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Ballard (1991) and Whitehead and Ballard (1990) demonstrated that perception is an active process of information acquisition, with vision dependent on action strategies rather than passive encoding.
“Ballard (1991) and Whitehead and Ballard (1990) further demonstrated that perception is an active process of information acquisition: by moving the eyes or body to gather task-relevant information, the agent learns control policies that determine what is sensed, making vision dependent on action strategies rather than passive encoding.”
From Toward Enactive Artificial Intelligence (2026) · cites this paper as Ballard (1991)
Early foundational work demonstrated that by actively controlling parameters such as camera pose or sensor configuration, agents can transform otherwise ill-posed perception problems into well-posed ones.
“Early founda- tional work (Aloimonos et al., 1988; Ballard, 1991; Whaite & Ferrie, 1997)—often termed”
From ACTIVE-o3: Empowering MLLMs with Active Perception via Pure Reinforcement Learning (2025) · cites this paper as Ballard, 1991
Active perception is a critical component of efficient perception and decision-making in humans and advanced embodied agents.
“Among the many components of perception, active percep- tion, the process of selective acquisition of sensory infor- mation to achieve specific goals, has proven essential for efficient information gathering and decision making in com- plex environments (Aloimonos et al., 1988; Ballard, 1991; Whaite & Ferrie, 1997).”
From ACTIVE-o3: Empowering MLLMs with Active Perception via Pure Reinforcement Learning (2025) · cites this paper as Ballard, 1991
Cites
A robust layered control system for a mobile robot
Brooks, R. (1986) · IEEE Journal on Robotics and Automation · doi:10.1109/jra.1986.1087032 · Cited by 7,799 · see citing works
Object vision and spatial vision: Two cortical pathways
Mishkin, M., Ungerleider, L. G., & Macko, K. A. (1983) · Trends in Neurosciences · doi:10.1016/0166-2236(83)90190-x · Cited by 2,947 · see citing works
Aloimonos, J., Weiss, I., & Bandyopadhyay, A. (1988) · International Journal of Computer Vision · doi:10.1007/bf00133571 · Cited by 931 · see citing works
Flagged first, then by citations
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Clark, A., & Chalmers, D. (1998) · Analysis · doi:10.1093/analys/58.1.7 · Cited by 5,457 · see citing works
A sensorimotor account of vision and visual consciousness
O'Regan, J. K., & Noë, A. (2001) · Behavioral and Brain Sciences · doi:10.1017/s0140525x01000115 · Cited by 3,126 · see citing works
Modeling visual attention via selective tuning
Tsotsos, J. K., Culhane, S. M., Kei Wai, W. Y., Lai, Y., Davis, N., & Nuflo, F. (1995) · Artificial Intelligence · doi:10.1016/0004-3702(95)00025-9 · Cited by 1,143 · see citing works
Deictic codes for the embodiment of cognition
Ballard, D. H., Hayhoe, M. M., Pook, P. K., & Rao, R. P. N. (1997) · Behavioral and Brain Sciences · doi:10.1017/s0140525x97001611 · Cited by 1,082 · see citing works
Self-citationAn embodied cognitive science?
