This article reports a study on modeling covert visual attention as a parallel process that unfolds in synergy with the embodied agent’s action selection process. The parallel nature of the proposed model is according to the multiple covert attention hypothesis and thus suitable for the concurrent search for multiple objects in the embodied agent’s visual field. In line with the active vision approach, the interaction with the action selection process is exploited by the model to deploy visual attention in a by-need way. Additionally, the multiple focuses of attention considered in the proposed model interact in a way that their collective behavior robustly self-organizes for a proper handling of the speed—accuracy trade-off inherent to visual search tasks. Besides the self-organization of a global spatiotemporal visual attention policy, the model also produces parallel, sparse, and active spatial working memories, that is, local maps of the environment. The underlying mechanisms of the model are based on the well known formalism that describes the self-organization of collective foraging strategies in social insects. This metaphor is particularly interesting because of its similarities with the problem tackled in this article, that is, the one of parallel visual attention. This claim is validated by experimental results on a simulated robot performing local navigation, where the ability of the model to generate accurate visual attention and spatial memories in a parsimonious and robust way is shown.
Aloimonos, J., Weiss, I., & Bandyopadhyay, A. (1988). Active vision. International Journal of Computer Vision, 1(4), 333-356.
2.
Antón-Canalís, L. , Hernández-Tejera , M., & SánchezNielsen, E. ( 2006). Particle swarms as video sequence inhabitants for object tracking in computer vision. In Proceedings of the 6th International Conference on Intelligent Systems Design and Applications (ISDA) (pp. 604-609). Washington, DC: IEEE Computer Society.
3.
Ashby, W.R. ( 1952). Design for a brain. London: Chapman and Hall.
4.
Bajcsy, R. ( 1988). Active perception. Proceedings of the IEEE, 76(8), 996-1005.
5.
Balkenius, C., Eriksson, A.P., & Astrom, K. ( 2004). Learning in visual attention. In Proceedings of the International Conference on Pattern Recognition (ICPR), Workshop on Learning for Adaptable Visual Systems (LAVS 2004), 4.
Ballard, D.H., Hayhoe, M.M., Pook, P.K., & Rao, R.P.N. (1997). Deictic codes for the embodiment of cognition. Behavioral and Brain Sciences, 20, 723-767.
8.
Bedau, M.A. ( 2003). Artificial life: Organization, adaptation and complexity from the bottom up. Trends in Cognitive Sciences, 7(11), 505-512.
9.
Beer, R.D. ( 1996). Toward the evolution of dynamical neural networks for minimally cognitive behavior. In P. Maes , M. Mataric, J.-A. Meyer, J. Pollack, & S. W. Wilson (Eds.), In Proceedings of the International Conference on Simulation of Adaptive Behavior (SAB) (pp. 421-429). The MIT Press.
10.
Beer, R.D. ( 1995). A dynamical systems perspective on agent-environment interaction. Artificial Intelligence, 72(1-2), 173-215.
11.
Beer, R.D. ( 2003). The dynamics of active categorical perception in an evolved model agent. Adaptive Behavior, 11(4), 209-243.
12.
Bonabeau, E., Dorigo, M., & Theraulaz, G. ( 1999). Swarm intelligence: From natural to artificial systems . Oxford: Oxford University Press .
13.
Broggi, A., & Cattani, S. ( 2006). An agent based evolutionary approach to path detection for off-road vehicle guidance. Pattern Recognition Letters , 27(11), 1164-1173.
Chialvo, D.R., & Millonas, M.M. (1995). How swarms build cognitive maps. In L. Steels (Ed.), The biology and technology of intelligent autonomous agents (Vol. 144, pp. 439-450). NATO ASI Series.
16.
Choe, Y., Yang, H.F., & Misra, N. ( 2008). Motor system’s role in grounding, receptive field development, and shape recognition. Proceedings of the 7th International Conference on Development and Learning (ICDL) (pp. 67-72). Monterey, CA: IEEE.
17.
Corbetta, M., & Shulman, G.L. ( 2002). Control of goal-directed and stimulus-driven attention in the brain. Nature Reviews Neuroscience, 3(3), 201-215.
18.
Couzin, I. ( 2009). Collective cognition in animal groups. Trends in Cognitive Sciences, 13(1), 36-43.
19.
