September 2018
Volume 18, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2018
Assessing the dynamic visual processing of informative local features with eye movements
Author Affiliations
  • Anna Montagnini
    Institut de Neurosciences de la Timone, Aix-Marseille Universitè & CNRS , France
  • Anna Paola Benini
    Institut de Neurosciences de la Timone, Aix-Marseille Universitè & CNRS , FranceDipartimento NEUROFARBA, Università degli Studi di Firenze, Italy
  • Maria Del Viva
    Dipartimento NEUROFARBA, Università degli Studi di Firenze, Italy
Journal of Vision September 2018, Vol.18, 1211. doi:10.1167/18.10.1211
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      Anna Montagnini, Anna Paola Benini, Maria Del Viva; Assessing the dynamic visual processing of informative local features with eye movements. Journal of Vision 2018;18(10):1211. doi: 10.1167/18.10.1211.

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

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Abstract

Visually salient features embedded in synthetic structured images typically attract a rapid foveating saccade even under very challenging visual conditions. However, a general definition of saliency, as well as its role for natural active vision are still matter of debate. Here we chose a specific set of local features, predicted by a constrained maximum-entropy model to be optimal information carriers (DelViva et al. 2013), as candidate salient features. These local patterns are spatial arrangements of 3x3 black and white pixels (about 9 arcmin of size). At each trial we randomly selected 10 patterns for the target stimulus (s of them being classified as salient, with s=1,4,6 or 10) and 10 non-salient patterns for the distractor. In a choice saccadic experiment we randomly presented target and distractor for 26ms on the right and left side of the screen respectively, at 5° eccentricity from the central fixation and at different angles (0°, ±45°; ±75°) with respect to the horizontal meridian. We recorded human participants' eye movements while they were asked to perform a saccade towards the most salient pattern. We estimated the oculometric target-selection curves based on the landing position of the first and second saccade with respect to the target and evaluated saccadic choice performance with respect to saccadic latencies. In addition we analyzed saccadic curvature as a possible landmark for an automatic capture of salient patterns. Results point to a dynamic evolution of oculomotor selection with a fast but imperfect attraction of salient patterns and a further refinement resulting in a more accurate second saccade for the highest values of signal to noise ratio. When analyzing the first saccade in more detail, choice accuracy improved with saccadic latency only for the highest SNR values, whereas saccadic curvature was slightly biased toward the non-targeted visual stimulus, regardless of its saliency.

Meeting abstract presented at VSS 2018

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