September 2024
Volume 24, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2024
Spatiotemporal regularities guide motor predictions in a dynamic visual search
Author Affiliations & Notes
  • Nir Shalev
    Haifa University, Israel
    University of Oxford, UK
  • Noam Tzionit
    Hebrew University of Jerusalem, IL
  • Danielle Filmon
    Hebrew University of Jerusalem, IL
  • Anna C. Nobre
    University of Oxford, UK
    Yale University, USA
  • Ayelet N. Landau
    Hebrew University of Jerusalem, IL
  • Footnotes
    Acknowledgements  ANL: JSMF Scholar Award, ISF (958/16 & 1899/21), TIMECODE ERC Starting Grant (852387), Joy Ventures, and the Product Academy Award. NS: Daniel Turnberg Fellowship, Academy of Medical Sciences. ACN: Wellcome Investigator Award (104571/Z/14/Z) and the JSMF Collaborator Award (220020448)
Journal of Vision September 2024, Vol.24, 432. doi:https://doi.org/10.1167/jov.24.10.432
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      Nir Shalev, Noam Tzionit, Danielle Filmon, Anna C. Nobre, Ayelet N. Landau; Spatiotemporal regularities guide motor predictions in a dynamic visual search. Journal of Vision 2024;24(10):432. https://doi.org/10.1167/jov.24.10.432.

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

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Abstract

Attention allows us to prioritise relevant information and ignore distraction in our sensory environment. Since natural scenes are constantly changing, it is important for us to adapt our attentional priorities accordingly. Predictable signals, like traffic lights, allow for anticipation and help us control attention in time and space. In this study, we explore how prediction-led attention affects how we guide the motor and oculomotor system in time and space. We used a dynamic variation of a visual search task, with trials lasting 14 seconds. Each trial included eight targets that faded in and out of the display, among visual distractors. Participants moved their eyes freely and used the mouse pointer to click on the targets. Critically, we embedded in each trial spatiotemporal regularities by presenting four out of eight targets at the same time and approximate location throughout the experiment. The remaining four targets could appear at any time and location. We also manipulated the distraction load by varying the number of irrelevant stimuli appearing in each trial. Our results offer a detailed description of the learning dynamics and prediction formation. Participants were faster and more accurate at detecting predictable targets compared to unpredictable ones. In line with the visual search literature, we also found that increasing the number of visual distractors reduced accuracy and slowed down responses. By tracking mouse and eye movements, we discovered that predictions enabled earlier and faster movements towards targets. Interestingly, we also observed earlier and more pronounced movements of the hand and eyes away from predictable targets once they were selected. These findings enhance our understanding of the real-time impact of prediction formation. In our presentation, we will provide a detailed description of these patterns under varying levels of visual distraction and discuss how they emerge during the task as a consequence of learning.

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