Croon, G. de., & Postma, E.O. ( 2007). Sensory-motor coordination in object detection. In H. A. Abbass, M. Bedau, S. Nolfi, & J. Wiles (Eds.), In Proceedings of the IEEE Symposium on Artificial Life (CI-ALIFE 2007) (pp. 147-154). IEEE.
20.
Deneubourg, J.L., Goss, S., Franks, N., & Pasteels, J.M. (1989). The blind leading the blind: Modeling chemically mediated army ant raid patterns. Journal of Insect Behavior, 2(5), 719-725.
21.
Dewey, J. ( 1896). The reflex arc concept in psychology. Psychological Review, 3, 357-370.
22.
Dickmanns, E.D., Mysliwetz, B., & Christians, T. (1990). An integrated spatio-temporal approach to automatic visual guidance of autonomous vehicles. IEEE Transactions on Systems, Man and Cybernetics, 20(6), 1273-1284.
23.
Doran, M.M., Hoffman, J.E., & Scholl, B.J. ( 2009). The role of eye fixations in concentration and amplification effects during multiple object tracking. Visual Cognition , 17(4), 574-597.
24.
Egner, T., Monti, J.M.P., Trittschuh, E.H., Wieneke, C.A., Hirsch, J., & Mesulam, M. ( 2008). Neural integration of top-down spatial and feature-based information in visual search. Journal of Neuroscience, 28(24), 6141.
25.
Floreano, D., Toshifumi, K., Marocco, D., & Sauser, E. ( 2004). Coevolution of active vision and feature selection. Biological Cybernetics, 90(3), 218-228.
26.
Franks, N.R. ( 1989). Army ants: a collective intelligence. American Scientist, 77(2), 138-145.
27.
Frintrop, S., Backer, G., & Rome, E. ( 2005). Goal-directed search with a top-down modulated computational attention system. In W. Kropatsch, R. Sablatnig, & A. Hanbury (Eds.), DAGM 2005, Lecture Notes In Computer Science (Vol 3663, pp. 117-124). Berlin Heidelberg: Springer-Verlag.
28.
Gerkey, B.P., Vaughan, R.T., & Howard, A. ( 2003). The player/stage project: Tools for multi-robot and distributed sensor systems. In Proceedings of the International Conference on Advanced Robotics (ICAR) (pp. 317-323). Coimbra, Portugal.
29.
Gibson, J. ( 1979). The ecological approach to visual perception. Mahwah, NJ: Lawrence Erlbaum Ass.
30.
Goodale, M.A. ( 2008). Action without perception in human vision. Cognitive Neuropsychology, 25(7), 891-919.
31.
Haan, B. de., Morgan, P.S., & Rorden, C. ( 2008). Covert orienting of attention and overt eye movements activate identical brain regions. Brain research, 1204, 102-111.
32.
Hernandez, D., Cabrera, J., Naranjo, A., Dominguez, A., & Isern, J. ( 2007). Gaze control in a multiple-task active-vision system . In Proceedings of the 5th International Conference on Computer Vision Systems (ICVS). Bielefeld, Germany.
33.
Hou, X., & Zhang, L. ( 2007). Saliency detection: A spectral residual approach. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 1-8). Minneapolis, MN: IEEE.
34.
Hwang, A.D., Higgins, E.C., & Pomplun, M. ( 2009). A model of top-down attentional control during visual search in complex scenes. Journal of Vision, 9(5), 1-18.
35.
Itti, L., Koch, C., & Niebur, E. ( 1998). A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11), 1254-1259.
36.
John, V., Trucco, E., & Ivekovic, S. ( 2010). Markerless human articulated tracking using hierarchical particle swarm optimisation. Image Vision Computing, 28(11), 1530-1547.
37.
Kim, D., & Moeller, R. ( 2006). Passive sensing and active sensing of a biomimetic whisker . In L. M. Rocha, L. S. Yaeger, M. A. Bedau, D. Floreano, R. L. Goldstone, & A. Vespignani (Eds.), In Proceedings of the International Conference on the Simulation and Synthesis of Living Systems (ALife X) (pp. 282-288). Bloomington: MIT Press.
38.
Koch, C., & Ullman, S. ( 1985). Shifts in selective visual attention: towards the underlying neural circuitry. Human Neurobiology, 4(4), 219-227.
39.
Kwok, C., & Fox, D. ( 2004). Reinforcement learning for sensing strategies. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (Vol 4, pp. 3158-3163). Sendai, Japan: IEEE .
