Detection, Inspection, Return: An Object-Based Classification and Metric of Fixations in Complex Scenes
PMC13053019
· 10.1162/OPMI.a.319
Gap Declaration
Other types of images with considerably sparser or denser distribution of visual objects may require adjusted spatial heuristics, which could be determined e.g., by labelling part of the stimuli or combining the D, I, R approach with the detection of region of interests via hidden Markov models (Coutrot et al.,). The present findings provide evidence for systematic and reliable differences between Detection, Inspection and Return fixations and point to potentially diverging functional roles. Future studies should test the functional significance of each type of fixation in targeted experiments, e.g., probing the effect of working memory load or individual capacity on the proportion of R fixations. Future research could also investigate how individual differences in gaze behaviour (e.g., Broda & de Haas,; de Haas et al.,; Linka & de Haas,) are reflected in D, I, and R fixations and how they vary across age (Helo et al.,; Krishna et al.,; Linka et al.,). Further, oculomotor metrics like saccadic velocity and acceleration may reveal further facets of D, I, and R fixations and the differences between them.
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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Structural Hole
65% bridge
Technique originates in computer science; functional analogues in psychology, criminal justice literature are absent.
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