Literature DB >> 34417308

Oculo-retinal dynamics can explain the perception of minimal recognizable configurations.

Liron Zipora Gruber1, Shimon Ullman2, Ehud Ahissar3.   

Abstract

Natural vision is a dynamic and continuous process. Under natural conditions, visual object recognition typically involves continuous interactions between ocular motion and visual contrasts, resulting in dynamic retinal activations. In order to identify the dynamic variables that participate in this process and are relevant for image recognition, we used a set of images that are just above and below the human recognition threshold and whose recognition typically requires >2 s of viewing. We recorded eye movements of participants while attempting to recognize these images within trials lasting 3 s. We then assessed the activation dynamics of retinal ganglion cells resulting from ocular dynamics using a computational model. We found that while the saccadic rate was similar between recognized and unrecognized trials, the fixational ocular speed was significantly larger for unrecognized trials. Interestingly, however, retinal activation level was significantly lower during these unrecognized trials. We used retinal activation patterns and oculomotor parameters of each fixation to train a binary classifier, classifying recognized from unrecognized trials. Only retinal activation patterns could predict recognition, reaching 80% correct classifications on the fourth fixation (on average, ∼2.5 s from trial onset). We thus conclude that the information that is relevant for visual perception is embedded in the dynamic interactions between the oculomotor sequence and the image. Hence, our results suggest that ocular dynamics play an important role in recognition and that understanding the dynamics of retinal activation is crucial for understanding natural vision.

Entities:  

Keywords:  active vision; closed-loop perception; eye movements; fixational drift; neural code

Mesh:

Year:  2021        PMID: 34417308      PMCID: PMC8403836          DOI: 10.1073/pnas.2022792118

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  62 in total

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Authors:  Bence P Olveczky; Stephen A Baccus; Markus Meister
Journal:  Nature       Date:  2003-05-11       Impact factor: 49.962

2.  Similarity of superior colliculus involvement in microsaccade and saccade generation.

Authors:  Ziad M Hafed; Richard J Krauzlis
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Review 3.  1-D Vision: Encoding of Eye Movements by Simple Receptive Fields.

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Journal:  Perception       Date:  2015       Impact factor: 1.490

4.  The effects of fixational tremor on the retinal image.

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Journal:  J Vis       Date:  2019-09-03       Impact factor: 2.240

Review 5.  Temporal Coding of Visual Space.

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Journal:  Trends Cogn Sci       Date:  2018-10       Impact factor: 20.229

6.  Haptic object localization in the vibrissal system: behavior and performance.

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Journal:  J Neurosci       Date:  2006-08-16       Impact factor: 6.167

Review 7.  Using goal-driven deep learning models to understand sensory cortex.

Authors:  Daniel L K Yamins; James J DiCarlo
Journal:  Nat Neurosci       Date:  2016-03       Impact factor: 24.884

8.  Neural dynamics of saccadic suppression.

Authors:  Frank Bremmer; Michael Kubischik; Klaus-Peter Hoffmann; Bart Krekelberg
Journal:  J Neurosci       Date:  2009-10-07       Impact factor: 6.167

9.  Perception as a closed-loop convergence process.

Authors:  Ehud Ahissar; Eldad Assa
Journal:  Elife       Date:  2016-05-09       Impact factor: 8.140

10.  High-acuity vision from retinal image motion.

Authors:  Alexander G Anderson; Kavitha Ratnam; Austin Roorda; Bruno A Olshausen
Journal:  J Vis       Date:  2020-07-01       Impact factor: 2.240

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