40.
Land, M. ( 1999). Motion and vision: why animals move their eyes. Journal of Computational Physiology A, 185, 341-352.
41.
Liu, J., Tang, Y., & Cao, Y. ( 1997). An evolutionary autonomous agents approach to image feature extraction. IEEE Transactions on Evolutionary Computation , 1(2), 141-158.
42.
Marr, D. ( 1982). Vision: A computational investigation into the human representation and processing of visual information. New York: Henry Holt and Co., Inc.
43.
Marshall, J.A.R., Bogacz, R., Dornhaus, A., Planqué, R., Kovacs, T., & Franks, N.R. ( 2009). On optimal decision-making in brains and social insect colonies. Journal of the Royal Society Interface, 6(40), 1065-1074.
Mazouzi, S., Guessoum, Z., Michel, F., & Batouche, M. ( 2007). A multi-agent approach for range image segmentation. In Proceedings of the 5th international Central and Eastern European Conference on Multi-Agent Systems and Applications V, LNAI 4696 (Vol 4696, pp. 1-10). Leipzig, Germany: Springer-Verlag.
46.
Milner, A.D., & Goodale, M.A. ( 1995). The visual brain in action. Oxford : Oxford University Press.
47.
Minsky, M. ( 1988). The society of mind. New York: Simon & Schuster.
48.
Mobahi, H., Ahmadabadi, M.N., & Araabi, B.N. ( 2006). Swarm contours: A fast self-organization approach for snake initialization. Complexity, 12(1), 41-52.
49.
Mossio, M., & Taraborelli, D. (2008). Action-dependent perceptual invariants: From ecological to sensorimotor approaches. Consciousness and Cognition, 17(4), 1324-1340.
50.
Nabbe, B., & Hebert, M. ( 2003). Where and when to look: how to extend the myopic planning horizon. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (Vol 1, pp. 920-927). Las Vegas: IEEE.
51.
Navalpakkam, V., & Itti, L. ( 2005). Modeling the influence of task on attention. Vision Research, 45(2), 205-231.
52.
Neider, M.B., & Zelinsky, G.J. (2006). Scene context guides eye movements during visual search. Vision Research, 46(5), 614-621
53.
Nolfi, S. ( 2005). Category formation in self-organizing embodied agents . In H. Cohen, & C. Lefebvre (Eds.), Handbook of categorization in cognitive science (pp. 869-889). Oxford: Elsevier.
54.
Nolfi, S., & Marocco, D. ( 2002). Active perception: a sensorimotor account of object categorization . In B. Hallam, D. Floreano, J. Hallam, G. Hayes, & J.-A. Meyer (Eds.), In Proceedings of the 7th International Conference on Simulation of Adaptive Behavior (SAB) (pp. 266-271). MIT Press.
55.
O’Regan, J.K., & Noe, A. ( 2001). A sensorimotor account of vision and visual consciousness . Behavioral and Brain Sciences, 24, 939-1031.
56.
Owechko, Y., & Medasani, S. ( 2005). A swarm-based volition/ attention framework for object recognition. In Proceedings of the IEEE Computer Vision and Pattern Recognition Workshop (CVPRW) (Vol. 3, pp. 91-98). IEEE.
57.
Palmer, S.E. ( 1999). Vision science: Photons to phenomenology. Cambridge, MA: MIT press.
58.
Passino, K.M., Seeley, T.D., & Visscher, P.K. (2008). Swarm cognition in honey bees. Behavioral Ecology and Sociobiology, 62(3), 401-414.
59.
Patel, K., Macklem, W., Thrun, S., & Montemerlo, M. (2005). Active sensing for high-speed offroad driving. In Proceedings of the IEEE International Conference Robotics and Automation (ICRA) (pp. 3162-3168). IEEE.
60.
Pfeifer, R., & Bongard, J.C. ( 2006). How the body shapes the way we think - a new view of intelligence. Cambridge, MA: MIT Press.
61.
Pfeifer, R., & Scheier, C. ( 1999). Understanding intelligence. Cambridge, MA: MIT Press.
62.
Poli, R., & Valli, G. ( 1993). Neural inhabitants of MR and echo images segment cardiac structures. In Proceedings of the Computers in Cardiology (pp. 193-196). London: IEEE Computer Science Society.
