Anisotropy of tolerance of perceptual completion at the blind spot

Araragi, Yukyu; Nakamizo, Sachio

doi:10.1016/j.visres.2007.12.001

Cited by 13 publications

(20 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The completion is visible for small orders of misalignment, but it fades out quickly with increasing misalignment. These results are consistent with the finding of psychophysical experiments [ 30 , 31 ]and therefore, suggest that the filling-in process could naturally arise out of the computational principle of hierarchical predictive coding (HPC) of natural images.…”

Section: Introductionsupporting

confidence: 90%

Predictive Coding: A Possible Explanation of Filling-In at the Blind Spot

Raman

Sarkar

2016

PLoS ONE

View full text Add to dashboard Cite

Filling-in at the blind spot is a perceptual phenomenon in which the visual system fills the informational void, which arises due to the absence of retinal input corresponding to the optic disc, with surrounding visual attributes. It is known that during filling-in, nonlinear neural responses are observed in the early visual area that correlates with the perception, but the knowledge of underlying neural mechanism for filling-in at the blind spot is far from complete. In this work, we attempted to present a fresh perspective on the computational mechanism of filling-in process in the framework of hierarchical predictive coding, which provides a functional explanation for a range of neural responses in the cortex. We simulated a three-level hierarchical network and observe its response while stimulating the network with different bar stimulus across the blind spot. We find that the predictive-estimator neurons that represent blind spot in primary visual cortex exhibit elevated non-linear response when the bar stimulated both sides of the blind spot. Using generative model, we also show that these responses represent the filling-in completion. All these results are consistent with the finding of psychophysical and physiological studies. In this study, we also demonstrate that the tolerance in filling-in qualitatively matches with the experimental findings related to non-aligned bars. We discuss this phenomenon in the predictive coding paradigm and show that all our results could be explained by taking into account the efficient coding of natural images along with feedback and feed-forward connections that allow priors and predictions to co-evolve to arrive at the best prediction. These results suggest that the filling-in process could be a manifestation of the general computational principle of hierarchical predictive coding of natural images.

show abstract

Section: Introductionsupporting

confidence: 90%

Predictive Coding: A Possible Explanation of Filling-In at the Blind Spot

Raman

Sarkar

2016

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Observers viewed the stimuli with only the right eye; the left eye was occluded. Before the experimental trials, the position of the blind spot of each observer's right eye was determined based on a mapping method (Araragi and Nakamizo, 2008). In the mapping method, a flashing dot that moved horizontally or vertically was used, and observers judged the disappearing and reappearing points while fixating on the mark.…”

Section: Methodsmentioning

confidence: 99%

“…Although the brain does not receive any visual signals from the retinal region corresponding to the blind spot, one perceives a continuous line across the blind spot. This phenomenon has been called perceptual filling-in or perceptual completion of a line at the blind spot (Araragi and Nakamizo, 2008).…”

Section: Introductionmentioning

confidence: 99%

Perceptual filling-in of a line segment presented on only one side of the blind spot

Araragi¹,

Ito²,

Shoji³

2009

Spatial Vis

Self Cite

View full text Add to dashboard Cite

Two experiments examined whether filling-in occurred at the blind spot when a line segment was presented on only one side of the blind spot. We used static and dynamic stimuli: a static test line segment and a pair of probe line segments were presented in Experiment 1 and a moving test line segment was presented in Experiment 2. We compared the probability that the proximal end was perceived to be on the blind spot side when the test line segment came into contact with the blind spot (blind spot condition) with that when the test line segment was outside the blind spot (control condition). The results of the two experiments showed that the proximal end was perceived to be more on the blind spot side in the blind spot condition than in the control condition. Notably, when a dynamic stimulus was presented below the blind spot, the mean amount of filling-in reached 2.84 degrees. Therefore, filling-in occurred at the blind spot even when a line segment was presented on only one side of the blind spot.

show abstract

“…In studies on filling-in completions at the blind spot 4,5 , it was speculated that there might be different anisotropic process responsible for different kinds of anisotropy observed in different (misalignment, disorientation, and luminance difference) filling-in investigations e.g., it was speculated that the anisotropy in misalignment experiment might have arose from the anisotropy in vernier acuity. Here in this study, we have proposed a possible alternative explanation in terms of a unified principle based on the role natural image statistics.…”

Section: Discussionmentioning

confidence: 99%

“…The dynamics and the learning rule, thus, result from minimizing the cost function (using gradient decent method), with respect to r and U respectively-) An optimum estimate at a visual processing level is determined by the error signal from lower area (first term in the equation (4)) as well as error signal corresponding to a higher level (second term in equation (4)) that carry the contextual information since the higher area codes larger visual patch. This multilevel optimum-estimate for prediction is considered as an internal representation of the sensory input.…”

Section: Standard Hierarchic Predictive Coding (Hpc)mentioning

confidence: 99%

Significance of Natural Scene Statistics in Understanding the Anisotropies of Perceptual Filling-in at the Blind Spot

Raman

Sarkar

2017

Sci Rep

View full text Add to dashboard Cite

Psychophysical experiments reveal our horizontal preference in perceptual filling-in at the blind spot. On the other hand, tolerance in filling-in exhibit vertical preference. What causes this anisotropy in our perception? Building upon the general notion that the functional properties of the early visual system are shaped by the innate specification as well as the statistics of the environment, we reasoned that the anisotropy in filling-in could be understood in terms of anisotropy in orientation distribution inherent in natural scene statistics. We examined this proposition by investigating filling-in of bar stimuli in a Hierarchical Predictive Coding model network. The model network, trained with natural images, exhibited anisotropic filling-in performance at the blind spot, which is similar to the findings of psychophysical experiments. We suggest that the over-representation of horizontal contours in the natural scene contributes to the observed horizontal superiority in filling-in and the broader distribution of vertical contours contributes to the observed vertical superiority of tolerance in filling-in. These results indicate that natural scene statistics plays a significant role in determining the filling-in performance at the blind spot and shaping the associated anisotropies.

show abstract

Anisotropy of tolerance of perceptual completion at the blind spot

Cited by 13 publications

References 18 publications

Predictive Coding: A Possible Explanation of Filling-In at the Blind Spot

Predictive Coding: A Possible Explanation of Filling-In at the Blind Spot

Perceptual filling-in of a line segment presented on only one side of the blind spot

Significance of Natural Scene Statistics in Understanding the Anisotropies of Perceptual Filling-in at the Blind Spot

Contact Info

Product

Resources

About