Eye guidance in natural vision: reinterpreting salience
Open accessAbstract
Models of gaze allocation in complex scenes are derived mainly from studies of static picture viewing. The dominant framework to emerge has been image salience, where properties of the stimulus play a crucial role in guiding the eyes. However, salience-based schemes are poor at accounting for many aspects of picture viewing and can fail dramatically in the context of natural task performance. These failures have led to the development of new models of gaze allocation in scene viewing that address a number of these issues. However, models based on the picture-viewing paradigm are unlikely to generalize to a broader range of experimental contexts, because the stimulus context is limited, and the dynamic, task-driven nature of vision is not represented. We argue that there is a need to move away from this class of model and find the principles that govern gaze allocation in a broader range of settings. We outline the major limitations of salience-based selection schemes and highlight what we have learned from studies of gaze allocation in natural vision. Clear principles of selection are found across many instances of natural vision and these are not the principles that might be expected from picture-viewing studies. We discuss the emerging theoretical framework for gaze allocation on the basis of reward maximization and uncertainty reduction.
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When deployed in the service of a task, eye movements may be explained by the simple imperative to sample information in order to reduce uncertainty regarding future states.
“Converging evidence suggests that, when deployed in the service of a task, eye movements may be explained by the simple imperative to sample information in order to reduce uncertainty regarding future states”
From Information-seeking, curiosity, and attention: computational and neural mechanisms (2013) · cites this paper as Tatler, Hayhoe et al. 2011
Computational studies of naturalistic behaviors show that acquiring information may have material value, as it increases the chances of success of the subject's future actions.
“Computational studies of naturalistic behaviors show that the act of acquiring information – whether it is overt or remains internal to the brain - may indeed have material value, as it increases the changes of success of the subject’s future actions (Tatler et al., 2011).”
From Attention, learning, and the value of information (2012) · cites this paper as Tatler et al., 2011
The processes required to compute information value differ markedly from those considered in decision tasks.
“However these studies also show that the processes required to compute information value differ markedly from those that have been so far considered in decision tasks.”
From Attention, learning, and the value of information (2012) · cites this paper as Tatler et al., 2011
Studies of eye movements in natural behaviors attempt to estimate the material value that an eye movement may bring.
“Rather than looking for an “intrinsic” preference for information, studies of eye movements in natural behaviors have adopted a more pragmatic approach and attempt to estimate the material value that an eye movement may bring (Hayhoe and Ballard, 2005; Tatler et al., 2011).”
From Attention, learning, and the value of information (2012) · cites this paper as Tatler et al., 2011
Practically all eye movements made in naturalistic goal-directed behaviors can be interpreted as acquiring information to guide a forthcoming action.
“Quantitative studies show that practically all the eye movements made in naturalistic goal-directed behaviors can be interpreted as acquiring information to guide a forthcoming action (Tatler et al., 2011).”
From Attention, learning, and the value of information (2012) · cites this paper as Tatler et al., 2011
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“An alternative explanation, however, emerges from studies of eye movements in natural behaviors, which suggest that the value of an eye movement lies in reducing uncertainty and increasing the expected reward (probability of success) of a future action (Ballard and Hayhoe, 2009; Hayhoe et al., 2012; Rothkopf et al., 2…”
BackgroundInfluentialA review of gaze entropy as a measure of visual scanning efficiency
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- Authors
- Tatler, B. W., Hayhoe, M. M., Land, M. F., & Ballard, D. H.
- Affiliations
- University of Dundee (GB)
- The University of Texas at Austin (US)
- University of Sussex (GB)
- Venue
- Journal of Vision
- Published
- 2011-05-27
- Volume
- 11
- Issue
- 5
- Pages
- 5-5
- ISSN
- 1534-7362 (ISSN-L)
- PMC
- PMC3134223
- OpenAlex
- W2081913479
- Cited by
- 775
- References
- 159
- Citations by year
- 2026: 47 · 2025: 35 · 2024: 29 · 2023: 35 · 2022: 41 · 2021: 61 · 2020: 80 · 2019: 53 · 2018: 49 · 2017: 61 · 2016: 50 · 2015: 60 · 2014: 72 · 2013: 59 · 2012: 38
- FWCI
- 59.97
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Journal of Vision (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Discovery Research Portal (University of Dundee) (submittedVersion)
- Funders
- National Eye Institute (R01-EY05729, R01EY019174, R01EY005729)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Keywords
- gaze allocation, eye guidance, natural vision, reward maximization, uncertainty reduction, scene viewing, visual attention
- MeSH terms
- Attention, Behavior, Eye Movements, Fixation, Ocular, Humans, Learning, Models, Psychological, Photic Stimulation, Reward, Saccades, Time Factors, Vision, Ocular
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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