Clark, A. (1999) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(99)01361-3 · Cited by 781 · see citing works
Dynamical approaches to cognitive science
Beer, R. D. (2000) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(99)01440-0 · Cited by 639 · see citing works
Learning and selective attention
Dayan, P., Kakade, S., & Montague, P. R. (2000) · Nature Neuroscience · doi:10.1038/81504 · Cited by 495 · see citing works
Multiple Realizability Revisited: Linking Cognitive and Neural States
Bechtel, W., & Mundale, J. (1999) · Philosophy of Science · doi:10.1086/392683 · Cited by 483 · see citing works
Task and context determine where you look
Rothkopf, C. A., Ballard, D. H., & Hayhoe, M. M. (2016) · Journal of Vision · doi:10.1167/7.14.16 · Cited by 366 · see citing works
Self-citationSensory acquisition in active sensing systems
Nelson, M. E., & MacIver, M. A. (2006) · Journal of Comparative Physiology A · doi:10.1007/s00359-006-0099-4 · Cited by 247 · see citing works
Interactive perception: Leveraging action in perception and perception in action
Bohg, J., Hausman, K., Sankaran, B., Brock, O., Kragic, D., Schaal, S., & Sukhatme, G. S. (2017) · IEEE Transactions on Robotics · doi:10.1109/tro.2017.2721939 · Cited by 227 · see citing works
On the relative complexity of active vs. passive visual search
Tsotsos, J. K. (1992) · International Journal of Computer Vision · doi:10.1007/bf00128132 · Cited by 164 · see citing works
Beyond the Grand Illusion: What Change Blindness Really Teaches Us About Vision
Noe, A., Pessoa, L., & Thompson, E. (2000) · Visual Cognition · doi:10.1080/135062800394702 · Cited by 147 · see citing works
Coevolution of active vision and feature selection
Floreano, D., Kato, T., Marocco, D., & Sauser, E. (2004) · Biological Cybernetics · doi:10.1007/s00422-004-0467-5 · Cited by 97 · see citing works
Look-Ahead Before You Leap: End-to-End Active Recognition by Forecasting the Effect of Motion
Jayaraman, D., & Grauman, K. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46454-1_30 · Cited by 75 · see citing works
““Active perception”, the idea of exploiting intelligent control strategies (agent motion, object manipulation, camera parameter changes etc .) for goal-directed data acquisition to improve machine vision, was pioneered by [10], [8], [11], [4].”
BackgroundNolfi, S., Bongard, J., Husbands, P., & Floreano, D. (2016) · Springer Handbooks · doi:10.1007/978-3-319-32552-1_76 · Cited by 68 · see citing works
FLAR: A Unified Prototype Framework for Few-sample Lifelong Active Recognition
Fan, L., Xiong, P., Wei, W., & Wu, Y. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01511 · Cited by 6 · see citing works
Interactive Perception for Deformable Object Manipulation
Weng, Z., Zhou, P., Yin, H., Kravberg, A., Varava, A., Navarro-Alarcon, D., & Kragic, D. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3431943 · Cited by 7 · see citing works
“Our work relates to concepts such as hand-eye coordination [11], visual servoing [12], active perception [13], [14], [15], [16], [17], [18] and interactive perception [1], but none of these fully addresses all the three aspects in a single framework: a deformable object, moving camera, and a robot…”
BackgroundACTIVE-o3: Empowering MLLMs with Active Perception via Pure Reinforcement Learning
Zhu, M., Zhong, H., Zhao, C., Du, Z., Liu, M., Huang, Z., Li, A., Chen, H., Zou, C., Chen, J., et al. (2025) · arXiv · doi:10.48550/arxiv.2505.21457
Toward Enactive Artificial Intelligence
Rafiee, B., & Sutton, R. (2026) · arXiv · doi:10.48550/arxiv.2605.24238
- Authors
- Ballard, D. H.
- Affiliations
- University of Rochester (US)
- Corresponding author
- Ballard, Dana H.
- Venue
- Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 1991-02
- Volume
- 48
- Issue
- 1
- Pages
- 57-86
- ISSN
- 0004-3702 (ISSN-L)
- OpenAlex
- W2913703059
- Cited by
- 1,022
- References
- 3
- Citations by year
- 2026: 6 · 2025: 6 · 2024: 9 · 2023: 7 · 2022: 7 · 2021: 20 · 2020: 13 · 2019: 12 · 2018: 12 · 2017: 14 · 2016: 15 · 2015: 20 · 2014: 26 · 2013: 32 · 2012: 31
- FWCI
- 62.75
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Artificial Intelligence (publishedVersion)
- Funders
- National Science Foundation (DCR-8602958)
- National Institutes of Health (R01 NS22407-01)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Computer Science › Artificial Intelligence › Psychiatry, Mental Health, Neuroscience
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Keywords
- gaze control, active vision, computational economy, low-resolution imaging, environmental cues, real-time constraints, learning, indexical reference, visual behavior
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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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