Abstract
The tilt illusion is a paradigmatic example of contextual influences on perception. We analyze it in terms of a neural population model for the perceptual organization of visual orientation. In turn, this is based on a well-found treatment of natural scene statistics, known as the Gaussian Scale Mixture model. This model is closely related to divisive gain control in neural processing and has been extensively applied in the image processing and statistical learning communities; however, its implications for contextual effects in biological vision have not been studied. In our model, oriented neural units associated with surround tilt stimuli participate in divisively normalizing the activities of the units representing a center stimulus, thereby changing their tuning curves. We show that through standard population decoding, these changes lead to the forms of repulsion and attraction observed in the tilt illusion. The issues in our model readily generalize to other visual attributes and contextual phenomena, and should lead to more rigorous treatments of contextual effects based on natural scene statistics.
| Original language | English (US) |
|---|---|
| Article number | 19 |
| Journal | Journal of Vision |
| Volume | 9 |
| Issue number | 4 |
| DOIs | |
| State | Published - Apr 24 2009 |
| Externally published | Yes |
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Keywords
- Computational modeling
- Perceptual organization
- Structure of natural images
ASJC Scopus subject areas
- Ophthalmology
- Sensory Systems
Cite this
Perceptual organization in the tilt illusion. / Schwartz, Odelia; Sejnowski, Terrence J.; Dayan, Peter.
In: Journal of Vision, Vol. 9, No. 4, 19, 24.04.2009.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Perceptual organization in the tilt illusion
AU - Schwartz, Odelia
AU - Sejnowski, Terrence J.
AU - Dayan, Peter
PY - 2009/4/24
Y1 - 2009/4/24
N2 - The tilt illusion is a paradigmatic example of contextual influences on perception. We analyze it in terms of a neural population model for the perceptual organization of visual orientation. In turn, this is based on a well-found treatment of natural scene statistics, known as the Gaussian Scale Mixture model. This model is closely related to divisive gain control in neural processing and has been extensively applied in the image processing and statistical learning communities; however, its implications for contextual effects in biological vision have not been studied. In our model, oriented neural units associated with surround tilt stimuli participate in divisively normalizing the activities of the units representing a center stimulus, thereby changing their tuning curves. We show that through standard population decoding, these changes lead to the forms of repulsion and attraction observed in the tilt illusion. The issues in our model readily generalize to other visual attributes and contextual phenomena, and should lead to more rigorous treatments of contextual effects based on natural scene statistics.
AB - The tilt illusion is a paradigmatic example of contextual influences on perception. We analyze it in terms of a neural population model for the perceptual organization of visual orientation. In turn, this is based on a well-found treatment of natural scene statistics, known as the Gaussian Scale Mixture model. This model is closely related to divisive gain control in neural processing and has been extensively applied in the image processing and statistical learning communities; however, its implications for contextual effects in biological vision have not been studied. In our model, oriented neural units associated with surround tilt stimuli participate in divisively normalizing the activities of the units representing a center stimulus, thereby changing their tuning curves. We show that through standard population decoding, these changes lead to the forms of repulsion and attraction observed in the tilt illusion. The issues in our model readily generalize to other visual attributes and contextual phenomena, and should lead to more rigorous treatments of contextual effects based on natural scene statistics.
KW - Computational modeling
KW - Perceptual organization
KW - Structure of natural images
UR - http://www.scopus.com/inward/record.url?scp=65349190024&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=65349190024&partnerID=8YFLogxK
U2 - 10.1167/9.4.19
DO - 10.1167/9.4.19
M3 - Article
C2 - 19757928
AN - SCOPUS:65349190024
VL - 9
JO - Journal of Vision
JF - Journal of Vision
SN - 1534-7362
IS - 4
M1 - 19
ER -