Skip to main content
Log in

A Dynamic Neural Field Approach to the Covert and Overt Deployment of Spatial Attention

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

The visual exploration of a scene involves the interplay of several competing processes (for example to select the next saccade or to keep fixation) and the integration of bottom-up (e.g. contrast) and top-down information (the target of a visual search task). Identifying the neural mechanisms involved in these processes and in the integration of these information remains a challenging question. Visual attention refers to all these processes, both when the eyes remain fixed (covert attention) and when they are moving (overt attention). Popular computational models of visual attention consider that the visual information remains fixed when attention is deployed while the primates are executing around three saccadic eye movements per second, changing abruptly this information. We present in this paper a model relying on neural fields, a paradigm for distributed, asynchronous and numerical computations and show that covert and overt attention can emerge from such a substratum. We identify and propose a possible interaction of four elementary mechanisms for selecting the next locus of attention, memorizing the previously attended locations, anticipating the consequences of eye movements and integrating bottom-up and top-down information in order to perform a visual search task with saccadic eye movements.

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

Access this article

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
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Ballard D, Hayhoe M, Pook P, Rao R. Deictic codes for the embodiment of cognition. Behav Brain Sci. 1997;20(4): 723–42; discussion 743–67.

  2. Alexandre F. Cortical basis of communication: local computation, coordination, attention. Neural Netw. 2009;22(2):126–33.

    Article  PubMed  Google Scholar 

  3. Findlay J, Walker R. A model of saccade generation based on parallel processing and competitive inhibition. Behav Brain Sci. 1999;22(4):661–74.

    PubMed  CAS  Google Scholar 

  4. Kramer A, Irwin D, Theeuwes J, Hahn S. Oculomotor capture by abrupt onsets reveals concurrent programming of voluntary and involuntary saccades. Behav Brain Sci. 1999;22:689–90.

    Article  Google Scholar 

  5. Godijn R, Theeuwes J. Programming of endogenous and exogenous saccades: evidence for a competitive integration model. J Exp Psychol Hum Percept Perform. 2002;28(5):1039–54.

    Article  PubMed  Google Scholar 

  6. Isa T Intrinsic processing in the mammalian superior colliculus. Curr Opin Neurobiol. 2002;12(6):668–77.

    Article  PubMed  CAS  Google Scholar 

  7. 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 

  8. Itti L, Koch C. Computational modeling of visual attention. Nat Rev Neurosci. 2001;2(3):194–203.

    Article  PubMed  CAS  Google Scholar 

  9. Cutsuridis V. A cognitive model of saliency, attention, and picture scanning. Cogn Comput. 2009;1:292–99.

    Article  Google Scholar 

  10. Trappenberg T, Dorris M, Munoz D, Klein R. A model of saccade initiation based on the competitive integration of exogenous and endogenous signals in the superior colliculus. J Cogn Neurosci. 2001;13(2):256–71.

    Article  PubMed  CAS  Google Scholar 

  11. Schneider S, Erlhagen W. A neural field model for saccade planning in the superior colliculus: speed-accuracy tradeoff in the double-target paradigm. Neurocomputing. 2002;44–46:623–28.

    Article  Google Scholar 

  12. Johnson J, Spencer J, Schoner G. Moving to higher ground: The dynamic field theory and the dynamics of visual cognition. New Ideas Psychol. 2008;26(2):227–51.

    Article  PubMed  Google Scholar 

  13. Faubel C, Schoner G. Learning to recognize objects on the fly: a neurally based dynamic field approach. Neural Netw. 2008;21(4):562–76.

    Article  PubMed  Google Scholar 

  14. Deco G, Rolls E. A neurodynamical cortical model of visual attention and invariant object recognition. Vision Res. 2004;44(6):621–42.

    Article  PubMed  Google Scholar 

  15. Rougier N, Fix J. Dana,distributed asynchronous numerical and adaptive modeling framework Frontiers in Neuroinformatics submitted.

  16. Goodale M, Milner A. Separate visual pathways for perception and action. Trends Neurosci. 1992;15(1):20–5.

    Article  PubMed  CAS  Google Scholar 

  17. Reynolds J, Chelazzi L. Attentional modulation of visual processing. Annu Rev Neurosci. 2004;27:611–47.

    Article  PubMed  CAS  Google Scholar 

  18. Posner M, Cohen Y. Attention and performance X, Lawrence Epblaum Associates, 1984, Ch. Components of visual orienting, pp. 531–56

