Skip to main content
SpringerLink
Log in

Eye Movements Show Optimal Average Anticipation with Natural Dynamic Scenes

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

A less studied component of gaze allocation in dynamic real-world scenes is the time lag of eye movements in responding to dynamic attention-capturing events. Despite the vast amount of research on anticipatory gaze behaviour in natural situations, such as action execution and observation, little is known about the predictive nature of eye movements when viewing different types of natural or realistic scene sequences. In the present study, we quantify the degree of anticipation during the free viewing of dynamic natural scenes. The cross-correlation analysis of image-based saliency maps with an empirical saliency measure derived from eye movement data reveals the existence of predictive mechanisms responsible for a near-zero average lag between dynamic changes of the environment and the responding eye movements. We also show that the degree of anticipation is reduced when moving away from natural scenes by introducing camera motion, jump cuts, and film-editing.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. http://www.crcns.org/data-sets/eye/eye-1.

  2. For further information visit http://www.gazecom.eu.

References

  1. Barth E, Dorr M, Böhme M, Gegenfurtner KR, Martinetz T. Guiding the mind’s eye: improving communication and vision by external control of the scanpath. In: Rogowitz BE, Pappas TN, Daly SJ, editors. Human vision and electronic imaging, vol 6057 of Proceedings of SPIE. Invited contribution for a special session on Eye Movements, Visual Search, and Attention: a Tribute to Larry Stark; 2006.

  2. Becker W. Saccades. In: Carpenter RHS, editor. Vision & visual dysfunction, vol 8: Eye movements. London: CRC Press; 1991. p. 95–137.

    Google Scholar 

  3. Böhme M, Dorr M, Krause C, Martinetz T, Barth E. Eye movement predictions on natural videos. Neurocomputing. 2006;69(16–18):1996–2004.

    Article  Google Scholar 

  4. Carmi R, Itti L. Visual causes versus correlates of attentional selection in dynamic scenes. Vis Res. 2006;46:4333–45.

    Article  PubMed  Google Scholar 

  5. Carpenter RHS. Oculomotor procrastination. In: Fisher DF, Monty RA, Senders JW, editors. Eye movements: cognition and visual perception. Hillsdale, NJ: Lawrence Erlbaum; 1981. p. 237–46.

    Google Scholar 

  6. Chajka K, Hayhoe M, Sullivan B, Pelz J, Mennie N, Droll J. Predictive eye movements in squash. J Vis. 2006;6(6):481–6.

    Article  Google Scholar 

  7. Crundall D, Chapman P, Phelps N, Underwood G. Eye movements and hazard perception in police pursuit and emergency response driving. J Exp Psychol. 2003;9(3):163–74.

    Google Scholar 

  8. Findlay JM. Spatial and temporal factors in the predictive generation of saccadic eye movements. Vis Res. 1981;21(3):347–54.

    Article  PubMed  CAS  Google Scholar 

  9. Flanagan JR, Johansson RS. Action plans used in action observation. Nature. 2003;424(6950):769–71.

    Article  PubMed  CAS  Google Scholar 

  10. Gesierich B, Bruzzo A, Ottoboni G, Finos L. Human gaze behaviour during action execution and observation. Acta Psychol. 2008;128(2):324–30.

    Article  Google Scholar 

  11. Goldstein RB, Woods RL, Peli E. Where people look when watching movies: do all viewers look at the same place?. Comput Biol Med. 2007;3(7):957–64.

    Article  Google Scholar 

  12. Gonzalez RC, Woods RE. Digital image processing, 2nd edn. Boston, MA: Addison-Wesley Longman Publishing Co., Inc; 2001.

    Google Scholar 

  13. Hayhoe M, Ballard D. Eye movements in natural behavior. Trends Cogn Sci. 2005;9(4):188–94.

    Article  PubMed  Google Scholar 

  14. Itti L. Automatic foveation for video compression using a neurobiological model of visual attention. IEEE Trans Image Process. 2004;13(10):1304–18.

    Article  PubMed  Google Scholar 

  15. Itti L. Quantifying the contribution of low-level saliency to human eye movements in dynamic scenes. Visual Cogn. 2005;12(6):1093–123.

    Article  Google Scholar 

  16. Itti L, Koch C, Niebur E. A model of saliency-based visual attention for rapid scene analysis. IEEE Trans Pattern Anal Mach Intell. 1998;20(11):1254–9.

    Article  Google Scholar 

  17. Koch C, Ullman S. Shifts in selective visual attention: towards the underlying neural circuitry. Hum Neurobiol. 1985;4(4):219–27.

