Visual and emotional salience influence eye movements

Niu, Yaqing; Todd, Rebecca M.; Kyan, Matthew; Anderson, Adam K.

doi:10.1145/2325722.2325726

Cited by 28 publications

(31 citation statements)

References 25 publications

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“…One candidate dimension is emotional salience, which has recently gained im-6.5 general limitations portance particularly in psychological research. Niu et al (2012) present evidence for the importance of emotional salience at a very early stage in the perception of pictures. Indeed, they show that "[e]motional salience can override visual salience and can determine attention allocation in complex scenes.…”

Section: Predictive Capabilities Of Modelsmentioning

confidence: 67%

Empirically measuring salience of objects for use in pedestrian navigation

Kattenbeck

2015

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems

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Auch wenn sie es vehement von sich weisen würden, so haben meine Eltern großen Anteil an dieser, meiner Dissertation. Sie gaben mir von Kindesbeinen an die Freiheit, mich mit dem Nachdenken auseinanderzusetzen. Glaubt mir, Mama und Papa, ohne diese Freiheit wäre ich heute nicht der, der ich bin.Schließlich danke ich meiner über alles geliebten Frau Anita. Wir sind fast genau so lange verheiratet, wie es gedauert hat, bis ich nun mein Promotionsverfahren erfolgreich abschließen konnte. In diesen Jahren haben wir einiges zusammen erlebt und durchgemacht.Du, Anita, hast immer zu mir gehalten und mich in Allem und mit Allem unterstützt.Ohne Dich, Anita, wäre diese Arbeit undenkbar.Dir, Anita, widme ich diese Arbeit.vi A B S T R A C T background Humans usually refer to landmarks when they give route directions to pedestrians. One of the reasons why current mobile pedestrian navigation systems do not yet mimic this mode of communication is the lack of available data sources. The usefulness of a crowd-sourced data acquisition approach to overcome this problem has long been mooted. However, to date no empirically sound way of measuring the salience of objects by means of surveys exists.goal Given this background, this doctoral work has three goals:1. To achieve a sound way of measuring salience and its subdimensions, i.e. visibility in advance, cognitive salience, prototypicality, structural salience, and visual salience based on taking dimensions revealed in earlier studies systematically and simultaneously into account.2. To find subgroups of visual features among the large number of visual attributes known from the literature.3. To find the most important subdimensions of salience by means of estimating two different structural equation models. Model I is based on assumptions of independence among subdimensions, whereas model II reflects hypotheses of mediation.Taken as a whole, achieving these goals will foster both, the advancement of theories of salience and landmark acquisition methods.methodology A large scale, in-situ experiment was implemented, trying to overcome weaknesses of earlier attempts made to estimate salience. An vii appropriate sample size of buildings and non-buildings was calculated a priori (n obj = 360). Objects were randomly selected based on their geographical coordinates and randomly grouped into n r = 55 routes. Participants were required to rate objects by means of a survey. The questions were derived from empirical evidence found in earlier studies. Each route was walked by two different participants (n = 112), id est (i.e.) two ratings per object were collected for data analysis.findings Model I and model II were analyzed using PLS Path Modeling and consistent PLS Path Modeling, respectively. The measurement models proposed showed a good fit, although some weaknesses were identified for prototypicality and cognitive salience. Geometrical aspects as well as features like (visual) age turned out to have a stronger impact on visual salience than color. Model I did not yield reasonable struct...

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Section: Predictive Capabilities Of Modelsmentioning

confidence: 67%

Empirically measuring salience of objects for use in pedestrian navigation

Kattenbeck

2015

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems

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“…Using different types of activities (scene memorization, pleasantness rating, visual search, and free viewing), Mills et al [46] found a direct task effect on both spatial and temporal characteristics of eye movements during scene perception. Research also shows that visual saliency and affective saliency are in competition for gaze control in stimuli that contain emotional information [31,49].…”

Section: Emotional Saliencementioning

confidence: 96%

Physiological heatmaps: a tool for visualizing users’ emotional reactions

Courtemanche

Léger

Dufresne

et al. 2017

Multimed Tools Appl

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Practitioners in many fields of human-computer interaction are now using physiological data to measure different aspects of user experience. The dynamic nature of physiological data offers a continuous window to the users and allows a better understanding of their experience while interacting with a system. However, in order to be truly informative, physiological signals need to be closely linked to users' behaviors and interaction states. This paper presents an analysis method that provides a direct visual interpretation of users' physiological signals when interacting with an interface. The proposed physiological heatmap tool uses eyetracking data along with physiological signals to identify regions where users are experiencing different emotional and cognitive states with a higher frequency. The method was evaluated in an experiment with 44 participants. Results show that physiological heatmaps are able to identify emotionally significant regions within an interface better than standard gaze heatmaps. Applications of the method to different fields of HCI research are also discussed.

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“…At the same time, fixations stemming from top-down guidance also affect our attention model. Test subjects focus on high-level objects (e.g., faces, eyes) and mid-level objects (e.g., horizon line, simple geometric objects) more often than on salient regions that are not connected to objects involving cognition (see, e.g., [Niu et al 2012]. For example, subjects often fixate on special regions in a face (e.g., eyes, mouth, and ears) displayed in an image.…”

Section: Visual Attention Modelmentioning

confidence: 99%

“…We therefore refrained from further studies involving different designs of cascades. Niu et al [2012] examined the influence of cognitive/affective effects on observers' attention. They compared the number of fixations in regions with both visual and emotional salience, showing that regions with high emotional salience predict fixations much better than regions with high visual salience.…”

Section: Evaluation Of the Visual Attention Modelmentioning

confidence: 99%

“…There are many experiments showing that focal attention, visual perception, and fixations are strongly correlated (e.g., as discussed by Hoffman and Subramaniam [1995], Saarinen [1993], and Niu et al [2012]). We also know from our own experiments that one can distinguish images of visual art quite well simply by using the distribution of fixations.…”

Section: Introductionmentioning

confidence: 99%

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Simulating Fixations When Looking at Visual Arts

Pflüger

Höferlin

Raschke

et al. 2015

ACM Trans. Appl. Percept.

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When people look at pictures, they fixate on specific areas. The sequences of such fixations are so characteristic for certain pictures that metrics can be derived that allow successful grouping of similar pieces of visual art. However, determining enough fixation sequences by eye tracking is not practically feasible for large groups of people and pictures. In order to get around this limitation, we present a novel algorithm that simulates eye movements by calculating scan paths for images and time frames in real time. The basis of our algorithm is an attention model that combines and optimizes rectangle features with Adaboost. The model is adapted to the characteristics of the retina, and its input is dependent on a few earlier fixations. This method results in significant improvements compared to previous approaches. Our simulation process delivers the same data structures as an eye tracker, thus can be analyzed by standard eye-tracking software. A comparison with recorded data from eye tracking experiments shows that our algorithm for simulating fixations has a very good prediction quality for the stimulus areas on which many subjects focus. We also compare the results with those from earlier works. Finally, we demonstrate how the presented algorithm can be used to calculate the similarity of pictures in terms of human perception.

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Visual and emotional salience influence eye movements

Cited by 28 publications

References 25 publications

Empirically measuring salience of objects for use in pedestrian navigation

Empirically measuring salience of objects for use in pedestrian navigation

Physiological heatmaps: a tool for visualizing users’ emotional reactions

Simulating Fixations When Looking at Visual Arts

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