October 2020
Volume 20, Issue 11
Open Access
Vision Sciences Society Annual Meeting Abstract  |   October 2020
Peri-saccadic attention drives saccade statistics in scene viewing
Author Affiliations & Notes
  • Lisa Schwetlick
    University of Potsdam
  • Lars O. M. Rothkegel
  • Ralf Engbert
  • Footnotes
    Acknowledgements  This work is part (Project B05) of Collaborative Research Center 1294 Data Assimilation at the University of Potsdam, funded by Deutsche Forschungsgemeinschaft
Journal of Vision October 2020, Vol.20, 700. doi:https://doi.org/10.1167/jov.20.11.700
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      Lisa Schwetlick, Lars O. M. Rothkegel, Ralf Engbert; Peri-saccadic attention drives saccade statistics in scene viewing. Journal of Vision 2020;20(11):700. https://doi.org/10.1167/jov.20.11.700.

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      © ARVO (1962-2015); The Authors (2016-present)

  • Supplements

Fixation location and locus of visual attention are usually assumed to coincide when modeling attention in natural scene viewing. However, research finds preferred processing at locations decoupled from eye position around the time of a saccade. Activation peaks occur at the upcoming location just before saccades and at the retinotopically shifted target location immediately after saccades. This anticipatory activation helps to maintain a stable percept of the visual world. Here we use a computational scan path model, to show that peri-saccadic attention has dramatic consequences on scanpath statistics. Our modeling work is based on the SceneWalk model, which implements two processing streams: saliency-based activation and fixation-based inhibitory tagging. In combination, these streams drive a priority map for saccade target selection. Here, we extend the model by adding (a) peri-saccadic attentional shifts, (b) delayed inactivation of recently fixation regions for facilitation of return, and (c) a central fixation bias. As the model is firmly theory-based, parameter values are interpretable allow evaluation of theoretical predictions. We implemented a Bayesian framework for model inference. This approach fits the model to data without relying on ad-hoc performance metrics that might overfit the model to the specific target effects. The results based on these statistically rigorous procedures capture both static effects and dynamic serial dependencies. The fitted extended model reproduces systematic tendencies in eye movement including saccade amplitude distributions, intersaccadic turning angles and their relation to fixation durations and saccade amplitudes, and distance-to-center behavior. Peri-saccadic attentional mechanisms are well-established both in experiments and neurocognitive theories of vision. The improvements we achieved in model results suggests that neurophysiological principles, in particular, the decoupling of fixation locations from attentional mechanisms around the time of saccade, play an important role in explaining saccade behavior.


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