Control of gaze in natural environments: effects of rewards and costs, uncertainty and memory in target selection

Hayhoe, Mary; Matthis, Jonathan

doi:10.1098/rsfs.2018.0009

Cited by 30 publications

(24 citation statements)

References 42 publications

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“…Second, a number of studies have highlighted that spatial positioning plays a strong role in guiding search towards objects when finding objects in both 2D scene images (Castelhano & Heaven, 2011;Malcolm & Henderson, 2010;Pereira & Castelhano, 2019;Võ & Henderson, 2009;Williams & Castelhano, 2019) and virtual 3D environments (Hayhoe & Matthis, 2018;Kit et al, 2014;Li, Aivar, Kit, Tong, & Hayhoe, 2016;Li, Aivar, Tong, & Hayhoe, 2018). Thus, it seems reasonable that the depth at which the information occurs would also play a role when searching in a real environment.…”

Section: Discussionmentioning

confidence: 99%

The Foreground Bias: Differing impacts across depth on visual search in scenes

Lly¹,

Krzyś²,

Castelhano³

2019

Preprint

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When you walk into a room, you perceive visual information that is both close to you and farther in depth. In the current study, we investigated how visual search is affected by information across scene depth and contrasted it with the effect of semantic scene context. Across two experiments, participants performed search for target objects appearing either in the foreground or background regions within scenes that were either normally configured or had semantically mismatched foreground and background contexts (Chimera scenes; Castelhano, Fernandes, & Theriault, 2018). In Experiment 1, we found participants had shorter latencies and fewer fixations to the target. This pattern was not explained by target size. In Experiment 2, a preview of the scene prior to search was added to better establish scene context prior to search. Results again show a Foreground Bias, with faster search performance for foreground targets. Together, these studies suggest processing differences across depth in scenes, with a preference for objects closer in space.

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Section: Discussionmentioning

confidence: 99%

The Foreground Bias: Differing impacts across depth on visual search in scenes

Lly¹,

Krzyś²,

Castelhano³

2019

Preprint

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show abstract

“…1). Participants were recruited based on (1) having normal or contact-corrected vision and no colorblindness, (2) having no neurological or psychiatric conditions, and 3 Eye-tracker specifications and accuracy. A monocular, in-headset eye-tracker (Pupil Labs: 120 Hz sampling frequency, 5.7 ms camera latency, 3.0 ms processing latency; 0.6 visual degrees accuracy, 0.08 visual degrees precision) continuously monitored the position of participants' right eye during scene viewing.…”

Section: Participantsmentioning

confidence: 99%

“…Constructing a sense of place in a complex, dynamic environment is an active process. Humans actively sample their sensory environment to build an understanding of their surroundings and gain information relevant to their behavioral goals 1,2 . Yet, much of what we know about how people encode real-world environments comes from paradigms that severely limit participants' active affordances: paradigms in which head-restricted participants view images that are passively displayed on a computer screen.…”

mentioning

confidence: 99%

Active vision in immersive, 360° real-world environments

Haskins

Mentch

Botch

et al. 2020

Sci Rep

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How do we construct a sense of place in a real-world environment? Real-world environments are actively explored via saccades, head turns, and body movements. Yet, little is known about how humans process real-world scene information during active viewing conditions. Here, we exploited recent developments in virtual reality (VR) and in-headset eye-tracking to test the impact of active vs. passive viewing conditions on gaze behavior while participants explored novel, real-world, 360° scenes. In one condition, participants actively explored 360° photospheres from a first-person perspective via self-directed motion (saccades and head turns). In another condition, photospheres were passively displayed to participants while they were head-restricted. We found that, relative to passive viewers, active viewers displayed increased attention to semantically meaningful scene regions, suggesting more exploratory, information-seeking gaze behavior. We also observed signatures of exploratory behavior in eye movements, such as quicker, more entropic fixations during active as compared with passive viewing conditions. These results show that active viewing influences every aspect of gaze behavior, from the way we move our eyes to what we choose to attend to. Moreover, these results offer key benchmark measurements of gaze behavior in 360°, naturalistic environments.

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“…During novel tool use [34,20] found that in actors and observers, respectively, prior knowledge and uncertainty alter problem solving. Uncertainty in eye movements has been examined both during cognitive decisions [15], problem solving while copying a model [3], outdoor navigation [17] and driving [46]. However, to our knowledge there have been no prior attempts to combine egocentric video, eye movement data, and frame-by-frame uncertainty ratings for a complex extended task like ours.…”

Section: Related Workmentioning

confidence: 99%

EPIC-Tent: An Egocentric Video Dataset for Camping Tent Assembly

Jang

Sullivan

Ludwig

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

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This paper presents an outdoor video dataset annotated with action labels, collected from 24 participants wearing two head-mounted cameras (GoPro and SMI eye tracker) while assembling a camping tent. In total, this is 5.4 hours of recordings. Tent assembly includes manual interactions with non-rigid objects such as spreading the tent, securing guylines, reading instructions, and opening a tent bag. An interesting aspect of the dataset is that it reflects participants' proficiency in completing or understanding the task. This leads to participant differences in action sequences and action durations. Our dataset, called EPIC-Tent 1 , also has several new types of annotations for two synchronised egocentric videos. These include task errors, self-rated uncertainty and gaze position, in addition to the task action labels. We present baseline results on the EPIC-Tent dataset using a state-of-the-art method for offline and online action recognition and detection.

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Control of gaze in natural environments: effects of rewards and costs, uncertainty and memory in target selection

Cited by 30 publications

References 42 publications

The Foreground Bias: Differing impacts across depth on visual search in scenes

The Foreground Bias: Differing impacts across depth on visual search in scenes

Active vision in immersive, 360° real-world environments

EPIC-Tent: An Egocentric Video Dataset for Camping Tent Assembly

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