Ramos, V., & Almeida, F. ( 2000). Artificial ant colonies in digital image habitats - a mass behavior effect study on pattern recognition. In M. Dorigo, M. Middendorf, & T. Stüzle (Eds.), In Proceedings of the 2nd International Workshop on Ant Algorithms - From Ant Colonies to Artificial Ants (ANTS) (pp. 113-116). Belgium.
65.
Rothkopf, C., Ballard, D., & Hayhoe, M. ( 2007). Task and context determine where you look. Journal of Vision, 7(14)(16), 1-20.
66.
Santana, P., & Correia, L. ( 2006). Behaviour cooperation by negation for mobile robots. In Proceedings of the IEEE International Conference on Robotics and Biomimetics, (ROBIO) (pp. 982-987). IEEE.
67.
Santana, P., & Correia, L. ( 2010). Experimental results supporting videos. In http://www.uninova.pt/∼pfs/videos_AB2010/videos.html.
68.
Santana, P., Santos, P., Correia, L., & Barata, J. ( 2008). Cross-country obstacle detection: Space-variant resolution and outliers removal. In Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS ’08) (pp. 1836-1841). IEEE.
69.
Scheier, C., Pfeifer, R., & Kuniyoshi, Y. ( 1998). Embedded neural networks: exploiting constraints. Neural Networks, (11), 1551-1596.
70.
Slocum, A., Downey, D., & Beer, R. ( 2000). Further experiments in the evolution of minimally cognitive behavior: From perceiving affordances to selective attention. In J.-A. Meyer, A. Berthoz , D. Floreano, H. Roitblat, & S. Wilson (Eds.), In Proceedings of the International Conference on Simulation of Adaptive Behavior (SAB) (Vol 6, pp. 430-439). MIT Press.
71.
Sporns, O., & Lungarella, M. (2006). Evolving coordinated behavior by maximizing information structure. In L. M. Rocha, L. S. Yaeger, M. A. Bedau, D. Floreano , R. L. Goldstone, & A. Vespignani (Eds.), In Proceedings of ALife X (pp. 323-329). Bloomington: MIT Press.
72.
Sprague, N., Ballard, D., & Robinson, A. ( 2007). Modeling embodied visual behaviors. ACM Transactions on Applied Perception, 4(2).
73.
Suzuki, M., & Floreano, D. ( 2006). Evolutionary active vision toward three dimensional landmark-navigation . In S. Nolfi, G. Baldassare, R. Calabretta, J. Hallam, D. Marocco, O. Miglino, J.-A. Meyer , & D. Parisi (Eds.), In Proceedings of the 9th International Conference on the Simulation of Adaptive Behavior (SAB) (pp. 263-273). MIT Press.
74.
Thelen, E., & Smith, L.B. ( 1996). A dynamic systems approach to the development of cognition and action. Cambridge, MA: MIT Press .
75.
Thrun, S., Burgard, W., & Fox, D. ( 2005). Probabilistic robotics (Intelligent robotics and autonomous agents). Cambridge, MA: MIT Press .
76.
Torralba, A., Murphy, K.P., Freeman, W.T., & Rubin, M.A. ( 2003). Context-based vision system for place and object recognition . In Proceedings of the IEEE International Conference on Computer Vision (ICCV) (pp. 273-280). IEEE.
77.
Treisman, A.M., & Gelade, G. ( 1980). A feature-integration theory of attention. Cognitive Psychology, 12(1), 97-136.
78.
Trianni, V., & Tuci, E. ( 2009). Annual Meeting of the Cognitive Science Society (CogSci), Workshop on Swarm Cognition. Amsterdam.
79.
Trianni, V., & Tuci, E. ( 2010). Swarm cognition and artificial life. In Proceedings of the European Conference on Artificial Life (ECAL) . (In press).
Walther, D., & Koch, C. ( 2006). Modeling attention to salient proto-objects. Neural Networks, 19, 1395-1407.
82.
Wolfe, J.M. ( 1994). Guided search 2. 0. a revised model of visual search . Psychonomic Bulletin & Review, 1(2), 202-238.
83.
Yarbus, A.L., & Haigh, B. ( 1973). Eye movements and vision. New York : Plenum press.
84.
Zhang, X., Hu, W., Maybank, S., Li, X., & Zhu, M. ( 2008). Sequential particle swarm optimization for visual tracking . In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 1-8). IEEE.
