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Seeing via Miniature Eye Movements: A Dynamic Hypothesis for Vision

Overview of attention for article published in Frontiers in Computational Neuroscience, January 2012
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

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6 X users
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1 Wikipedia page

Citations

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82 Dimensions

Readers on

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146 Mendeley
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2 CiteULike
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Title
Seeing via Miniature Eye Movements: A Dynamic Hypothesis for Vision
Published in
Frontiers in Computational Neuroscience, January 2012
DOI 10.3389/fncom.2012.00089
Pubmed ID
Authors

Ehud Ahissar, Amos Arieli

Abstract

During natural viewing, the eyes are never still. Even during fixation, miniature movements of the eyes move the retinal image across tens of foveal photoreceptors. Most theories of vision implicitly assume that the visual system ignores these movements and somehow overcomes the resulting smearing. However, evidence has accumulated to indicate that fixational eye movements cannot be ignored by the visual system if fine spatial details are to be resolved. We argue that the only way the visual system can achieve its high resolution given its fixational movements is by seeing via these movements. Seeing via eye movements also eliminates the instability of the image, which would be induced by them otherwise. Here we present a hypothesis for vision, in which coarse details are spatially encoded in gaze-related coordinates, and fine spatial details are temporally encoded in relative retinal coordinates. The temporal encoding presented here achieves its highest resolution by encoding along the elongated axes of simple-cell receptive fields and not across these axes as suggested by spatial models of vision. According to our hypothesis, fine details of shape are encoded by inter-receptor temporal phases, texture by instantaneous intra-burst rates of individual receptors, and motion by inter-burst temporal frequencies. We further describe the ability of the visual system to readout the encoded information and recode it internally. We show how reading out of retinal signals can be facilitated by neuronal phase-locked loops (NPLLs), which lock to the retinal jitter; this locking enables recoding of motion information and temporal framing of shape and texture processing. A possible implementation of this locking-and-recoding process by specific thalamocortical loops is suggested. Overall it is suggested that high-acuity vision is based primarily on temporal mechanisms of the sort presented here and low-acuity vision is based primarily on spatial mechanisms.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 146 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 4%
Germany 3 2%
United Kingdom 2 1%
Switzerland 1 <1%
France 1 <1%
Norway 1 <1%
Russia 1 <1%
Canada 1 <1%
Unknown 130 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 45 31%
Researcher 26 18%
Student > Master 15 10%
Professor > Associate Professor 10 7%
Student > Bachelor 6 4%
Other 24 16%
Unknown 20 14%
Readers by discipline Count As %
Neuroscience 36 25%
Agricultural and Biological Sciences 24 16%
Psychology 20 14%
Engineering 15 10%
Physics and Astronomy 7 5%
Other 20 14%
Unknown 24 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 February 2020.
All research outputs
#5,472,051
of 22,685,926 outputs
Outputs from Frontiers in Computational Neuroscience
#240
of 1,336 outputs
Outputs of similar age
#48,748
of 244,123 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#15
of 69 outputs
Altmetric has tracked 22,685,926 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,336 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one has done well, scoring higher than 81% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 244,123 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 79% of its contemporaries.
We're also able to compare this research output to 69 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.