  19. Wilson H, Cowan J. A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue. Kybernetik. 1973;13(2):55–80.

    Article  PubMed  CAS  Google Scholar 

  20. Amari S. Dynamics of pattern formation in lateral-inhibition type neural fields. Biol. Cybern. 1977;27(2):77–87.

    Article  PubMed  CAS  Google Scholar 

  21. Taylor J. Neural bubble dynamics in two dimensions. Biol Cybern. 1999;80:5167–74.

    Article  Google Scholar 

  22. Coombes S. Waves, bumps, and patterns in neural field theories. Biol Cybern. 2005;93(2):91–108.

    Article  PubMed  CAS  Google Scholar 

  23. Erlhagen W, Schoener G. Dynamic field theory of movement preparation. Psychol Rev. 2002;109(3):545–72.

    Article  PubMed  Google Scholar 

  24. Erlhagen W, Bicho E (2006) The dynamic neural field approach to cognitive robotics. J Neural Eng 3(3):R36–54

    Article  PubMed  Google Scholar 

  25. Rougier N, Vitay J. Emergence of attention within a neural population. Neural Netw. 2006;19(5):573–81.

    Article  Google Scholar 

  26. Sauser E, Billard A. Dynamic updating of distributed neural representations using forward models. Biol Cybern. 2006;95(6):567–88.

    Article  PubMed  Google Scholar 

  27. Gurney K, Prescott T, Redgrave P. A computational model of action selection in the basal ganglia. i. a new functional anatomy. Biol Cybern. 2001;84(6):401–10.

    Article  PubMed  CAS  Google Scholar 

  28. Vitay J, Rougier N. Using neural dynamics to switch attention, in: International Joint Conference on Neural Networks (IJCNN 2005) (2005).

  29. Kopecz K, Schoner G. Saccadic motor planning by integrating visual information and pre-information on neural dynamic fields. Biol Cybern. 1995;73(1):49–60.

    Article  PubMed  CAS  Google Scholar 

  30. Johnson J, Spencer J, Luck S, Schoner G. A dynamic neural field model of visual working memory and change detection. Psychol Sci. 2009;20(5):568–77.

    Article  PubMed  Google Scholar 

  31. Fix J, Vitay J, Rougier N. A distributed computational model of spatial memory anticipation during a visual search task. In: Butz M, Sigaud O, Baldassarre G, Pezzulo G (eds) Anticipatory Behavior in Adaptive Learning Systems: From Brains to Individual and Social Behavior, Vol. 4520 of LNCS. Springer, 2007;pp. 170–88.

  32. Alexandre F, Guyot F. Neurobiological inspiration for the architecture and functioning of cooperating neural networks. In: IWANN 1995, 1995;pp. 24–30.

  33. Zhang K. Representation of spatial orientation by the intrinsic dynamics of the head-direction cell ensemble: a theory. J Neurosci. 1996;16(6):2112–26.

    PubMed  CAS  Google Scholar 

  34. Pouget A, Sejnowski T. Spatial transformations in the parietal cortex using basis functions. J Cogn Neurosci. 1997;9:222–37.

    Article  Google Scholar 

  35. Andersen R, Essick G, Siegel R. Encoding of spatial location by posterior parietal neurons. Science. 1985;230(4724):456–8

    Article  PubMed  CAS  Google Scholar 

  36. Salinas E, Thier P. Gain modulation: a major computational principle of the central nervous system. Neuron. 2000;27(1):15–21.

    Article  PubMed  CAS  Google Scholar 

  37. Stringer S, Trappenberg T, Rolls E, Araujo I. Self-organizing continuous attractor networks and path integration: one-dimensional models of head direction cells. Netw Comput Neural Syst. 2002;13(2):217–42.

    CAS  Google Scholar 

  38. Stringer S, Rolls E, Trappenberg T. Self-organizing continuous attractor networks with multiple activity packets, and the representation of space. Neural Netw. 2004;17:5–27.

    Article  PubMed  CAS  Google Scholar 

  39. Weber C, Wermeter S. A self-organizing map of sigma-pi units. Neurocomputing. 2007;70:2552–60.

    Article  Google Scholar 

  40. Tononi G, Sporns O, Edelman G. Reentry and the problem of integrating multiple cortical areas: simulation of dynamic integration in the visual system. Cereb Cortex. 1992;2(4):310–35.

    Article  PubMed  CAS  Google Scholar 

  41. Hamker F. The reentry hypothesis: the putative interaction of the frontal eye field, ventrolateral prefrontal cortex, and areas V4, IT for attention and eye movement. Cereb Cortex. 2005;15(4):431–47.

    Article  PubMed  Google Scholar 

  42. Deco G, Lee T. A unified model of spatial and object attention based on inter-cortical biased competition. Neurocomputing. 2002;44-46:775–81.

    Article  Google Scholar 

  43. Hamker F. The emergence of attention by population-based inference and its role in distributed processing and cognitive control of vision. Comput Vis Image Underst. 2005;100:64–106.

    Article  Google Scholar 

  44. Frintrop S. VOCUS: A Visual Attention System for Object Detection and Goal-directed Search, Vol. 3899 of Lecture Notes in Computer Science, Springer; 2006.

