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Modelling Task-Dependent Eye Guidance to Objects in Pictures

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Abstract

We introduce a model of attentional eye guidance based on the rationale that the deployment of gaze is to be considered in the context of a general action-perception loop relying on two strictly intertwined processes: sensory processing, depending on current gaze position, identifies sources of information that are most valuable under the given task; motor processing links such information with the oculomotor act by sampling the next gaze position and thus performing the gaze shift. In such a framework, the choice of where to look next is task-dependent and oriented to classes of objects embedded within pictures of complex scenes. The dependence on task is taken into account by exploiting the value and the payoff of gazing at certain image patches or proto-objects that provide a sparse representation of the scene objects. The different levels of the action-perception loop are represented in probabilistic form and eventually give rise to a stochastic process that generates the gaze sequence. This way the model also accounts for statistical properties of gaze shifts such as individual scan path variability. Results of the simulations are compared either with experimental data derived from publicly available datasets and from our own experiments.

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Acknowledgments

The authors are grateful to the Referees and the Associate Editor, for their enlightening and valuable comments that have greatly improved the quality and clarity of an earlier version of this paper. This work was partially supported by the Spanish projects TIN2011-24631, TIN2009-14633-C03-03, CONSOLIDER INGENIO CSD2007-00018 and the fellowships RYC-2009-05031 and 2009FIB00020. With support from the Commission for Universities and Research Department for Innovation, Universities and Enterprise of the Generalitat of Catalonia and the European Social Fund.

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Correspondence to Giuseppe Boccignone.

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Clavelli, A., Karatzas, D., Lladós, J. et al. Modelling Task-Dependent Eye Guidance to Objects in Pictures. Cogn Comput 6, 558–584 (2014). https://doi.org/10.1007/s12559-014-9262-3

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Keywords

  • Visual attention
  • Gaze guidance
  • Value
  • Payoff
  • Stochastic fixation prediction