A new approach to modeling the influence of image features on fixation selection in scenes

Nuthmann, Antje; Einhäuser, Wolfgang

doi:10.1111/nyas.12705

Cited by 36 publications

(67 citation statements)

References 54 publications

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“…However, determining which objects in a scene are fixated, in what order, and for how long, relies heavily on the interplay between the viewer’s goals and available visual information. When the viewer’s goal is non-specific (e.g., memorizing a scene), image-based properties can predict where people fixate: edge density, visual clutter and homogenous segments predict fixation probability, while luminance and contrast play more minor roles [34]. The features used to select fixation sites are also determined by distance from the previous fixation, with shorter saccades (<8°) relying more on specific image features, particularly high-spatial frequencies, compared to longer saccades [35].…”

Section: Goal 2: Where Is X?mentioning

confidence: 99%

Making Sense of Real-World Scenes

Malcolm

Groen

Baker

2016

Trends in Cognitive Sciences

123

128

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To interact with the world, we have to make sense of the continuous sensory input conveying information about our environment. A recent surge of studies has investigated the processes enabling scene understanding, using increasingly complex stimuli and sophisticated analyses to highlight the visual features and brain regions involved. However, there are two major challenges to producing a comprehensive framework for scene understanding. First, scene perception is highly dynamic, subserving multiple behavioral goals. Second, a multitude of different visual properties co-occur across scenes and may be correlated or independent. We synthesize the recent literature and argue that for a complete view of scene understanding, it is necessary to account for both differing observer goals and the contribution of diverse scene properties.

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Section: Goal 2: Where Is X?mentioning

confidence: 99%

Making Sense of Real-World Scenes

Malcolm

Groen

Baker

2016

Trends in Cognitive Sciences

123

128

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“…Importantly, specifically examining early, potentially automatic shifts of attention allowed us to more closely inspect the nature of the mechanisms underlying the allocation of attention in naturalistic scenes. Finally, we applied a recently proposed linear mixed model analysis approach (Nuthmann and Einhäuser, 2015) which constitutes an important advancement over previous studies because it enabled us to compare physical saliency and social features as well as their interaction as predictors of fixations within a shared model.…”

Section: Introductionmentioning

confidence: 99%

Preferential Processing of Social Features and Their Interplay with Physical Saliency in Complex Naturalistic Scenes

End

Gamer

2017

Front. Psychol.

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According to so-called saliency-based attention models, attention during free viewing of visual scenes is particularly allocated to physically salient image regions. In the present study, we assumed that social features in complex naturalistic scenes would be processed preferentially irrespective of their physical saliency. Therefore, we expected worse prediction of gazing behavior by saliency-based attention models when social information is present in the visual field. To test this hypothesis, participants freely viewed color photographs of complex naturalistic social (e.g., including heads, bodies) and non-social (e.g., including landscapes, objects) scenes while their eye movements were recorded. In agreement with our hypothesis, we found that social features (especially heads) were heavily prioritized during visual exploration. Correspondingly, the presence of social information weakened the influence of low-level saliency on gazing behavior. Importantly, this pattern was most pronounced for the earliest fixations indicating automatic attentional processes. These findings were further corroborated by a linear mixed model approach showing that social features (especially heads) add substantially to the prediction of fixations beyond physical saliency. Taken together, the current study indicates gazing behavior for naturalistic scenes to be better predicted by the interplay of social and physically salient features than by low-level saliency alone. These findings strongly challenge the generalizability of saliency-based attention models and demonstrate the importance of considering social influences when investigating the driving factors of human visual attention.

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“…Knowing that the luminance distribution across different regions of the field-ofview [50], [51] and the relationship between these regions can contribute to subjective visual comfort perception [52] and behavioral objective responses [53], [54], [55], the changes in this relations caused by gaze shifts should be observed. Nevertheless, the assumption behind existing prediction metrics is that gaze direction is fixed and directed towards a task area.…”

Section: A Preliminary Gaze Responsive Light-driven (Gr L ) Modelmentioning

confidence: 99%

A human-centric approach to assess daylight in buildings for non-visual health potential, visual interest and gaze behavior

Ámundadóttir

Rockcastle

Khanie

et al. 2017

Building and Environment

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This paper introduces a novel approach for the assessment of daylight performance in buildings, venturing beyond existing methods that evaluate 2-dimensional illumination and comfort within a fixed field-of-view in order to predict human responses to light concerning non-visual health potential, visual interest, and gaze behavior in a visually immersive scene. Using a 3D rendered indoor environment to exemplify this coordinated approach, the authors assess an architectural space across a range of view directions to predict non-visual health potential, perceptual visual interest, and gaze behavior at the eye level of an occupant across an immersive field-of-view. This method allows the authors to explore and demonstrate the impact of space, time, and sky condition on three novel daylight performance models developed to predict the effects of ocular light exposure using a humancentric approach. Results for each model will be presented in parallel and then compared to discuss the need for a multi-criteria assessment of daylight-driven human responses in architecture. A parallel and comparative approach can allow the designer to adapt the architectural space based on the program use and occupants needs.

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A new approach to modeling the influence of image features on fixation selection in scenes

Cited by 36 publications

References 54 publications

Making Sense of Real-World Scenes

Making Sense of Real-World Scenes

Preferential Processing of Social Features and Their Interplay with Physical Saliency in Complex Naturalistic Scenes

A human-centric approach to assess daylight in buildings for non-visual health potential, visual interest and gaze behavior

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