Abstract
Human Attention Allocation Strategy (HAAS) is related closely to operating performance when he/she is interacting a machine through a human-machine interface. Gaze behaviors, which is acquisited by eye tracking technology, can be used to observe attention allocation. But the performance-sensitive attention allocation strategy is still hard to measure using gaze cue. In this paper, we attempt to understand visual attention allocation behavior and reveal the relationship between attention allocation strategy and interactive performance in a quantitative manner. By using a novel Multiple-Level Clustering approach, we give some results on probabilistic analysis about interactive performance of HAAS patterns in a simulation platform of thermal-hydraulic process plant. It can be observed that these patterns are sensitive to interactive performance. We conclude that our Multiple-Level Clustering approach can extract efficiently human attention allocation patterns and evaluate interactive performance using gaze movements.
Project supported by the National Nature Science Foundation of China (No. 61471252) and the Natural Science Foundation of Jiangsu Province (No. BK20130303).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Gonalves, M.G., Luiz, A.A.F., Oliveira, R.A., Grupen, D.S., Wheeler, A.H.F.: Tracing patterns and attention: Humanoid robot cognition. IEEE Intell. Syst. Mag. 15, 70–75 (2000)
Gabaix, X., Laibson, D.I., Moloche, G., Weinberg, S.E.: The allocation of attention: theory and evidence. MIT Department of Economics Working Paper No. 03–31 (2003)
Admoni, H., Dragan, A., Srinivasa, S.S., Scassellati, B.: Deliberate delays during robot-to-human handovers improve compliance with gaze communication, pp. 49–56 (2014)
Lin, Y., Zhang, W.J., Wu, C., Yang, G., Dy, J.: A fuzzy logics clustering approach to computing human attention allocation using eyegaze movementcue. Int. J. Hum. Comput. Stud. 67(5), 455–463 (2009)
Brown, R., Pham, B., Maeder, A.: Visual importance-biased image synthesis animation. In: Proceedings of the 1st International Conference on Computer Graphics and Interactive Techniques, New York, pp. 63–70 (2003)
Wu, C.: Extracting performance-related attention pattern for human-computer interaction. J. Comput. Inf. Syst. 10(17) (2014)
Harr, R.: Cognitive Science. Sage Publications, New Jersey (2002)
Wu, C., Lin, Y., Zhang, W.J.: Human attention modeling in a human-machine interface based on the incorporation of contextual features in a Bayesian network. In: Proceedings - IEEE International Conference on Systems, Man and Cybernetics, vol. 1, pp. 760–766 (2005)
Horvitz, E., Kadie, C., Paek, T., Hovel, D.: Models of attention in computing and communication: from principles to applications. Commun. ACM 46(3), 52–59 (2003)
Hoque, M.M., Onuki, T., Kobayashi, Y., Kuno, Y.: Effect of robots gaze behaviors for attracting and controlling human attention. Adv. Robot. 27(11), 813–829 (2013)
Erkelens, C.J., Vogels, I.: The initial direction and landing position of saccades. In: Findlay, J.M., Walker, R., Kentridge, R.W. (eds.) Eye Movement Research: Mechanisms, Processes, and Applications, pp. 133–144. Elsevier Science Publishing, New York (1995)
Asteriadis, S., Karpouzis, K., Kollias, S.: Visual focus of attention in non-calibrated environments using gaze estimation. Int. J. Comput. Vis. 107(3), 293–316 (2014)
Sorostinean, M., Ferland, F., Dang, T.-H.-H., Tapus, A.: Motion-oriented attention for a social gaze robot behavior. In: Beetz, M., Johnston, B., Williams, M.-A. (eds.) ICSR 2014. LNCS, vol. 8755, pp. 310–319. Springer, Heidelberg (2014)
Schilling, H.E.H., Rayner, K., Chumbley, J.I.: Comparing naming, lexical decision, and eye fixation times: word frequency effects and individual differences. Mem. Cognit. 26, 1270–1281 (1998)
Pomarjanschi, L., Dorr, M., Bex, P.J., Barth, E.: Simple gaze-contingent cues guide eye movements in a realistic driving simulator, vol. 8651 (2013)
Pejsa, T., Andrist, S., Gleicher, M., Mutlu, B.: Gaze and attention management for embodied conversational agents. ACM Trans. Interact. Intell. Syst. 5, 3 (2015)
Suppes, P.: Eye-movement models for arithmetic and reading performance. In: Kowler, E. (ed.) Eye Movements and their Role in Visual and Cognitive Processes, pp. 455–477. Elsevier Science Publishing, New York (1990)
Hegarty, M., Mayer, R.E., Green, C.E.: Comprehension of arithmetic word problems: evidence from students eye fixations. J. Educ. Psychol. 84, 76–84 (1992)
Bampatzia, S., Vouloutsi, V., Grechuta, K., Lallée, S., Verschure, P.F.M.J.: Effects of gaze synchronization in human-robot interaction. In: Duff, A., Lepora, N.F., Mura, A., Prescott, T.J., Verschure, P.F.M.J. (eds.) Living Machines 2014. LNCS, vol. 8608, pp. 370–373. Springer, Heidelberg (2014)
Michael, E.J., Kim, J.V.: Attention allocation within the abstraction hierarchy. Int. J. Hum. Comput. Stud. 48, 521–545 (1992)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media Singapore
About this paper
Cite this paper
Wu, C., Xie, F., Yan, C. (2016). Recognizing Visual Attention Allocation Patterns Using Gaze Behaviors. In: Che, W., et al. Social Computing. ICYCSEE 2016. Communications in Computer and Information Science, vol 623. Springer, Singapore. https://doi.org/10.1007/978-981-10-2053-7_49
Download citation
DOI: https://doi.org/10.1007/978-981-10-2053-7_49
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-2052-0
Online ISBN: 978-981-10-2053-7
eBook Packages: Computer ScienceComputer Science (R0)