes capture distinct fixation profiles, we reanalysed a large dataset of scene fixations. We computed separate heatmaps for D, I, and R and found significantly higher inter-o…
Conclusions / Discussion
DISCUSSION Traditional models of visual attention either focus on the spatial distribution of fixations across an image (e.g., heatmaps) or on their precise sequence in scan-paths. However, neither approach utilizes the object-directed nature of typical fixation sequences, nor allows for intuitive distinctions between different types of fixations. Here, we present a simple algorithm to sort fixations towards naturalistic scenes into three distinct classes. An evaluation on a large scene viewing dataset confirmed systematic and reliable differences between Detections, Inspections and Returns, including distinct salience profiles and dynamics. We computed separate D, I, and R fixation maps for 700 naturalistic scenes using data from >100 observers and found reliable differences between these classes, which generalised across images and observers. Previous work has shown a shift in fixation duration and saccadic amplitude over free viewing time (ambient vs. focal mode; Pannasch et al.,; Unema et al.,) and examined the proportion of refixations under different task instructions and visuo-sensorimotor tasks (e.g., Droll & Hayhoe,). The algorithm proposed here classifies each object-dire…
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Does this declaration represent a genuine open research gap?
Structural Hole
65% bridge
Technique originates in genomics bioinformatics; functional analogues in epidemiology, psychology literature are absent.
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No paper has claimed this gap. Appreciate the opportunity.
Provenance