<p>Yes there is evidence of the encoding of surface slant in the visual field</p>
<ul>
<li><p><a href="http://www.jneurosci.org/content/jneuro/23/18/7117.full.pdf" rel="nofollow noreferrer">Disparity-Based Coding of Three-Dimensional Surface Orientation by Macaque Middle Temporal Neurons</a></p>
<blockquote>
<p>We now show that MT
contains robust, disparity-based signals regarding the 3-D orientation (tilt and slant) of planar surfaces. This tilt selectivity does
not result from vergence eye movements or subtle monocular
dot-density cues, and approximately one-half of MT neurons
respond more strongly to a tilted stimulus (i.e., a nonzero slant)
than to any frontoparallel stimulus of the same size (data not
shown). In addition, we show that the tilt preference of MT neurons is primarily independent of the mean depth and slant of the
surface, properties that may simplify the extraction of 3-D orientation signals from a population of MT neurons.</p>
</blockquote>
</li>
<li><p><a href="https://www.physiology.org/doi/full/10.1152/jn.00578.2011?url_ver=Z39.88-2003&amp;rfr_id=ori:rid:crossref.org&amp;rfr_dat=cr_pub%3Dpubmed" rel="nofollow noreferrer">Representation of 3-D surface orientation by velocity and disparity gradient cues in area MT</a></p>
<blockquote>
<p>This study demonstrates that single MT neurons signal 3-D surface orientation (tilt and slant) via selectivity for velocity and disparity gradients, and to a lesser degree, for texture gradients. Selectivity is similar in multiunit activity recorded simultaneously, suggesting that 3-D surface orientation tuning is clustered in MT. Tilt selectivity is generally enhanced when multiple gradient cues are presented together, indicating that MT may integrate cues to represent surface structure with greater fidelity. This occurs despite the fact that tilt preferences for disparity and velocity gradients are poorly correlated overall. In addition, we found that responses to the combined stimulus, which contains all three gradient cues, are well approximated by a linear weighted sum of responses to the individual cues, with disparity and velocity gradients weighted most heavily. This study provides the first systematic examination of neural integration of multiple gradient cues to surface orientation and suggests that area MT contains an early multicue representation of 3-D surface structure.</p>
</blockquote>
</li>
</ul>

CC BY-SA 4.0
2019-01-11T17:55:42.420Z
2019-01-11T17:55:42.420Z
2020-06-17T09:55:31.633Z
norlesh
<p>In the way orientation columns display selectivity for various edge orientations projected on the retina which is then repeated at various angles in a localised region of the cortex. I am wondering whether in an analogous fashion if any similar neuronal circuitry has been shown to represent variations in the surface slant at the region being projected onto the retina. </p>

<p><em>By slant I mean a measure of the surfaces orientation away from co-planar with the visual focal plane.</em></p>

<p>Perhaps this information is extracted from binocular receptive fields?</p>

<p>If it hasn't been found then I guess I'm also wondering if anyone has ever looked? and or how the depth information from binocular receptive fields is encoded/represented for further processing? </p>

CC BY-SA 4.0
2019-01-05T20:05:54.353Z
Welcome. What do you mean by '... *surface normal at the image source* ...'?
AliceD
CC BY-SA 4.0
2019-01-05T20:16:49.420Z
Thanks, sorry - graphics terminology. what I was getting at is the angle of the surface at that point with respect to the image plane... if that's any clearer
norlesh
CC BY-SA 4.0
2019-01-05T20:27:20.163Z
I edited the question somewhat. Last thing is: ...the angle of the surface *at that point*... Which point? The very best thing to do is to provide an image to illustrate what you mean. It can say more than a 1,000 words.
AliceD
CC BY-SA 4.0
2019-01-05T20:34:28.000Z
ah so the 'surface normal' is projected out from a point on the object ... so I think it translates to : the point on the image source where the stimuli of the retinas receptive field originates from. Oh and thanks
norlesh
<neuroscience><theoretical-neuroscience><vision>
Is there any evidence for or against the existence of neurons that encode surface slant in vision?
norlesh
# Is there any evidence for or against the existence of neurons that encode surface slant in vision?

(Asked by: username_0 on 2019-01-05)

In the way orientation columns display selectivity for various edge orientations projected on the retina which is then repeated at various angles in a localised region of the cortex. I am wondering whether in an analogous fashion if any similar neuronal circuitry has been shown to represent variations in the surface slant at the region being projected onto the retina.

*By slant I mean a measure of the surfaces orientation away from co-planar with the visual focal plane.*

Perhaps this information is extracted from binocular receptive fields?

If it hasn't been found then I guess I'm also wondering if anyone has ever looked? and or how the depth information from binocular receptive fields is encoded/represented for further processing?

**Tags:** neuroscience, theoretical-neuroscience, vision

> **Comments:**
> **username_1**: Welcome. What do you mean by '... *surface normal at the image source* ...'?  
> **username_0**: Thanks, sorry - graphics terminology. what I was getting at is the angle of the surface at that point with respect to the image plane... if that's any clearer  
> **username_1**: I edited the question somewhat. Last thing is: ...the angle of the surface *at that point*... Which point? The very best thing to do is to provide an image to illustrate what you mean. It can say more than a 1,000 words.  
> **username_0**: ah so the 'surface normal' is projected out from a point on the object ... so I think it translates to : the point on the image source where the stimuli of the retinas receptive field originates from. Oh and thanks  

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## 1 answers

### Answer by username_0 on 2019-01-11. Score: 2 | Accepted answer

Yes there is evidence of the encoding of surface slant in the visual field

* [Disparity-Based Coding of Three-Dimensional Surface Orientation by Macaque Middle Temporal Neurons](http://www.jneurosci.org/content/jneuro/23/18/7117.full.pdf)

  > We now show that MT
  > contains robust, disparity-based signals regarding the 3-D orientation (tilt and slant) of planar surfaces. This tilt selectivity does
  > not result from vergence eye movements or subtle monocular
  > dot-density cues, and approximately one-half of MT neurons
  > respond more strongly to a tilted stimulus (i.e., a nonzero slant)
  > than to any frontoparallel stimulus of the same size (data not
  > shown). In addition, we show that the tilt preference of MT neurons is primarily independent of the mean depth and slant of the
  > surface, properties that may simplify the extraction of 3-D orientation signals from a population of MT neurons.
* [Representation of 3-D surface orientation by velocity and disparity gradient cues in area MT](https://www.physiology.org/doi/full/10.1152/jn.00578.2011?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3Dpubmed)

  > This study demonstrates that single MT neurons signal 3-D surface orientation (tilt and slant) via selectivity for velocity and disparity gradients, and to a lesser degree, for texture gradients. Selectivity is similar in multiunit activity recorded simultaneously, suggesting that 3-D surface orientation tuning is clustered in MT. Tilt selectivity is generally enhanced when multiple gradient cues are presented together, indicating that MT may integrate cues to represent surface structure with greater fidelity. This occurs despite the fact that tilt preferences for disparity and velocity gradients are poorly correlated overall. In addition, we found that responses to the combined stimulus, which contains all three gradient cues, are well approximated by a linear weighted sum of responses to the individual cues, with disparity and velocity gradients weighted most heavily. This study provides the first systematic examination of neural integration of multiple gradient cues to surface orientation and suggests that area MT contains an early multicue representation of 3-D surface structure.


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