    PubMed  CAS  Google Scholar 

  18. Land MF, Furneaux S. The knowledge base of the oculomotor system. Philos Trans R Soc B Biol Sci. 1997;352:1231–9.

    Article  CAS  Google Scholar 

  19. Land MF, Hayhoe M. In what ways do eye movements contribute to everyday activities?. Vis Res. 2001;41:3559–65.

    Article  PubMed  CAS  Google Scholar 

  20. Land MF, Lee DN. Where we look when we steer. Nature. 1994;369:742–4.

    Article  PubMed  CAS  Google Scholar 

  21. Land MF, McLeod P. From eye movements to actions: how batsmen hit the ball. Nat Neurosci. 2000;3:1340–5.

    Article  PubMed  CAS  Google Scholar 

  22. Land MF, Mennie N, Rusted J. The roles of vision and eye movements in the control of activities of daily living. Perception. 1999;28:1311–28.

    Article  PubMed  CAS  Google Scholar 

  23. Mennie N, Hayhoe M, Sullivan B. Look-ahead fixations: anticipatory eye movements in natural tasks. Exp Brain Res. 2007;179(3):427–42.

    Article  PubMed  Google Scholar 

  24. Pelz JB, Canosa R. Oculomotor behavior and perceptual strategies in complex tasks. Vis Res. 2001;41:3587–96.

    Article  PubMed  CAS  Google Scholar 

  25. Reinagel P, Zador AM. Natural scene statistics at the centre of gaze. Network Comput Neural Syst. 1999;10:341–50.

    Article  CAS  Google Scholar 

  26. Russo FD, Pitzalis S, Spinelli D. Fixation stability and saccadic latency in élite shooters. Vis Res. 2003;43(17):1837–45.

    Article  PubMed  Google Scholar 

  27. Smit AC, Van Gisbergen JAM. A short-latency transition in saccade dynamics during square-wave tracking and its significance for the differentiation of visually-guided and predictive saccades. Exp Brain Res. 1989;76:64–74.

    Article  PubMed  CAS  Google Scholar 

  28. Smith TJ, Henderson JM. Edit blindness: the relationship between attention and global change blindness in dynamic scenes. J Eye Movement Res. 2008;2(2):1–17.

    CAS  Google Scholar 

  29. ’t Hart BM, Vockeroth J, Schumann F, Bartl K, Schneider E, König P, Einhäuser W. Gaze allocation in natural stimuli: comparing free exploration to head-fixed viewing conditions. Visual Cogn. 2009;17(6/7):1132–58.

    Article  Google Scholar 

  30. Tatler BW, Baddeley RJ, Gilchrist ID. Visual correlates of fixation selection: effects of scale and time. Vis Res. 2005;45:643–59.

    Article  PubMed  Google Scholar 

  31. Underwood G, Phelps N, Wright C, van Loon E, Galpin A. Eye fixation scanpaths of younger and older drivers in a hazard perception task. Ophthal Physiol Opt. 2005;25:346–56.

    Article  Google Scholar 

  32. Vig E, Dorr M, Barth E. Efficient visual coding and the predictability of eye movements on natural movies. Spat Vis 2009;22(5):397–408.

    Article  PubMed  Google Scholar 

  33. Vig E, Dorr M, Martinetz T, Barth E. A learned saliency predictor for dynamic natural scenes. In: Diamantaras K, Duch W, Iliadis LS, editors. ICANN 2010, Part III, LNCS 6354, Berlin: Springer; 2010. p. 52–61.

  34. Wooding DS. Eye movements of large populations: II. Deriving regions of interest, coverage, and similarity using fixation maps. Behav Res Methods Instruments Comput. 2002;34(4):518–28.

    Article  Google Scholar 

Download references

Acknowledgements

We would like to thank Karl Gegenfurtner: data were collected in his lab at the Dept. of Psychology of Giessen University. Our research has received funding from the European Commission within the project GazeCom (contract no. IST-C-033816, http://www.gazecom.eu) of the 6th Framework Programme. All views expressed herein are those of the authors alone; the European Community is not liable for any use made of the information.

Author information

Authors and Affiliations

  1. Institute for Neuro- and Bioinformatics, University of Lübeck, Ratzeburger Allee 160, 23538, Lübeck, Germany

    Eleonora Vig, Michael Dorr, Thomas Martinetz & Erhardt Barth

  2. Schepens Eye Research Institute, Department of Ophthalmology, Harvard Medical School, 20 Staniford Street, Boston, MA, 02114, USA

    Michael Dorr

Authors
  1. Eleonora Vig
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael Dorr
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thomas Martinetz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Erhardt Barth
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eleonora Vig.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Vig, E., Dorr, M., Martinetz, T. et al. Eye Movements Show Optimal Average Anticipation with Natural Dynamic Scenes. Cogn Comput 3, 79–88 (2011). https://doi.org/10.1007/s12559-010-9061-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-010-9061-4

Keywords

Search

Navigation