  45. Riesenhuber M, Poggio T. Hierarchical models of object recognition in cortex. Nat Neurosci. 1999;2(11):1019–25.

    Article  PubMed  CAS  Google Scholar 

  46. Moran J, Desimone R. Selective attention gates visual processing in the extrastriate cortex. Science. 1985;229(4715):782–4.

    Article  PubMed  CAS  Google Scholar 

  47. Desimone R, Duncan J. Neural mechanisms of selective visual attention. Ann Rev Neurosci. 1995;18:193–222.

    Article  PubMed  CAS  Google Scholar 

  48. Reynolds J, Chelazzi L, Desimone R. Competitive mechanisms subserve attention in macaque areas v2 and v4. J Neurosci. 1999;19(5):1736–53.

    PubMed  CAS  Google Scholar 

  49. Shipp S. The brain circuitry of attention. Trends Cogn Sci. 2004;8(5):223–30.

    Article  PubMed  Google Scholar 

  50. Lynch J, Tian J-R. Cortico-cortical networks and cortico-subcortical loops for the higher control of eye movements. Prog Brain Res. 2005;151:461–501.

    Article  Google Scholar 

  51. Hikosaka O, Takikawa Y, Kawagoe R. Role of the basal ganglia in the control of purposive saccadic eye movements. Physiol Rev. 2000;80(3):953–78.

    PubMed  CAS  Google Scholar 

  52. Robinson D, Petersen S. The pulvinar and visual salience. Trends Neurosci. 1992;15(4):127–32.

    Article  PubMed  CAS  Google Scholar 

  53. Zhaoping L. A saliency map in primary visual cortex. Trends Cogn Sci. 2002;6(1):9–16.

    Article  Google Scholar 

  54. Gottlieb J, Kusunoki M, Goldberg M. The representation of visual salience in monkey parietal cortex. Nature. 1998;391(6666):481–4.

    Article  PubMed  CAS  Google Scholar 

  55. Thompson K, Bichot N. A visual salience map in the primate frontal eye field. Prog Brain Res. 2005;147:251–62.

    PubMed  Google Scholar 

  56. Rockland K, VanHoesen G. Direct temporal-occipital feedback connections to striate cortex (V1) in the macaque monkey. Cereb Cortex. 1994;4(3):300–13.

    Article  PubMed  CAS  Google Scholar 

  57. Moore T, Armstrong K. Selective gating of visual signals by microstimulation of frontal cortex. Nature. 2003;421(6921):370–3.

    Article  PubMed  CAS  Google Scholar 

  58. Hikosaka O. Basal ganglia mechanisms of reward-oriented eye movement. Ann N Y Acad Sci. 2007;1104:229–49.

    Article  PubMed  CAS  Google Scholar 

  59. Funahashi S, Bruce C, Goldman-Rakic P. Mnemonic coding of visual space in the monkey’s dorsolateral prefrontal cortex. J of Neurophysiol. 1999;61(2):331–49.

    Google Scholar 

  60. Constantinidis C, Wang X. A neural circuit basis for spatial working memory. Neuroscientist. 2004;10(6):553–65.

    Article  PubMed  Google Scholar 

  61. Watanabe Y, Funahashi S. Neuronal activity throughout the primate mediodorsal nucleus of the thalamus during oculomotor delayed-responses. I. cue-, delay- and response-period activity. J Neurophysiol. 2004;92(3):1738–55.

    Article  PubMed  Google Scholar 

  62. Watanabe Y, Funahashi S. Neuronal activity throughout the primate mediodorsal nucleus of the thalamus during oculomotor delayed-responses. II. Activity encoding visual versus motor signal. J Neurophysiol. 2004;92(3):1756–69.

    Article  PubMed  Google Scholar 

  63. Sommer M, Wurtz R. Influence of the thalamus on spatial visual processing in frontal cortex. Nature. 2006;444(7117):374–7.

    Article  PubMed  CAS  Google Scholar 

  64. Duhamel J, Colby C, Goldberg M. The updating of the representation of visual space in parietal cortex by intended eye movements. Science. 1992;255(5040):90–2.

    Article  PubMed  CAS  Google Scholar 

  65. Quaia C, Optican L, Goldberg M. The maintenance of spatial accuracy by the perisaccadic remapping of visual receptive fields. Neural Netw. 1998;11(7–8):1229–40.

    Article  PubMed  Google Scholar 

  66. Rizzolatti G, Riggio L, Dascola I, Umiltá C. Reorienting attention across the horizontal and vertical meridians: evidence in favor of a premotor theory of attention. Neuropsychologia. 1987;25(1A):31–40.

    Article  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jeremy Fix.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Fix, J., Rougier, N. & Alexandre, F. A Dynamic Neural Field Approach to the Covert and Overt Deployment of Spatial Attention. Cogn Comput 3, 279–293 (2011). https://doi.org/10.1007/s12559-010-9083-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-010-9083-y

Keywords

Navigation